INCENP and CDCA8 predict neoadjuvant chemotherapy response and outcomes in esophageal squamous cell carcinoma

Domain: neuroscience
Published: 2026-01-21
Editorial outcome: major_revision

Abstract
Neoadjuvant chemotherapy (NACT), a key strategy for various cancers, markedly improves patient prognosis and 5-year survival rates. However, numerous patients develop resistance to NACT and thus fail to benefit from it. Therefore, identifying reliable biomarkers to predict patient responsiveness to NACT remains a critical challenge. Here, we demonstrate that elevated expression of INCENP and CDCA8 contributes to poor NACT responsiveness across multiple cancers. Mechanistically, the 5′ UTR (GGACT at position 113) of INCENP and the 3′ UTR (GGACT at position 1041) of CDCA8 undergo m⁶A methylation and are recognized by YTHDF3, which facilitates their translation through interaction with eIF3A, ultimately driving poor response to NACT. Moreover, inhibition of INCENP and CDCA8 enhances NACT sensitivity by promoting multipolar spindle formation. Collectively, our findings establish that INCENP and CDCA8 serve as crucial biomarkers for predicting NACT responsiveness and as potential therapeutic targets for combination therapy with NACT to improve patient survival.

Introduction
Neoadjuvant chemotherapy (NACT) has become a standard treatment for patients with various cancers undergoing surgery, particularly in lung cancer, breast cancer, ovarian cancer, and esophageal cancer1–4. It aims to reduce tumor size, improve surgical resectability, and lower the risk of postoperative recurrence. Among the commonly used NACT regimens, paclitaxel plus cisplatin is a primary first-line treatment for several malignancies5–7. Although this regimen is considered highly cytotoxic to tumors, its response rate in esophageal cancer remains limited at approximately 50–60%8,9. Notably, a subset of patients exhibits resistance to NACT, leading to tumor progression and significantly compromising both survival outcomes and quality of life10,11. Therefore, identifying reliable predictive biomarkers for NACT sensitivity is essential for guiding personalized treatment.

The mechanisms underlying resistance to NACT are complex, including tumor microenvironment influences and molecular heterogeneity of cancers12,13. Among these, dysregulation of the cell cycle has emerged as a key contributor. Mitosis is a fundamental biological process that regulates cell division and serves as a crucial therapeutic target in overcoming paclitaxel resistance14,15. Overexpression of mitotic regulators, such as cyclins and checkpoint proteins, can effectively halt or repair damage in mitosis, thereby diminishing the efficacy of paclitaxel16,17. Conversely, targeting these regulators, which leads to chromosomal instability or maintains multipolar spindles throughout mitosis, can improve the clinical utility of paclitaxel18,19. Additionally, cisplatin resistance has been linked to dysregulation of cell cycle-associated proteins20,21. In this study, we focus on the chromosomal passenger complex (CPC), a key mitotic regulator composed of Aurora B kinase, inner centromere protein (INCENP), survivin, and cell division cycle-associated 8 (CDCA8). The CPC plays a central role in mitosis by orchestrating chromatin-induced microtubule stabilization, chromosome bi-orientation, and spindle assembly checkpoint regulation22–24. However, the role of CPC in regulating NACT resistance remains unexplored.

Emerging evidence suggests that epigenetic modifications, particularly abnormal N6-methyladenosine (m6A) methylation, contribute to malignancy progression25. m6A modifications regulate gene expression through RNA methyltransferases (writers), demethylases (erasers), and m6A-binding proteins (readers), all of which are frequently dysregulated in various tumor types26,27. Recent studies indicate that m6A modification plays a key role in drug resistance via multiple molecular pathways28,29; however, the role of m6A in mitotic regulation and its impact on NACT response remain poorly understood. Therefore, further investigation is required to elucidate the role of m6A methylation in NACT resistance.

In this study, we identify INCENP and CDCA8 as key predictors of poor response to NACT across multiple cancer types. We demonstrate that m6A methylation of INCENP and CDCA8 mRNAs is catalyzed by METTL3 and recognized by YTHDF3, which promotes their translation by interacting with eIF3A, thereby contributing to NACT resistance. Importantly, the efficacy of these chemotherapeutic agents is dependent on the m6A modification status of INCENP and CDCA8. Inhibition of INCENP and CDCA8 leads to the formation of a multipolar spindle, thereby enhancing sensitivity to NACT both in vitro and in vivo. These results provide a strong rationale for exploring combination therapies that target INCENP and CDCA8 to improve NACT responsiveness across multiple cancers.

Results
Upregulation of INCENP and CDCA8 contributes to poor NACT response
To identify potential drivers of NACT response, we performed transcriptomic profiling of clinical esophageal squamous cell carcinoma (ESCC) tissue samples from NACT recipients (Cohort 1, Supplementary Data 1) following the study design (Fig. 1a). Patients with evaluable responses were classified as responders (TRG0, 1) and non-responders (TRG2, 3) based on the National Comprehensive Cancer Network (NCCN) TRG criteria30. To explore the mechanisms regulating NACT response, we began with gene ontology (GO) analysis, which revealed that differentially expressed genes (DEGs) converge on key regulators of cell division (Fig. 1b). Consistent with these findings, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed significant alterations in cell cycle pathways (Supplementary Fig. 1a), highlighting the crucial role of cell mitotic regulation in NACT response. Additionally, we observed that the N6-methyladenosine-containing RNA binding function ranked as the top-enriched molecular function (Fig. 1b), and m6A modification plays a critical role in RNA metabolism by regulating gene expression through modulation of mRNA stability, translation efficiency, and splicing31. More importantly, emerging evidence suggests that dynamic m6A modifications play a significant role in chemotherapy resistance32,33. Therefore, we hypothesize that m6A modification modulates the progression of the mitotic pathway by regulating the RNA metabolism of mitosis-related genes, thereby driving a poor response to NACT. To address this, we analyzed the differential expression of 20 m6A regulators in Cohort 1 (Fig. 1c) and found a markedly elevated YTHDF3 expression in non-responder samples (Supplementary Fig. 1b). These findings suggest that YTHDF3 upregulation plays a significant role in the poor response to NACT in ESCC.

We further investigated whether YTHDF3, as an upstream m6A regulator, modulates the mitotic pathway and identified candidate mitosis-related genes that may contribute to poor NACT responsiveness. Therefore, we employed a functional proteomics analysis using YTHDF3 knockdown (KD) ESCC cells. Initially, we analyzed the variation in KEGG pathways, and surprisingly, the results indicated enrichment of numerous proteins involved in the cell cycle (Supplementary Fig. 1c). More importantly, GO enrichment analysis highlighted the chromosomal passenger complex in the cellular component section as the top altered pathway in shYTHDF3 ESCC cells (Fig. 1d). Moreover, Gene Set Enrichment Analysis (GSEA) revealed a negative association between genes involved in mitotic nuclear division (Fig. 1e, Supplementary Fig. 1d) in YTHDF3 KD cells. Additionally, the core components of the CPC, including INCENP, CDCA8, and AURKB, were significantly downregulated in shYTHDF3 cells. Notably, expression levels of INCENP and CDCA8 showed more pronounced changes in both shYTHDF3 and YTHDF3 over-expressing (OE) ESCC cell lines (Supplementary Fig. 1e–h); therefore, we focused on INCENP and CDCA8 as potential targets for further investigation. We subsequently verified that the expression of INCENP, CDCA8 and YTHDF3 proteins was upregulated in non-responders of cohort 1 clinical samples by immunohistochemical staining (IHC) (Fig. 1f, g), and that the expression of INCENP and CDCA8 was positively correlated with YTHDF3 levels (Fig. 1h). Collectively, by integrating transcriptomic profiling of Cohort 1, proteomics analysis of shYTHDF3 cells, and pathological examination of protein expression, we demonstrate that INCENP and CDCA8, as cell mitotic regulators modulated by YTHDF3, may contribute to poor NACT response in ESCC patients.

To further confirm that elevated expression of INCENP and CDCA8 is associated with poor response to NACT in ESCC patients, we analyzed another clinical cohort (Cohort 2, Supplementary Data 2). Radiological images indicated that tumor size was significantly reduced in responders, and IHC results revealed markedly higher INCENP and CDCA8 expression in tissue samples from non-responders (Fig. 1i, j) and in cancer tissues from ESCC tissue arrays (Supplementary Fig. 1i, j, Supplementary Data 3). These findings suggest that the upregulation of INCENP and CDCA8 contributes to poor response to NACT and is associated with ESCC progression. Furthermore, elevated expression levels of INCENP and CDCA8 were associated with poor clinical outcomes, including disease progression (Fig. 1k) and shorter survival times in ESCC patients receiving NACT (Supplementary Fig. 1k). Collectively, INCENP and CDCA8 may serve as potential drivers of NACT non-responsiveness in ESCC.

Knockdown of INCENP and CDCA8 potentiates the sensitivity of ESCC to NACT
Given the elevated expression of INCENP and CDCA8 in non-responders, we explored the impact of these proteins on the chemosensitivity of ESCC cells to paclitaxel and cisplatin. To investigate the correlation between basal expression levels of INCENP and CDCA8 and the sensitivity of ESCC cell lines to paclitaxel and cisplatin, we treated a panel of ESCC cell lines with these drugs. These findings indicate that elevated basal expression levels of INCENP and CDCA8 are associated with increased IC50 values, suggesting a negative correlation between their expression and the sensitivity to paclitaxel and cisplatin across ESCC cell lines. (Fig. 2a, b and Supplementary Fig. 2a).

To further validate the sensitizing effect of INCENP and CDCA8 KD on paclitaxel and cisplatin responses in ESCC cells, an MTT assay was utilized, and the results showed that INCENP and CDCA8 KD decreased the IC50 values of paclitaxel and cisplatin compared with mock controls (Fig. 2c, d and Supplementary Fig. 2b, c). Consistently, plate colony formation assays demonstrated that INCENP and CDCA8 KD increased the sensitivity of ESCC cells to paclitaxel and cisplatin compared with the mock control (Fig. 2e, f and Supplementary Fig. 2d, e). Furthermore, overexpression (OE) of INCENP and CDCA8 increased the IC50 values of paclitaxel and cisplatin in KYSE450 ESCC cells compared with the vector control (Fig. 2g and Supplementary Fig. 2f), with similar results observed in the plate colony formation assay (Fig. 2h). To further validate these findings, in vivo assays were conducted to examine the impact of targeting INCENP and CDCA8 on the therapeutic efficacy of paclitaxel and cisplatin. Consistent with our in vitro findings, treatment of shINCENP and shCDCA8 ESCC cells with paclitaxel or cisplatin resulted in a reduced ESCC tumor burden compared with paclitaxel- or cisplatin-treated control cells. (Fig. 2i–k). These findings support the idea that targeting INCENP and CDCA8 enhances response to NACT in ESCC.

YTHDF3 drives mitosis and NACT resistance via INCENP and CDCA8
To further investigate whether YTHDF3 acts as an upstream regulator that modulates the cell mitotic pathway and suppresses NACT responsiveness through the regulation of INCENP and CDCA8 expression, we examined cell cycle and mitotic alterations in shYTHDF3 ESCC cells. The results showed that YTHDF3 KD led to G2/M phase arrest (Fig. 3a) and decreased protein expression of G2 phase regulators (Supplementary Fig. 3a, b). Consistently, immunofluorescence (IF) results demonstrated that YTHDF3 KD induced a defective cell mitosis phenotype characterized by multipolar spindle formation in ESCC cells (Fig. 3b and Supplementary Fig. 3c, d).

To further investigate the function of YTHDF3, we used Ebselen as a selective inhibitor. Its target specificity was systematically validated by assessing the viability of YTHDF3 KD cells following Ebselen treatment (Supplementary Fig. 3e, f). Consistently, Ebselen treatment also induced G2/M phase arrest (Supplementary Fig. 3g–i) and promoted the formation of multipolar spindles in ESCC cells (Supplementary Fig. 3j, k). These findings further confirm that YTHDF3 plays a crucial role in regulating mitosis in ESCC cells.

Given the elevated expression of YTHDF3 in non-responders, we investigated its role in modulating chemosensitivity in ESCC cells. Treatment of a panel of ESCC cell lines with paclitaxel and cisplatin revealed that higher basal expression of YTHDF3 was associated with increased IC50 values, indicating reduced drug sensitivity (Supplementary Fig. 3l, m). Furthermore, YTHDF3 KD and Ebselen treatment enhanced the sensitivity of ESCC cells to paclitaxel and cisplatin compared with the control (Fig. 3c–f and Supplementary Fig. 3n, o). In contrast, YTHDF3 OE decreased the sensitivity of ESCC cells to paclitaxel and cisplatin compared with the vector control (Fig. 3g, h and Supplementary Fig. 3p). In vivo studies revealed that Ebselen sensitized ESCC cells to paclitaxel (Fig. 3i–k) and cisplatin treatment (Fig. 3l–n), as evidenced by reduced tumor size and weight. Importantly, no significant differences in total body weight were associated with any treatment regimens (Supplementary Fig. 3q, r). These findings support the idea that inhibiting YTHDF3 potentiates NACT responsiveness both in vitro and in vivo.

To determine whether the effect of YTHDF3 on NACT response is functionally dependent on INCENP and CDCA8, we examined the impact of their overexpression in YTHDF3 KD cells. We found that INCENP and CDCA8 OE reversed the enhanced sensitivity to paclitaxel and cisplatin caused by YTHDF3 KD, resulting in decreased drug sensitivity (Fig. 3o, p and Supplementary Fig. 3s, t). These findings support the notion that both INCENP and CDCA8 are essential for proper mitotic progression and functionally act as downstream effectors of YTHDF3. Collectively, our results suggest that the effect of YTHDF3 on NACT responsiveness depends on INCENP and CDCA8.

INCENP and CDCA8 mRNAs are m6A-methylated and recognized by YTHDF3 to modulate NACT response
To elucidate whether YTHDF3 regulates INCENP and CDCA8 expression in an m6A-dependent manner, we assessed the requirement of its m6A-binding ability in this regulatory process. YTHDF3 contains two hydrophobic residues W438 and W492, which are integral to the specific recognition of m6A31,34. To examine this, we constructed a YTHDF3 mutant (YTHDF3-mut) plasmid (Supplementary Fig. 4a) and transfected both WT and YTHDF3-mut plasmids into YTHDF3 KD cells. As expected, the decreased expression of CDCA8 and INCENP in YTHDF3 KD cells was rescued by ectopic expression of WT YTHDF3, but not by the YTHDF3-mut (Fig. 4a, b and Supplementary Fig. 4b, c). Furthermore, G2/M phase arrest (Fig. 4c) and reduced expression of G2 phase regulator (Fig. 4d and Supplementary Fig. 4d) observed in YTHDF3 KD cells were reversed upon WT YTHDF3 transfection. Moreover, the formation of multipolar spindles in mitotic YTHDF3 KD ESCC cells was rescued by WT YTHDF3 overexpression, but not by the mutant YTHDF3 (Fig. 4e, f). Collectively, these findings suggest that YTHDF3 regulates the expression of INCENP and CDCA8 in an m6A-dependent manner and plays a pivotal role in cell mitosis.

To further investigate how CDCA8 and INCENP transcripts undergo m6A modification, we analyzed the GEPIA database and observed a positive correlation between the expression of the RNA methyltransferase METTL3 and the levels of both INCENP and CDCA8 (Supplementary Fig. 4e), and METTL3 expression was examined to identify its role in the m6A modification of these transcripts. Subsequently, we generated stable cell lines with low levels of m6A decoration by METTL3 KD (Supplementary Fig. 4f). We observed reduced protein levels of CDCA8 and INCENP in both METTL3 KD cells (Fig. 4g and Supplementary Fig. 4g, h) and ESCC patient-derived xenograft organoids (PDXO) (Fig. 4h–k and Supplementary Fig. 4i). Moreover, multipolar spindle formation was observed in mitotic METTL3 KD cells (Fig. 4l, m and Supplementary Fig. 4j). These data collectively demonstrate that YTHDF3 regulates METTL3-mediated N⁶-methylation of CDCA8 and INCENP transcripts in an m⁶A-dependent manner. More importantly, METTL3 expression was significantly higher in non-responder tissues compared to responder tissues (Fig. 4n, o). Further analysis revealed that METTL3 expression was positively correlated with INCENP and CDCA8 expression (Fig. 4p), thereby identifying N6-methylation of INCENP and CDCA8 transcripts as a key factor associated with poor NACT responsiveness in ESCC cells.

YTHDF3 promotes the translation of INCENP and CDCA8 mRNAs
Given that the expression of INCENP and CDCA8 proteins was decreased in shYTHDF3 ESCC cells (Supplementary Fig. 1e, f) and PDXO (Fig. 5a, b and Supplementary Fig. 5a, b), we sought to elucidate the molecular mechanisms by which YTHDF3 regulates INCENP and CDCA8. RNA immunoprecipitation sequencing (RIP-seq) analysis revealed direct binding of YTHDF3 protein to both INCENP and CDCA8 transcripts (Fig. 5c, d), which was subsequently confirmed by RIP-qPCR assays (Fig. 5e). Given that YTHDF3 facilitates translation and decay of N6-methyladenosine-modified RNA31, we next examined whether YTHDF3 knockdown affected target mRNA abundance. qRT-PCR results showed that the mRNA levels of INCENP and CDCA8 were unaffected by YTHDF3 KD (Supplementary Fig. 5c), suggesting that the observed protein downregulation likely occurs at the translational level. Indeed, the nascently synthesized proteins on ESCC cell lines overexpressing Flag-YTHDF3 were increased compared to the vector control group as determined by a protein synthesis assay (Fig. 5f, g) and the Surface Sensing of Translation (SUnSET) assay (Supplementary Fig. 5d). To further investigate, we constructed pmirGLO-CDCA8 and pmirGLO-INCENP luciferase reporters. The dual-luciferase assay showed that the translation efficiency of CDCA8 and INCENP was significantly lower in YTHDF3 KD ESCC cells compared with mock cells (Fig. 5h, i). Collectively, these findings indicate that YTHDF3 regulates the translation of CDCA8 and INCENP mRNAs.

To elucidate the underlying mechanism by which YTHDF3 regulates the translation of INCENP and CDCA8, we performed co-immunoprecipitation (co-IP) to identify proteins associated with translation regulation, including eEF1A2, RPS3A, eIF4A1, and eIF3A (Supplementary Fig. 5e). Our results demonstrated that YTHDF3 interacts with eIF3A, a component of the eukaryotic translation initiation factor (eIF) complex, as confirmed by both co-IP and IF assays (Fig. 5j, k and Supplementary Fig. 5f, g). To further confirm the direct interaction between YTHDF3 and eIF3A, we performed a proximity ligation assay (PLA), which illustrated the distribution of in situ PLA spots of YTHDF3-eIF3A in the cell cytoplasm (Fig. 5l, m). To investigate whether YTHDF3 regulates the translation of INCENP and CDCA8 through eIF3A, we performed eIF3A RIP-qPCR and found that both YTHDF3 KD and Ebselen treatments significantly reduced the binding of eIF3A to CDCA8 and INCENP mRNAs (Fig. 5n, o and Supplementary Fig. 5h). Additionally, ESCC cells treated with Ebselen showed reduced binding of YTHDF3 to CDCA8 and INCENP mRNAs (Supplementary Fig. 5i), leading to decreased protein expression levels of INCENP and CDCA8 (Supplementary Fig. 5j, k). Collectively, these findings highlight that YTHDF3 enhances the translation of CDCA8 and INCENP by recruiting eIF3A to its target mRNAs.

To further investigate whether YTHDF3 regulates CDCA8 and INCENP translation in an m6A-dependent manner. RIP-qPCR was performed, and the results showed that YTHDF3 and eIF3A bind more strongly to CDCA8 and INCENP mRNAs in ESCC cells overexpressing WT YTHDF3, but not in cells expressing mutant YTHDF3 (Supplementary Fig. 5l, m). Additionally, dual luciferase assays demonstrated a significant increase in the translation efficiency of CDCA8 and INCENP mRNAs in YTHDF3 KD ESCC cells ectopically expressing WT YTHDF3, compared with those expressing mutant YTHDF3 (Supplementary Fig. 5n). Moreover, RIP-qPCR assays showed that the binding of YTHDF3 and eIF3A to m6A-modified CDCA8 and INCENP mRNAs was significantly reduced in METTL3 KD cells (Fig. 5p, q). These findings collectively suggest that YTHDF3 regulates the translation of CDCA8 and INCENP in an m6A-dependent manner.

INCENP and CDCA8 contribute to poor NACT response in an m6A-dependent manner
Based on our findings that m6A modification of INCENP and CDCA8 mRNAs is indispensable for YTHDF3 recognition and regulation, it is essential to explore the m6A modification sites of INCENP and CDCA8 mRNAs for the regulation of NACT response in ESCC cells. Very high-confidence m6A modification sites were predicted using the SRAMP web tool (www.cuilab.cn/sramp) (Fig. 6a, b). Methylated RNA immunoprecipitation followed by qPCR (meRIP-qPCR) results showed that, among the predicted m⁶A regions of INCENP, the 5′ UTR m⁶A site (site 1: GGACT at position 113) exhibited the most pronounced decrease in methylation upon METTL3 KD in ESCC cells (Fig. 6c, Supplementary Fig. 6a, b). For CDCA8, the CDS region (site 2: TGACT at position 909) and the 3′ UTR region (site 4: GGACT at position 1041) showed the greatest reduction in m⁶A levels following METTL3 KD (Fig. 6d, Supplementary Fig. 6c, d). Dual-luciferase reporter assays further demonstrated that mutation of the 5′ UTR m⁶A site in INCENP, as well as mutations of the 3′ UTR and dual-site regions in CDCA8, significantly reduced translation efficiency in ESCC cells, whereas mutation of the CDS m⁶A site alone in CDCA8 had no detectable effect (Supplementary Fig. 6e, f). Collectively, these data indicate that the 5′ UTR (GGACT at position 113) of INCENP and the 3′ UTR (GGACT at position 1041) of CDCA8 undergo METTL3-mediated m⁶A modification, which contributes to their translation regulation in ESCC.

To elucidate whether CDCA8 and INCENP contribute to the poor response to NACT through these m6A modification sites, we overexpressed either WT or double-mutant CDCA8 (mut-CDCA8) and 5’UTR-mutant INCENP (mut-INCENP) in YTHDF3 KD ESCC cell lines. We found that the frequency of multipolar spindle formation observed in YTHDF3 KD ESCC cells was decreased upon expression of WT INCENP or CDCA8 (Fig. 6e, f). To determine whether m6A modification of INCENP and CDCA8 plays a pivotal role in NACT responsiveness, we assessed the sensitivity of YTHDF3 KD ESCC cells to paclitaxel and cisplatin following transfection with either WT or m⁶A site–mutant INCENP and CDCA8 constructs. Our results showed that paclitaxel and cisplatin sensitivity was reduced in YTHDF3 KD cells transfected with WT INCENP or CDCA8 plasmids, but not in those transfected with the m⁶A site–mutant constructs (Fig. 6g, h and Supplementary Fig. 6g, h). These findings indicate that the ability of INCENP and CDCA8 to modulate the NACT response is dependent on their m⁶A methylation. Collectively, INCENP and CDCA8 transcripts are N6-methylated by METTL3 and subsequently recognized by YTHDF3, which promotes their translation via interaction with eIF3A, ultimately leading to paclitaxel and cisplatin resistance in ESCC (Fig. 6i).

INCENP and CDCA8 serve as predictors of poor NACT response across multiple cancer types
Given the contribution of INCENP, CDCA8, and YTHDF3 to poor NACT response, we retrospectively analyzed their expression levels in relation to clinical outcomes using a cohort of 65 pre-treatment biopsy samples from ESCC patients (Cohort 3, Supplementary Data 4). Based on annotated clinical and molecular attributes of individual patient data, we observed that non-responders were more likely to present with advanced TNM stages and medullary histological types (Fig. 7a and Supplementary Fig. 7a), suggesting a correlation between these clinical parameters and NACT response. IHC staining revealed that responders exhibited significantly lower expression levels of INCENP, CDCA8, and YTHDF3 compared with non-responders in biopsy tissues from ESCC (Fig. 7b, c). Given the widespread use of the paclitaxel plus cisplatin regimen as an NACT strategy across multiple cancer types35–37. We extended our analysis to assess the protein expression levels of INCENP, CDCA8, and YTHDF3 in response to this regimen in breast and lung cancer patients from Cohort 3. IHC results revealed that patients who responded favorably to NACT exhibited significantly lower expression levels of INCENP, CDCA8, and YTHDF3 compared with non-responders (Fig. 7b, d, e). Furthermore, expression levels of INCENP and CDCA8 showed a positive correlation with YTHDF3 expression (Fig. 7f and Supplementary Fig. 7b, c). Collectively, these findings demonstrate that INCENP, CDCA8, and YTHDF3 are upregulated in multiple cancer types, supporting the pivotal role of the YTHDF3-m6A-CDCA8/INCENP axis in driving poor NACT responsiveness across multiple cancers.

To further investigate the prognostic relevance of INCENP, CDCA8, and YTHDF3 expression in the context of NACT, we performed survival analysis. Results revealed that responders exhibited significantly longer progression-free survival (PFS) and overall survival (OS) compared with non-responders (Supplementary Fig. 7d, e). Notably, among non-responders, high expression of INCENP, CDCA8, and YTHDF3 proteins was associated with significantly shorter PFS in ESCC patients (Fig. 7g–i), suggesting that elevated levels of these proteins are correlated with poor clinical outcomes following NACT. Receiver operating characteristic (ROC) curves were generated for each biomarker across multiple cancer types to evaluate their predictive performance, reflecting the sensitivity and specificity of these predictors. The results revealed area under the curve (AUC) values of 0.785, 0.736, and 0.707 for INCENP, CDCA8, and YTHDF3, respectively. Importantly, a combined analysis of these biomarkers demonstrated superior predictive accuracy, with an AUC of 0.817 in ESCC (Fig. 7j). These findings indicate that a composite biomarker model provides more accurate prediction of poor NACT response in ESCC patients. Additionally, in both lung and breast cancers, YTHDF3, INCENP, and CDCA8 also exhibited significant potential as predictors of NACT response (Fig. 7k, l). Collectively, these results highlight the critical role of YTHDF3, INCENP, and CDCA8 as robust and broadly applicable predictors of NACT responsiveness across multiple cancer types, with enhanced accuracy achieved through combined assessment.

Methods
Ethical compliance statement
The study complied with all relevant ethical regulations and was approved by the Medical Ethics Committee of Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital (Approval No. 2021-KY-0239-001). Written informed consent was obtained from all patients. Sex and/or gender were not considered in the study design, and the sex of participants was determined based on information recorded in their medical records; gender identity was not separately collected in this study. Clinical information about the patients can be found in Supplementary Data 1–4.

Animal studies
Female athymic nude mice (nu/nu, Mus musculus, genetically immunodeficient, 6–8 weeks old, from Beijing HFK Bioscience) were used for the subcutaneous xenograft models. The mice were maintained under specific pathogen-free conditions in 12 h light/dark cycles at a temperature of 20 ± 2 °C and humidity of 55 ± 10%. Mice had free access to water and rodent diet (Beijing keao xieli feed co., LTD, 1016706476803973120). This study was approved by the Ethical Committee of China-US (Henan) Hormel Cancer Institute (CUHCI2018063). All animal experiments complied with the ethics committee, and the maximum allowable tumor volume was set at 1000 mm3. Notably, the tumor volumes observed in this study did not exceed the specified limit.

Antibodies, key commercial kit, Plasmids, and software
Detailed information on the antibodies, key commercial kits, plasmids, and software used in this study is provided in Supplementary Table 1.

Cell lines and cell culture
Human ESCC cell lines (KYSE30, KYSE70, KYSE150, KYSE410, KYSE450, and KYSE510) and HEK293T were preserved and donated by Professor Ziming Dong (the Department of Pathophysiology, School of Basic Medical Sciences of Zhengzhou University). The immortalized normal esophageal epithelial cell line (SHEE) was generously provided by Dr. Enmin Li (Shantou University, Guangdong, China). Authentication of all cell lines was performed using STR analysis. ESCC cell lines were cultured in RPMI-1640 medium (VivaCell, Cat# C3010-0500), and HEK293T cells were cultured in DMEM high glucose medium (VivaCell, Cat# C3110-0500). Both RPMI-1640 medium and DMEM medium were supplemented with 10% FBS (VivaCell, Cat #2302008) and 1% penicillin/streptomycin. All the cell lines were maintained in a humidified incubator at 37 °C under 5% CO2.

PDX-derived organoid (PDXO)
PDXO were established from fresh PDX tumor fragments and cultured according to methods described in a previous report49. Briefly, the PDX tumor tissue was minced into small fragments and incubated in HBSS with dispase, collagenase Ⅳ, Y-27632, and Amphotericin B to create a single-cell suspension. Next, the tissue fragments were filtered, trypsinized, and further filtered through a 70 μm strainer. The cells were then centrifuged (200 × g) at 4 °C, and the cell pellet was washed and resuspended in basal medium prior to quantifying cell numbers using the Trypan Blue exclusion test. To initiate PDXO in a 24-well plate, 1 × 104 live cells were seeded per well in Matrigel and cultured in organoid medium. The medium was replaced every 2–3 days, and organoid growth was monitored under a microscope. After 4-6 days, we used Mock, shYTHDF3, and shMETTL3 viruses for infection. On day 14, the organoids were photographed. Organoid numbers and size were quantified using the ImageJ software.

Western blotting
Cells were harvested with a cell scraper on ice and lysed with RIPA lysis buffer for 30 min (50 mM Tris-HCl, 0.5 mM NaCl, 10 mM imidazole, 5% glycerol, 1% NP-40, and 0.25% Triton X-100; pH adjusted to 8.0). Next, the cell lysates were centrifuged (14,000 × g, 10 min) at 4 °C, and the supernatant was transferred to a fresh tube. A BCA kit (Cat# PC0020, Solarbio) was used to quantify the protein concentration of each harvested lysate. 10–30 µg of protein was denatured in loading buffer and resolved via SDS-PAGE. The gel was subsequently transferred to a 0.45-μm PVDF membrane. The membranes were then blocked with 5% non-fat milk and incubated on a rotator with specific antibodies at 4 °C overnight. The next day, the membranes were washed with TBST and incubated with the secondary antibody for 1 h. The proteins were detected by using chemiluminescence (ECL) reagent. The membranes were exposed using an Amersham Imager 800.

Co-immunoprecipitation
Cell lysates were pre-cleared with IgG antibody and protein A/G agarose for 3 h at 4 °C. After centrifugation (600 × g, 5 min) at 4 °C, the cell lysates were incubated on a rotator overnight (4 °C) with IgG and specific antibodies. Protein A/G agarose was added to the samples, and they were rotated for another 2 h at 4 °C. The beads were then washed three times with lysis buffer. The beads were then eluted with 2 × SDS loading buffer. The eluted proteins were heated at 95 °C for 10 min and resolved using Western blotting.

Cell cycle
In preparation for cell cycle analysis, cells were seeded in 60 mm dishes for 48 h with Ebselen treatment. The cells were fixed in 70% pre-cooled ethanol for 24 h and washed with 1 mL PBS three times. The cells were resuspended with 250 µL 0.5% Triton X-100 and incubated with 5 µL 10 µg/mL RNase A at room temperature for 1 h. The cells were then incubated with 5 µL 1 mg/mL propidium iodide at room temperature for 20 min. After staining, the cells were analyzed using a flow cytometer (BD Biosciences, San Jose, CA).

Cell proliferation and colony formation assays
For the cell proliferation assay, 1500 cells were seeded into 96-well plates, and cell viability was assessed for 72 h with or without paclitaxel and cisplatin treatment using an MTT assay. For the colony formation assay, 1000 cells were seeded into 6-well plates for approximately 10 days with or without paclitaxel and cisplatin treatment. Afterward, the medium was aspirated, and the plate was gently washed with distilled water. Finally, the cells were stained with crystal violet and counted at the endpoint. All studies were conducted at least in triplicate.

Plasmid construction
Candidate genes ORF expression plasmid were constructed by YouBio Biology Company (Changsha, China). Short hairpin RNAs (shRNA) were designed and subcloned into the pLKO.1 vector. All plasmids were used after being confirmed by sequencing. The primers are listed in Supplementary Table 2, and the plasmid information is listed on Supplementary Table 1.

Cell transfection and lentiviral infection
The lentiviral shRNA/ORF plasmid was co-transfected with packaging vectors (pMD2G and psPAX2) into HEK293T cells using Lipo2000 transfection reagent. After transfection, virus particles were harvested at 48 h and filtered using a 0.45 µm filter. To establish stable knockdown cell lines, cells were infected with the designated virus particles along with 8 µg/ml polybrene. After 24 h of infection, selection of stable cell lines was performed by incubation with 2–5 μg/mL puromycin for 48 h. Stable knockdown cell lines were generated through 2–3 passages. To overexpress YTHDF3, INCENP, and CDCA8, a pcDNA3.1-3×Flag-YTHDF3, pcDNA3.1-myc-INCENP, or pcDNA3.1-myc-CDCA8 plasmid was transfected into ESCC cells using the Lipo2000 reagent. After 4 h, the culture medium was replaced with fresh complete growth medium, and the cells were incubated for an additional 24 h. The knockdown and overexpression efficiency were evaluated by a Western blotting.

In vivo study
KYSE150 cells stably transfected with pLKO.1 were used as a mock control, and KYSE150 shINCENP or shCDCA8 cells were resuspended in cold PBS with 1‰ penicillin-streptomycin. A total of 5 × 106 cells were subcutaneously injected into the flank of nude female mice. Mice were randomly divided into Mock (n = 15), shINCENP (n = 15), and shCDCA8 (n = 15) groups. Each group was further divided into three subgroups, with five mice per subgroup: (1) vehicle, (2) 10 mg/kg paclitaxel per week (i.p.), (3) 10 mg/kg cisplatin per week (i.p.). For the Ebselen combination treatment experiment, mice were injected with KYSE150 cells and divided into six groups, with six mice per group: (1) vehicle, (2) 20 mg/kg Ebselen (i.p.), five times per week, (3) 10 mg/kg paclitaxel/week (i.p.), (4) 10 mg/kg cisplatin/week (i.p.), and (5) 10 mg/kg paclitaxel combined with 20 mg/kg Ebselen, and (6) 10 mg/kg cisplatin combined with 20 mg/kg Ebselen. Tumor volume was measured twice per week throughout the experiment. At the end of the study, mice were euthanized, and tumors were extracted for further analysis. Tumor volume (T) was calculated as T=(length×width2)/2. The maximum tumor size allowed was 1000 mm3 according to the Animal Ethics Committee. Notably, the maximal tumor burden didn’t exceed the limitation in this study. Nude mice aged 6-8 weeks were used to evaluate the tumorigenesis of the cells, with no specific gender requirements. Female mice were chosen for their more moderate character.

Immunofluorescence
KYSE30 and KYSE150 cells were seeded into a 24-well plate at 60% density with a slide. After 24 h, the cell culture medium was aspirated, and the cells were rinsed three times with cold PBS. The cells were then fixed in 400 μL of cold methanol for 25 min at room temperature, washed three times with PBS, and incubated overnight at 4 °C with YTHDF3 and eIF3A antibody (1:100, in solution 2% BSA, 0.3% Triton-100, 1% normal goat serum, 0.1% NaN3). The next day, the cells were washed with PBS and then incubated with the appropriate secondary antibody (Goat anti-rabbit IgG H&L (Alexa Fluor 488), Goat anti-mouse IgG(H+L) (Alexa Fluor 568)) and DAPI. Then, the slides were washed with PBS, air-dried, and mounted. Finally, images were captured by a Nikon confocal microscope. For cell mitosis visualization, cells were seeded into a 24-well plate at 60% density onto slides. 16 h post-seeding, the cells were fixed, washed, and incubated 2 h at RT with α-Tubulin antibody (1:300, in solution 2% BSA, 0.3% Triton-100, 1% Normal goat serum, 0.1% NaN3). Afterwards, the slides were washed with PBS, air dried, and mounted. Finally, images were captured by a Nikon confocal microscope. For organoid slides, the organoids were embedded in paraffin and cut into slices before IF detection. Slices were subjected to antigen retrieval and blocking with 5% BSA. The tissue slices were incubated with 30 µL primary antibodies, including YTHDF3, METTL3, INCENP, and CDCA8 antibodies (1:100 in 2% BSA solution) at 4 °C overnight. The slices were then washed with 1× PBS and incubated with the appropriate secondary antibody (Goat anti-rabbit IgG H&L (Alexa Fluor 488), Goat anti-mouse IgG(H+L) (Alexa Fluor 568), 1:500) and DAPI. Then, the slides were washed with PBS, air-dried, and mounted. Finally, images were captured by a Nikon confocal microscope. The relative protein expression of IF images was quantified using ImageJ software. Fluorescence intensity was measured and normalized by calculating the average intensity per cell or per unit area, effectively controlling for variations in cell numbers. Comparisons between experimental groups were made to assess relative changes in protein expression levels.

Protein synthesis assay
After KYSE30 and KYSE150 cells were transfected with 3× flag-YTHDF3 for 48 h, the nascently synthesized proteins were labeled with 10 µM Click-iT® OPP for another 30 min according to the manufacturer’s specifications of the Protein Synthesis Assay Kit. After fixation with 3.7% formaldehyde for 15 min, the cells were subsequently stained using a 100 µL OPP reaction cocktail and NuclearMask™ Blue. The stained cells were imaged using a Nikon confocal microscope and analyzed by ImageJ.

RNA Isolation and Quantitative Real-Time PCR (qPCR)
Cells were harvested and lysed using Trizol reagent according to the manufacturer’s instructions. Total RNA was then reverse-transcribed into cDNA using the Prime RT Reagent Kit (Vazyme, Cat# R323). Next, 20 ng of cDNA was then combined with gene-specific primers and SYBR qPCR Master Mix (Vazyme, Cat# Q711). Relative mRNA levels were then measured using a quantitative real-time PCR assay. The primers were listed in Supplementary Table 2.

Dual-luciferase reporter assay
ESCC cell lines were seeded in 48-well plates to allow 70% confluence the following day. The reporter plasmids pmirGLO-INCENP and pmirGLO-CDCA8 were then transfected into mock and shYTHDF3 cells at 200 ng per well using Lipofactamine 2000 (Invitrogen) according to the manufacturer’s protocol. 48 h later, firefly luciferase and renilla luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega) according to the supplier’s instructions.

SUnSET assay
The SUnSET assay was performed to monitor protein synthesis rates using a western blot. Briefly, pcDNA3.1-3×flag vector and Flag-YTHDF3 transfected cells were seeded in a 12-well plate at a density of 5 × 104 cells per well and incubated in culture medium supplemented with puromycin (2 μg/mL) for the indicated time periods. Proteins were then extracted from the cells, and Western blotting was conducted using an anti-puromycin antibody.

Proximity ligation assay (PLA)
PLA was performed according to the Duolink® In Situ Red Starter Kit (Mouse/Rabbit). In detail, cells were seeded onto sterile coverslips in a 24-well tissue-culture plate supplemented with 1 ml complete culture media and incubated at 37 °C under 5% CO2. After incubation for 24 h, the supernatant was removed and the cells were rinsed three times with 1 mL PBS, followed by fixation with 4% paraformaldehyde for 15 min. After three washes with PBS, the cells were incubated with 0.6% Triton X-100 Buffer for 15 min. The cells were then blocked with Duolink block solution for 1 h at 37 °C, and incubated overnight at 4 °C with the following antibodies: mouse anti-eIF3A and rabbit anti-YTHDF3. Subsequently, the cells were washed twice with 40 µL of 1× Duolink in situ wash buffer A and incubated in a mixture of PLA probes (anti-rabbit PLUS and anti-mouse MINUS, respectively) for 1 h at 37 °C. The cells were washed twice with 1× Duolink in situ wash buffer A and incubated with ligation mix at 37 °C for 30 min. The cells were then washed twice with 1× Duolink in situ wash buffer A and incubated with amplification mix at 37 °C for 100 min. Finally, the cells were washed twice with 1× Duolink in situ wash buffer B, washed once with 0.01× Buffer B, and mounted with Duolink in situ mounting medium with DAPI. Images were captured using an A1R Nikon confocal microscope.

m6A RNA methylation assay
For quantification of methylated m6A RNA, total RNA was extracted from human cells, and 200 ng RNA was used as input for the EpiQuik™ m6A RNA Methylation Quantification Kit (Colorimetric). The kit was used according to the manufacturer’s instructions. The absolute m6A abundance of the sample was proportional to the absorbance at a wavelength of 450 nm as measured by a microplate reader (Thermo Scientific Multiskan GO).

Immunohistochemistry
The use of paraffin-embedded human ESCC (cohort 1, n=14; cohort 2, n=68; cohort 3, n=65), lung cancer (n=26), and breast cancer (n=22) tissue samples was approved by the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital. All tissue samples were collected in compliance with the informed consent policy. For the detailed method, the tissues were subjected to antigen retrieval and blocking according to the rabbit or mouse SP Detection Kit (Cat# SP9001, ZSGB-BIO). The tissue slices were incubated with 50 µL YTHDF3, INCENP, and CDCA8 antibodies at 4 °C overnight. The slices were then washed with 1× PBS and incubated with secondary antibodies at room temperature for 30 min. The slices were then stained with 3,3’-diaminobenzidine (DAB) working buffer. After re-staining with hematoxylin, the slices were photographed and analyzed using the ImageJ software. The staining of the tissue sections was quantitatively scored according to the percentage of positive cells. The following proportion scores were assigned to the sections: 0 for 0% of the tumor cell exhibited positive staining, 1 for 0–1% of cells stained, 2 for 2–10% of cells stained, 3 for 11–30% of cells stained, 4 for 31–70% of cells stained and 5 for 71–100% of cells stained. In addition, the staining intensity was rated on a scale of 0–3: 0, no staining; 1, weak; 2, moderate; and 3, strong. The proportion and intensity scores were then combined to obtain a total score (range, 0–8). The scores were compared with progression-free survival (PFS), which is defined as the time from the start of treatment to the time of disease progression.

meRIP assay
EpiQuik™ CUT&RUN m6A RNA Enrichment Kit was used to investigate the m6A modification of specific mRNA transcripts. 50 μg total RNA was isolated using Trizol reagent from KYSE30 and KYSE150 cells. Next, RNA sequence fragments containing the m6A target were cleaved, bound to magnetic beads containing an m6A antibody, and pulled down. Finally, the enriched RNA was released, purified, and eluted. According to the information provided on the SRAMP platform (http://www.cuilab.cn/sramp), we designed primers for specific mRNA transcripts. qPCR was subsequently conducted to detect differences in m6A modification of certain transcripts.

RIP-qPCR analysis
Tumor cells were grown in a 10-cm dish until 80% confluence was achieved. The cells were then harvested, and RNA immunoprecipitation was performed using the RNA Immunoprecipitation Kit according to the manufacturer’s instructions. Briefly, magnetic beads coated with normal antibodies against YTHDF3, eIF3A, mouse normal IgG, or rabbit normal IgG were individually incubated with cell lysates overnight at 4 °C. Collected RNA-protein complexes were washed five times and then subjected to RNA extraction for further analysis by quantitative real-time PCR. The primers were listed in Supplementary Table 2.

RIP-seq analysis
The RIP assay was performed on KYSE30 cells by SeqHealth (Wuhan, China). Cells were collected and lysed with cell lysis buffer. Ten percent (10%) of the lysate was stored as “input”, 80% was used in immunoprecipitation reactions with anti-YTHDF3 antibody (Abcam, ab220161), and the remaining 10% was incubated with rabbit IgG (Cell Signaling Technology) as a negative control and named “IgG”, respectively. The RNA of input and IP was extracted using TRIzol reagent (Invitrogen, cat. NO 15596026). The stranded RNA sequencing library was constructed by using KC-DigitalTM Stranded mRNA Library Prep Kit for Illumina® (Catalog NO. DR08502, Wuhan Seqhealth Co., Ltd, China) following the manufacturer’s instructions. The library products corresponding to 200–500 bp were enriched, quantified, and finally sequenced on a DNBSEQ-T7 sequencer (MGI Tech Co., Ltd, China) with the PE150 model. Raw sequencing data were initially filtered by Trimmomatic (version 0.36). Low-quality reads were discarded, and reads contaminated with adaptor sequences were trimmed. Clean reads were further treated with in-house scripts to eliminate duplication bias introduced in library preparation and sequencing. In brief, clean reads were first clustered by UMI sequence, and reads sharing the same UMI sequence were grouped into the same cluster. Reads in the same cluster were compared to each other by pairwise alignment, and then reads with sequence identity over 95% were extracted to a new sub-cluster. After all sub-clusters were generated, multiple sequence alignment was performed to get one consensus sequence for each sub-cluster. After these steps, any errors and biases introduced by PCR amplification or sequencing were eliminated. The de-duplicated consensus sequences were used for protein binding site analysis. Sequences were mapped to the reference Homo sapiens (human) genome from NCBI using STAR software (version 2.5.3a) with default parameters. The RSeQC (version 2.6) was used for read distribution analysis. The exomePeak (Version 3.8) software was used for peak calling. Peaks were annotated using bedtools (Version 2.25.0). The deepTools (version 2.4.1) was used for peak distribution analysis. The differentially binding peaks were identified by a Python script using the Fisher test. Sequence motifs enriched in peak regions were identified using Homer (version 4.10). Gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis for annotated genes were both implemented by KOBAS software (version: 2.1.1) with a corrected P-value cutoff of 0.05 to judge statistically significant enrichment.

Transcriptomic analysis
ESCC tumor tissues (cohort 1, n=14) from patients were collected during surgery in the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital in China. Appropriate tissue samples were transferred into the corresponding tissue grinding tube for sample lysis using TRIzol reagent, then RNA purification, quality testing, mRNA isolation, fragmentation, cDNA synthesis, end repair, Add A and Adaptor Ligation, PCR, corresponding library quality control, circularization, and sequencing process were performed. The sequencing data were filtered with SOAPnuke; afterwards, clean reads were obtained. Bowtie2 was applied to align the clean reads to the geneset, in which known coding and noncoding transcripts were included. The expression levels of genes were calculated by RSEM (v1.3.1). Essentially, differential expression analysis was performed using the DESeq2 (v1.4.5) (or DEGseq or Poisson Dis) with Q value ≤ 0.05 (or FDR ≤ 0.001). To gain insight into the change of phenotype, GO (http://www.geneontology.org/) and KEGG enrichment analysis of annotated differentially expressed genes was performed by Phyper based on the Hypergeometric test. The significant levels of terms and pathways were corrected by Q value with a rigorous threshold (Q value ≤ 0.05).

Label-free proteomic analysis
The proteomic analysis was performed on mock and shYTHDF3 KYSE30 cells (n=3 technical replicates per group). In total, six samples were analyzed: three for mock cells and three for shYTHDF3 cells. As controls, mock-treated cells were included. Initially, total cellular protein was extracted using SDT (4% SDS, 100 mM Tris-HCl, pH 7.6) buffer. The protein concentration was quantified using a BCA Protein Assay Kit (Bio-Rad, USA). 20 µg of protein for each sample was individually mixed with 5× loading buffer and boiled for 5 min. The proteins were then resolved on a 4–20% SDS-PAGE gel (constant voltage 180 V, 45 min). Protein bands were visualized by Coomassie Blue R-250 staining. Protein digestion by trypsin was performed according to the filter-aided sample preparation (FASP) procedure described by Wisniewski50. LC-MS/MS was performed on an Orbitrap Astral mass spectrometer coupled with a Vanquish Neo UHPLC system (Thermo Fisher Scientific). Peptides from each sample were loaded into a column (50 cm Low-Load µPAC™ Neo HPLC Column, Thermo Scientific) at a flow rate of 2.2 μL/min. Peptides were eluted with a gradient of 0.1% formic acid in water (A) and 0.1% formic acid in 80% acetonitrile (B) at 2.2 μL/min. The eluted peptides were analyzed on an Orbitrap Astral mass spectrometer. DIA MS/MS scans were acquired from 150 to 2000 m/z with a 2 m/z isolation window, 500% AGC target, and 3 ms injection time. Data were recorded in profile mode for MS scans and centroid mode for DIA scans. Data analysis was performed using DIA-NN 1.8.151 for protein identification, with the MS data searched against the UniProtKB human proteome database52. Trypsin was used for digestion, with one missed cleavage allowed and a mass tolerance of 10 ppm. Carbamidomethylation of cysteines was set as a fixed modification, and acetylation of the N-terminus and oxidation of methionine were variable modifications. Results were filtered with a false discovery rate (FDR) < 1%. Bioinformatics analysis was performed in R, including hierarchical clustering, volcano plots, GO, and KEGG enrichment analysis using Fisher’s exact test p<0.01 with FDR correction. Protein–protein interaction (PPI) networks were constructed using STRING and Cytoscape software53.

Statistical analysis
Statistical analyses were conducted using the GraphPad Prism 9 software. Only biological replicates were used for statistical analyses. A two-tailed unpaired Student’s t-test was conducted for comparison of means of data between two groups. One-way ANOVA was used to compare multiple independent groups, whereas two-way ANOVA was employed to analyze the interaction between two independent variables and their effects on the dependent variable. All graphs are presented as mean ± SD and exact P values; P < 0.05 was recognized as statistically significant.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The transcriptomic raw sequenced data generated in this study have been deposited in the GSA-Human database under the accession code HRA011110. The raw sequence data are available under restricted access for research purposes only, access can be obtained by the DAC (DataAccess Committees) of the GSA-human database. According to the guidelines of GSA-human, all non-profit researchers are allowed access to the data, and the Principle Investigator of any research group is allowed to apply for Controlled-access of the data. The user can register the GSA database [https://ngdc.cncb.ac.cn/gsa-human/] and request the data. The approximate response time for accession requests is about 3 days. The access authority can be obtained for Research Use Only. The user can also contact the corresponding author directly. Once access has been approved, the data will be available to download for 2 months. The mass spectrometry proteomics publicly available data for Mock and shYTHDF3 cells generated in this study have been deposited in the ProteomeXchange Consortium database under accession code PXD062909 [https://www.iprox.cn//page/project.html?id=IPX0011698000]. The original RIP-Seq publicly available data for KYSE30 cells have been deposited in the NCBI GEO database under the accession code GSE294561. The remaining data are available in the Article, Supplementary Information or Source Data file. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Wang X. et al reported a predictive role of YTHDF3 in paclitaxel-based NACT by upregulating INCENP and CDCA8. It have been well-reported that either YTHDF3 or METTL3 are associated with poor prognosis of many cancers. Authors firstly reported the m6A-modification dependent role of YTHDF3 to promote the translation of INCENP and CDCA8, which was not surprising at all.\n1, The mechanism of INCENP and CDCA8 to confer paclitaxel non-response should be the major novelty of this study, which was completely missing.\n2, In 4e/f, knock-down of INCENP and CDCA8 inhibited cell growth in the absence of paclitaxel, indicating that the upregulation of INCENP and CDCA8 may render poor prognosis irrespective of paclitaxel treatment.\n3, In 4K, knock-down of YTHDF3 inhibited cell growth significantly in the absence of paclitaxel and the growth-inhibition of YTHDF3 KD cells seems to be significantly more sensitive to paclitaxel with the difference quite moderate.\n4, Authors should make clear that it is paclitaxel-based NACT but nit NACT in general and point out INCENP and CDCA8 in the title.", "reviewer_2": "Wang et al. are evaluating the role of YTHDF3 in neoadjuvant chemotherapy (NACT) for esophageal squamous cell carcinoma (ESCC). The authors identify YTHDF3 as a critical mediator of poor response to paclitaxel-based NACT, link YTHDF3's regulation of INCENP and CDCA8 mRNA translation in an m6A-dependent manner to enhanced cell division, thus offering a mechanistic explanation for its role in NACT resistance. This study is exploring an important topic. The manuscript reads well, with a clear and logical flow throughout. The data is compelling for the most part, and extensive analysis has been performed to substantiate the role of YTHDF3 in NACT resistance in esophageal squamous cell carcinoma (ESCC). To improve this manuscript, the authors should address the following points.\nMajor comments:\n1. The authors used a heatmap to claim that 9 m6A regulators were upregulated in non-responders (Fig. 1b). However, I noticed that the sixth responder (from the left) also exhibited a more pronounced upregulation of these 9 m6A regulators. Conversely, the sixth and ninth non-responders showed decreased expression of m6A regulators, including YTHDF3, which the authors highlighted. The authors should address these observations in the manuscript to clarify the interpretation of this data. Additionally, the selection of YTHDF3 as a candidate is not strongly justified based on the heatmap. Furthermore, the authors mention METTL3 in later sections; therefore, its expression should also be shown in this heatmap. The authors should also clarify what the \"p\" denotes beside the heatmap in the figure legend.\n2. The authors used a heatmap and GSEA results to claim that INCENP and CDCA8 are regulated by YTHDF3 and subsequently identified these two genes as downstream targets of YTHDF3. However, there are other candidates listed, and the authors should clarify the criteria used to select these two genes.\n3. The authors used the same scale bar for Fig. 4a and Fig. 4c, implying that the figures should display cells with the same size. However, it appears that the cells in Fig. 4c are smaller than those in Fig. 4a, leading to some ambiguity in interpreting the results. The authors should clarify whether this discrepancy is due to a typo error or some other reasons.\n4. The authors used Western blotting to show the expression levels of INCENP and CDCA8 in Fig. 5a, Fig. 5m, Fig. 6c, and Fig. 7j. However, there are inconsistencies in the bands presented. For example, INCENP shows two bands in KYSE30 cells in Fig. 6c, whereas other figures display only one band. Similarly, CDCA8 appears to have a faint band above the main band in Fig. 5a and Fig. 6c, but other figures show only a single, clear band. These inconsistencies need to be addressed to ensure data reliability.\n5. The authors investigated the regulation of INCENP and CDCA8 by YTHDF3 throughout the manuscript. However, it remains unclear which of these factors is more critical as a downstream effector of YTHDF3. Fig. 7i suggests that either INCENP or CDCA8 can rescue the phenotype of multipolar spindle formation in shYTHDF3 cells. The authors should discuss this in more detail to clarify the relative importance of these factors.\n6. In Figures 5a and 6c, rescue effect of YTHDF3 on CDCA8 and INCENP expression is ambiguous. Data needs to be normalized for GAPDH and multiple experiments performed and plotted.\n7. For figure 5b and 5c, the authors stated that YTHDF3 affected the protein levels of INCENP and CDCA8. As YTHDF3 promotes cell proliferation, how can the authors substantiate that the alterations in protein levels of INCENP and CDCA8 are attributable to YTHDF3 expression rather than changes in cell number?\n8. The observation that eIF3A and YTHDF3 can also be precipitated by IgG (Figure 5i) should be repeated and interpreted.\n9. The data looks pretty good in figure 8e and 8f, may I know which one is more important for the prediction the clinical outcomes of NACT response.\nMinor comments:\n1. Statistics are not always shown in figures. This needs to be addressed for each panel (e.g., Western blots and IF, IHC staining).\n2. Most of the figures are densely populated. Font size is pretty small, making it hard to decipher certain figures.\n3. P6 line 216, should it be fig. 4e, f?\n4: P3 line 106, the brackets should include IHC instead of Immunohistochemistry staining.\nP4 line 147, “proteomics sequencing” is not a proper terminology.\nP12 line 422, there is an incorrect back bracket.\nP12 line 424, Should it be 70 μm strainer?\nP12 line 443, it should be 0.45-μm PVDF membrane.\nP13 line 462, “Te cells” should be revised.\nP39 Extended Data Fig. 1b, “Ggrade II” should be revised.\n5. Other grammar and typo issues, eg., line 111 “. and”, line 194 “, (Fig. 3d)”.", "reviewer_3": "YTHDF3 predicts neoadjuvant chemotherapy response and outcomes in Esophageal Squamous Cell Carcinoma by Xiangyu Wang, have studied the role of YTH domain-containing family protein 3 (YTHDF3) in predicting poor responses to neoadjuvant chemotherapy (NACT) in esophageal squamous cell carcinoma (ESCC) patients. YTHDF3 enhances cell mitosis by regulating the translation of specific mRNAs, leading to diminished effectiveness of paclitaxel-based NACT. High levels of YTHDF3 correlate with worse patient outcomes. The study suggests that Targeting YTHDF3 not only improves responses to chemotherapy but also offers a potential therapeutic strategy to enhance treatment efficacy and survival rates in cancer patients.\nYTHDF3 is crucial for predicting outcomes in breast cancer and hepatocellular carcinoma. Previous studies have established a YTHDF3-based model for overall survival prediction. While research on esophageal cancer has been limited, the mechanisms underlying YTHDF3 overexpression are now understood. The authors' study of YTHDF3 upregulation in esophageal cancer, alongside the use of paclitaxel as a neoadjuvant chemotherapy agent and its associated sensitivity, could provide a significant contribution worthy of publication in Nature Communications.\nOne of the major criticism for this manuscript is, multiple way of action of Paclitaxel has been identified, till Paclitaxel is in the clinic. There are seminal publications by Antonio Fojo group and Fernando Cabral group more than decades ago stating that Paclitaxel can inhibit cell division as a antimitotic agent, but can also inhibit, cell migration and angiogenesis. The authors fully focused on the effect of cell division after YTHDF3 overexpression. Though there are recent publications on the impact of YTDF3 on cell migration and angiogenesis, the authors overlooked that for this publication.\nThere are several items that can be improved before considered for publication. To investigate the role of YTHDF3 in response to paclitaxel, the authors have assessed whether inhibiting YTHDF3 could increase the sensitivity of ESCC cells to paclitaxel treatment.\nThe term “Adjacent” in Fig 1E or throughout the manuscript is difficult to follow. What the authors meant by adjacent? Also , what is the basis for the selection of “responders vs non responders.” The authors stated in the Fig 1n that elevated YTHDF3 levels were negatively correlated with tumor remission rates (Fig. 1n).Did the author found correlation coefficient? A graph showing reverse correlation will be beneficial. Also which method the authors used to calculate correlation? There are similar problems when the authors talk about positive and negative correlations, the numbers and significance of the correlation should be provided.\nThe authors have used YTDHF3 mutant. Are they dominant negative? If they are not what is the rationale of using them, when the authors have already used shRNA and mock sh RNA for their study. The authors have calculated mitotic sister chromatid separation and nuclear envelop breakdown. Why the authors used both the methods? Is it to rule out mitotic slippage? Better explanation is needed.\nThe authors did not address the significance of the pathways impacted by YTDF3 downregulation, beyond those related to cell division. Given the availability of RNA sequencing data, it would be valuable to explore additional effects on other signaling pathways, such as those involved in cell migration or epithelial-to-mesenchymal transition (EMT) and mesenchymal-to-epithelial transition (MET). A more thorough examination of these pathways could enhance the novelty of the manuscript.\nA significant limitation of the paper is the authors' focus solely on INCENP and CDCA8. While the data clearly demonstrate effects on the cell division pathway, it's important to note that the average cell division time in humans is around 90 days, with only about 2% of cells being mitotic at any given time. Additionally, Paclitaxel is administered at maximum tolerable doses, leading to only a limited number of mitotic blocks in vivo within the treatment window. This context may explain why the IHC data from patients do not show an increased propensity for mitotic blockage. I encourage the authors to discuss how YTHDF3 downregulation may affect or sensitize other antitumorigenic pathways.", "reviewer_4": "The work described in the manuscript seeks to identify biomarkers predictive of clinical response to neoadjuvant chemotherapy (NACT) in patients with esophageal squamous cell carcinoma (ESCC) before surgery. Using transcriptomic analyses in tumors from patients who responded to paclitaxel-based NACT vs the non-responders, they identified an enrichment for YTHDF3 transcript in the non-responders. YTHDF3 is a cytoplasmic regulator of m6A (N6-methyladenosine). YTHDF3 was also upregulated in cancer versus adjacent normal tissue, and correlated with higher clinical tumor stage and tumor infiltration depth. They used many cell biology and multi-omics tools to elaborate on a potential mechanism by which YTHDF3 induces the resistance phenotype. Authors show that high levels of YTHDF3 upregulate the translation of INCENP and CDCA8 transcripts (components of the chromosomal passenger complex) by recruiting eID3A proteins to its target mRNAs. Authors speculate that using Ebselen, a chemical inhibitor of YTHDF3, will induce defects in the mitotic spindle and sensitize ESCC patients' response expressing high levels of YTHDF3 to paclitaxel. However, there are major concerns for this manuscript as outlined below.\nMajor concerns:\n1. Taxanes are mostly used in combination with cisplatin or carboplatin in the neoadjuvant setting for ESCC. So, the correlations in the current study between the YTHDF3 transcript and response to NACT treatment, refer to clinical response to the drug cocktail used and not to paclitaxel specifically. Thus, the current study by addressing mechanisms relevant to taxanes only, do not really address the clinical problem of NACT failure in patients with ESCC\na. Thus, in Figure 1 clinical response does not reflect paclitaxel-only response and also if the transcript is associated with response to treatment, overall survival is not the appropriate metric. Instead they should use pathologic response prior to surgery\nb. In Figure 1O-time to death should be displayed as opposed to death alone-also the authors do not mention if the patients received additional treatment after surgery and how such treatment is weighing into their correlations\n2. The authors claim that YTHDF3 KD or OE modulates the sensitivity of paclitaxel. Unfortunately, there are important concerns in the data that do not support this statement. Several important controls are missing, thereby diminishing the experimental rigor of these results\n- Is there any correlation between the basal expression levels of the YTHDF3 with taxane sensitivity/resistance in the KYSE cell panel?\n-To determine the role of YTHDF3 in taxane resistance in vitro, they treat different ESCC cell lines (parental vs. shYTHDF3 (Fig.2A) and vs. Ebselen treated (unknown concentration) (Fig.2C) at different concentrations of paclitaxel. The ranges of [paclitaxel] used in these experiments to determine the IC50 are inappropriate. For example, they use a range from 0 to 8nM paclitaxel to estimate an IC50 of 92nM.\n-The colony formation assay shows very modest differential response to paclitaxel treatment in YTHDF3 KD (Fig 2B) and cells treated with Ebselen (unknown concentration) (Fig 2D). The authors also need to test the effect of Ebselen on the cell viability of YTHDF3 KD cell lines. If the inhibition is through the same pathway of YTHDF3, no impact or small additive effect should be produced by Ebselen treatment in YTHDF3 KD cells.\n-In Figure 2E, the IC50 fitting of the experimental data to calculate the IC50 needs to be revised. A more precise fitting needs to be performed, which might generate different outcomes. Also, they need to validate the expression levels of shYTHDF3 EO by Western blot and their cytoplasmic distribution by immunofluorescence\n-In Fig 2G, I assumed that the drug concentrations of Ebselen + paclitaxel administered to the animals are identical to the ones in a single treatment. That needs to be clarified in the manuscript\n-In Figure 2M, authors need to perform densitometry analysis to quantify if YTHDF3 KD induces the decrease of protein expression of G2 phase regulators. It is unclear if this is the case in this figure.\n-In extended data Fig.2. the authors rescue the knockdown of YTHDF3 in KYSE-30 and -150 cells by OE of YTHDF3 wt and mutant. I couldn’t find in the text which shRNAs were used to KD endogenous proteins without affecting the expression of the ectopically expressed proteins. More experimental details are needed.\n-In Figure 4, to demonstrate that INCENP and CDCA8 OE abolish the effect of YTHDF3 KD on ESCC cells in the presence of paclitaxel treatment (fig 4K and fig4L / 1nM paclitaxel), they need to perform a cell viability assay similar to the ones shown in Fig 2a.\n-In Figure 5, authors claim that INCENP and CDCA8 proteins were evidently reduced in YTHDF3 KD cells (Fig 5A and 5B). They need to provide a better-quality WB that proves that together with quantification. The same case is true for the OE of YTHDF3 in 5a (right panel). It is not evident that the OE of YTHDF3 increases the expression levels of INCENP and CDCA8. Images in 5B are too small, and they need to describe how the images were collected and quantified\n-In Figure 8: It must be clarified if the retrospective samples from cohort 2 were obtained pre- or post-placitaxel treatment. Do the grey bars mean that there is no sample information? What is the reason for the number of grey bars in the responder patients?\nMinor points:\nCited references do not always correspond to the text.\nLow quality of figure. Figures need to be bigger and more accessible to interpret. There are missing units in most of the graph axis, none of the western blots are quantified, and confocal images are too small.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Dear Editor,\nWe sincerely appreciate the time and effort that the Editor and Reviewers have\ndedicated to providing valuable and constructive feedback on our manuscript,\nNCOMMS-24-47215-T, titled \"YTHDF3 predicts neoadjuvant chemotherapy\nresponse and outcomes in Esophageal Squamous Cell Carcinoma\". In response to the\nreviewers' suggestions, we have carefully considered each comment and conducted\nadditional experiments and analyses to clarify and strengthen our findings.\nWe believe the revisions we have made substantially improve the quality, depth\nand novelty of the manuscript and satisfy all of the reviewer's comments.\nWe respectfully request that the Editor consider the manuscript for publication in Nature\nCommunications. We greatly appreciate the opportunity to submit this revised\nmanuscript.\nBelow, we provide a point-by-point response to each of the reviewers’ comments\nand outline our revisions.\nReviewers' comments:\nReviewer #1 (Remarks to the Author):\nIn this manuscript, Wang X. et al reported a predictive role of YTHDF3 in\npaclitaxel-based NACT by upregulating INCENP and CDCA8. It have been well-\nreported that either YTHDF3 or METTL3 are associated with poor prognosis of many\ncancers. Authors firstly reported the m6A-modification dependent role of YTHDF3 to\npromote the translation of INCENP and CDCA8, which was not surprising at all.\n1, The mechanism of INCENP and CDCA8 to confer paclitaxel non-response should be\nthe major novelty of this study, which was completely missing.\nResponse: Thank you for your valuable comment. We have now focus on the role of\nINCENP and CDCA8 in paclitaxel non-response and the mechanism of INCENP and\nCDCA8 to confer NACT non-response have deeply researched. Accordingly, we have\nrevised the manuscript to reflect these key findings:\n(1) INCENP and CDCA8 is a critical component of the chromosome passenger\ncomplex (CPC), however, the role of INCENP and CDCA8 in regulating NACT\nresistance remains unexplored. Our analysis of tumor tissue from NACT patients\nshowed that INCENP and CDCA8 were significantly upregulated in non-responders\nacross various cancer types (Fig. 1 and Fig. 7, line361). And we validated that knocking\ndown INCENP and CDCA8 enhanced the sensitivity of paclitaxel and cisplatin (Fig.\n2).\n(2) We have clarified that the mechanism of INCENP and CDCA8 to confer paclitaxel\nnon-response in revised manuscript (Fig. 3-6). In detail, inhibition of INCENP and\nCDCA8 promotes multipolar spindle formation, resulting in more severe mitotic\ndefects, leading to enhanced paclitaxel efficacy and increased cell death, thus\nimproving NACT response to patient. In non-responders, the upregulation of INCENP\nand CDCA8 results from m6A modification recognized by YTHDF3, leading to\nenhanced translation, ultimately leading to NACT resistance. Following Mechanism\ndiagram explained it in detail:\n2, In 4e/f, knock-down of INCENP and CDCA8 inhibited cell growth in the absence of\npaclitaxel, indicating that the upregulation of INCENP and CDCA8 may render poor\nprognosis irrespective of paclitaxel treatment.\nResponse: Thank you for your valuable comment. We agree with your comments that\nINCENP and CDCA8, as key regulators of mitosis, play an important role in cell\nproliferation. Our study is focus on searching biomarkers to predict patient response to\nNACT and improve 5-year survival rate, avoiding patients suffer from the toxic side\neffects of chemotherapy. And we identified that reliable biomarkers INCENP and\nCDCA8 predict patient responsiveness to NACT across various cancer types.\n3, In 4K, knock-down of YTHDF3 inhibited cell growth significantly in the absence of\npaclitaxel and the growth-inhibition of YTHDF3 KD cells seems to be significantly\nmore sensitive to paclitaxel with the difference quite moderate.\nResponse: Thank you for your constructive suggestions, as well as those from\nReviewer #4. Upon careful consideration, we recognized that the limitations in our\ninitial experimental design may have contributed to the moderate results observed. To\naddress this, we have optimized our experimental methodology and using a wider range\nof paclitaxel and cisplatin concentrations to assess the effect of YTHDF3 KD on\npaclitaxel and cisplatin sensitivity. These improvements obtain more robust and\nstatistically significant results, as demonstrated in the revised manuscript (Fig. 3c, d).\n4, Authors should make clear that it is paclitaxel-based NACT but not NACT in general\nand point out INCENP and CDCA8 in the title.\nResponse: Thank you for your thoughtful and constructive feedback. we have made it\nclear that the regimes of NACT are paclitaxel plus cisplatin in revised manuscript. And\nthe novel title of this article is \"INCENP and CDCA8 predict neoadjuvant\nchemotherapy response and outcomes in pan-cancer\".\nReviewer #2 (Remarks to the Author):\nWang et al. are evaluating the role of YTHDF3 in neoadjuvant chemotherapy (NACT)\nfor esophageal squamous cell carcinoma (ESCC). The authors identify YTHDF3 as a\ncritical mediator of poor response to paclitaxel-based NACT, link YTHDF3's regulation\nof INCENP and CDCA8 mRNA translation in an m6A-dependent manner to enhanced\ncell division, thus offering a mechanistic explanation for its role in NACT resistance.\nThis study is exploring an important topic. The manuscript reads well, with a clear and\nlogical flow throughout. The data is compelling for the most part, and extensive\nanalysis has been performed to substantiate the role of YTHDF3 in NACT resistance in\nesophageal squamous cell carcinoma (ESCC). To improve this manuscript, the authors\nshould address the following points.\nMajor comments:\n1. The authors used a heatmap to claim that 9 m6A regulators were upregulated in non-\nresponders (Fig. 1b). However, I noticed that the sixth responder (from the left) also\nexhibited a more pronounced upregulation of these 9 m6A regulators. Conversely, the\nsixth and ninth non-responders showed decreased expression of m6A regulators,\nincluding YTHDF3, which the authors highlighted. The authors should address these\nobservations in the manuscript to clarify the interpretation of this data. Additionally,\nthe selection of YTHDF3 as a candidate is not strongly justified based on the heatmap.\nFurthermore, the authors mention METTL3 in later sections; therefore, its expression\nshould also be shown in this heatmap. The authors should also clarify what the \"p\"\ndenotes beside the heatmap in the figure legend.\nResponse: Thank you for your valuable comments. In previous manuscript, Figure 1b\npresented sequencing data from paclitaxel-based NACT samples. According to the\nreviewer’s suggestion and to ensure consistency in treatment regimen, we have now\nfocused our analysis exclusively on patients who received the cisplatin plus paclitaxel\nNACT regimen throughout our study. We have also performed additionally sequencing\nto supply cohort1 (supplementary table 1). Subsequent analysis revealed that YTHDF3\nexhibited the most significantly differentially expressed protein among the 20 m6A\nregulatory proteins, and the expression level of METTL3 was analysis and has no\nchanges in non-responders. Additionally, we have clarified the statistical significance\n(\"p\") in the revised Figure 1c to improve transparency.\n2. The authors used a heatmap and GSEA results to claim that INCENP and CDCA8\nare regulated by YTHDF3 and subsequently identified these two genes as downstream\ntargets of YTHDF3. However, there are other candidates listed, and the authors should\nclarify the criteria used to select these two genes.\nResponse: Thank you for your insightful comments. In the manuscript (line 114), we\nclarified that GO enrichment and GESA analysis highlighted the chromosomal\npassenger complex (CPC) in the cellular component section as the top altered pathway\nand involved in mitotic nuclear division by proteomics analysis in shYTHDF3 ESCC\ncells (Fig.1d, e). Importantly, GSEA analysis revealed that key proteins in the CPC,\nincluding INCENP, CDCA8, and AURKB, were significantly downregulated in\nshYTHDF3 cells. Western blotting confirmed that INCENP and CDCA8 showed more\npronounced changes in both shYTHDF3 and OE-YTHDF3 ESCC cell lines\n(Supplementary Fig. 1e-h), therefore, we selected and continued to research INCENP\nand CDCA8.\n3. The authors used the same scale bar for Fig. 4a and Fig. 4c, implying that the figures\nshould display cells with the same size. However, it appears that the cells in Fig. 4c are\nsmaller than those in Fig. 4a, leading to some ambiguity in interpreting the results. The\nauthors should clarify whether this discrepancy is due to a typo error or some other\nreasons.\nResponse: Thank you for your careful consideration. We sincerely apologize for our\nmistakes, we have clarified Fig. 4c in the revised manuscript as show in Supplementary\nFig. 1i, and have indicated the scale bar in the figure legend.\n4. The authors used Western blotting to show the expression levels of INCENP and\nCDCA8 in Fig. 5a, Fig. 5m, Fig. 6c, and Fig. 7j. However, there are inconsistencies in\nthe bands presented. For example, INCENP shows two bands in KYSE30 cells in Fig.\n6c, whereas other figures display only one band. Similarly, CDCA8 appears to have a\nfaint band above the main band in Fig. 5a and Fig. 6c, but other figures show only a\nsingle, clear band. These inconsistencies need to be addressed to ensure data reliability.\nResponse: Thank you for your thoughtful observation regarding the inconsistencies in\nthe Western blot bands for INCENP and CDCA8 across the figures. Upon reviewing\nthe data, the band inconsistencies are due to the use of different antibody clones.\nTherefore, the same antibody was standardized for all WB experiments to ensure\nconsistency. The expression levels of INCENP and CDCA8 in Fig. 5a and Fig. 6c\ndisplay clearer and more consistent bands across all figures (Fig. 4a, b and\nSupplementary Fig. 1e, g).\n5. The authors investigated the regulation of INCENP and CDCA8 by YTHDF3\nthroughout the manuscript. However, it remains unclear which of these factors is more\ncritical as a downstream effector of YTHDF3. Fig. 7i suggests that either INCENP or\nCDCA8 can rescue the phenotype of multipolar spindle formation in shYTHDF3 cells.\nThe authors should discuss this in more detail to clarify the relative importance of these\nfactors.\nResponse: Thank you for your thoughtful and constructive comment. Both INCENP\nand CDCA8 are core components of the Chromosomal Passenger Complex (CPC) and\nare functionally indispensable for proper mitotic progression. Our rescue experiments\ndemonstrated that overexpression of either INCENP or CDCA8 in YTHDF3 KD cells\neffectively reversed the increased sensitivity to paclitaxel and cisplatin, highlighting\ntheir functional importance downstream of YTHDF3. While INCENP showed a slightly\nstronger rescue effect, both proteins are essential mediators of YTHDF3-driven\nchemoresistance (Fig. 3o, p).\n6. In Figures 5a and 6c, rescue effect of YTHDF3 on CDCA8 and INCENP expression\nis ambiguous. Data needs to be normalized for GAPDH and multiple experiments\nperformed and plotted.\nResponse: Thank you for your valuable feedback. we have now normalized the\nexpression levels of CDCA8 and INCENP to GAPDH. And we have updated the figures\nto include the normalized data across multiple experiments (Supplementary Fig. 4b, c).\n7. For figure 5b and 5c, the authors stated that YTHDF3 affected the protein levels of\nINCENP and CDCA8. As YTHDF3 promotes cell proliferation, how can the authors\nsubstantiate that the alterations in protein levels of INCENP and CDCA8 are\nattributable to YTHDF3 expression rather than changes in cell number?\nResponse: Thank you for your valuable suggestions. To address the concern about cell\nnumber, we used ImageJ to quantify the relative protein expression levels of INCENP\nand CDCA8, normalizing them to the total cell number. This ensures that the observed\nchanges in protein levels reflect the effect of YTHDF3 expression rather than\ndifferences in cell number. We have also clarified how the IF images were collected and\nquantified (line 594).\n8. The observation that eIF3A and YTHDF3 can also be precipitated by IgG (Figure 5i)\nshould be repeated and interpreted.\nResponse: Thank you for your detailed and helpful suggestions. We found that protein\nA/G magnetic beads were not thoroughly washed during the experimental procedure,\nresulting in non-specific bands. To address this, we have repeated the experiment with\nmore stringent washing steps to ensure that no bead-associated proteins eIF3A and\nYTHDF3 were co-precipitated with the IgG (Fig. 5j, k).\n9. The data looks pretty good in figure 8e and 8f, may I know which one is more\nimportant for the prediction the clinical outcomes of NACT response.\nResponse: Thank you for your insightful suggestions. Considering that INCENP and\nCDCA8 are both essential subunits of the chromosomal passenger complex, the loss of\neither INCENP or CDCA8 severely compromises CPC function. Therefore, INCENP\nand CDCA8 are likely to be equally important in predicting clinical responses to NACT.\nWe have now discussed this point in the revised manuscript to clarify the importance\nof both factors in predicting clinical outcomes (line448).\nMinor comments:\n1. Statistics are not always shown in figures. This needs to be addressed for each panel\n(e.g., Western blots and IF, IHC staining).\nResponse: Thank you for your constructive feedback. We agree that the inclusion of\nstatistical analysis is important to enhance the clarity and robustness of the results. We\nhave presented the statistical data for the western blots in the Supplementary Fig. 2-6,\nand have statistically validated and clearly interpreted IF and IHC panels. The figure\nlegends have also been updated accordingly to reflect these changes.\n2. Most of the figures are densely populated. Font size is pretty small, making it hard to\ndecipher certain figures.\nResponse: Thank you for your valuable feedback. We have carefully revised the figures\nto improve their clarity. The font size has been increased to ensure figures are easily\ndecipherable.\n3. P6 line 216, should it be fig. 4e, f?\nResponse: Thank you for your detailed and helpful suggestions. We have carefully\nreviewed the manuscript to prevent such errors.\n4: P3 line 106, the brackets should include IHC instead of Immunohistochemistry\nstaining.\nResponse: Thank you for your careful reading. We have revised the brackets in the\nrevised manuscript (line 130).\nP4 line 147, “proteomics sequencing” is not a proper terminology.\nResponse: Thank you for highlighting it. The term “proteomics sequencing” has been\nrevised to “proteomic analysis” in the revised manuscript (Line 115) to ensure\nterminological accuracy.\nP12 line 422, there is an incorrect back bracket.\nResponse: We appreciate your attention to detail. The incorrect back bracket on Page\n12, Line 422 has been corrected in the revised manuscript (line 497).\nP12 line 424, Should it be 70 μm strainer?\nResponse: Thank you for your detailed and helpful suggestions. The correct\nspecification is a \"70 μm strainer\" as stated in Line 499.\nP12 line 443, it should be 0.45-μm PVDF membrane.\nResponse: Thank you for highlighting it. The correct specification is a \"0.45 μm PVDF\nmembrane \" as stated in Line 515.\nP13 line 462, “Te cells” should be revised.\nResponse: Thank you for your careful reading. \"Te cells\" has been revised to \"The\ncells\" in Line 534 of the revised manuscript.\nP39 Supplementary Fig. 1b, “Ggrade II” should be revised.\nResponse: We appreciate your attention to detail. We have double-checked the\nmanuscript and figures to avoid these similar errors.\n5. Other grammar and typo issues, eg., line 111 “. and”, line 194 “, (Fig. 3d)”.\nResponse: Thank you for your rigorous review. We have meticulously proofread the\nmanuscript to eliminate any spelling, grammatical, or typographical errors and to ensure\nlinguistic accuracy.\nReviewer #3 (Remarks to the Author):\nYTHDF3 predicts neoadjuvant chemotherapy response and outcomes in Esophageal\nSquamous Cell Carcinoma by Xiangyu Wang, have studied the role of YTH domain-\ncontaining family protein 3 (YTHDF3) in predicting poor responses to neoadjuvant\nchemotherapy (NACT) in esophageal squamous cell carcinoma (ESCC) patients.\nYTHDF3 enhances cell mitosis by regulating the translation of specific mRNAs, leading\nto diminished effectiveness of paclitaxel-based NACT. High levels of YTHDF3 correlate\nwith worse patient outcomes. The study suggests that Targeting YTHDF3 not only\nimproves responses to chemotherapy but also offers a potential therapeutic strategy to\nenhance treatment efficacy and survival rates in cancer patients.\nYTHDF3 is crucial for predicting outcomes in breast cancer and hepatocellular\ncarcinoma. Previous studies have established a YTHDF3-based model for overall\nsurvival prediction. While research on esophageal cancer has been limited, the\nmechanisms underlying YTHDF3 overexpression are now understood. The authors'\nstudy of YTHDF3 upregulation in esophageal cancer, alongside the use of paclitaxel as\na neoadjuvant chemotherapy agent and its associated sensitivity, could provide a\nsignificant contribution worthy of publication in Nature Communications.\n--One of the major criticism for this manuscript is, multiple way of action of Paclitaxel\nhas been identified, till Paclitaxel is in the clinic. There are seminal publications by\nAntonio Fojo group and Fernando Cabral group more than decades ago stating that\nPaclitaxel can inhibit cell division as a antimitotic agent, but can also inhibit, cell\nmigration and angiogenesis. The authors fully focused on the effect of cell division after\nYTHDF3 overexpression. Though there are recent publications on the impact of YTDF3\non cell migration and angiogenesis, the authors overlooked that for this publication.\nResponse: Thank you for your valuable comment. We fully agree that paclitaxel affects\nnot only cell division but also processes like angiogenesis and cell migration. In this\nmanuscript, we focus on identify reliable biomarkers that can predict neoadjuvant\nchemotherapy sensitivity, aiming to avoid the toxic side effects of chemotherapy for\npatients. Through our comprehensive investigation, we identified YTHDF3, INCENP\nand CDCA8, can serve as reliable and effective biomarkers for predicting neoadjuvant\nchemotherapy sensitivity, with particularly strong relevance across multiple cancer\ntypes. Additionally, we observed that YTHDF3 KD altered the expression of cell\nadhesion molecules (Supplementary Fig. 1c), which are involved in angiogenesis and\nmigration pathways, however, it ranked relatively low in significance in KEGG\npathway analysis, whereas cell cycle-related pathways, particularly those involving\nmitotic regulation, were consistently ranked among the top five. Given this clear\nenrichment and the strong correlation with the observed multipolar spindle formation\nin YTHDF3 KD cells, we prioritized the mechanistic dissection of the cell division\npathway in this study. Therefore, we believe that YTHDF3, along with INCENP and\nCDCA8, represents a more promising set of predictors for chemotherapy sensitivity.\n--There are several items that can be improved before considered for publication. To\ninvestigate the role of YTHDF3 in response to paclitaxel, the authors have assessed\nwhether inhibiting YTHDF3 could increase the sensitivity of ESCC cells to paclitaxel\ntreatment. The term “Adjacent” in Fig 1E or throughout the manuscript is difficult to\nfollow. What the authors meant by adjacent? Also , what is the basis for the selection\nof “responders vs non responders.” The authors stated in the Fig 1n that elevated\nYTHDF3 levels were negatively correlated with tumor remission rates (Fig. 1n). Did\nthe author found correlation coefficient? A graph showing reverse correlation will be\nbeneficial. Also which method the authors used to calculate correlation? There are\nsimilar problems when the authors talk about positive and negative correlations, the\nnumbers and significance of the correlation should be provided.\nResponse: Thank you for your detailed and helpful suggestions.\n--In Fig 1E, we have revised “Adjacent” revised into “Adjacent normal tissue” in\nSupplementary Fig. 1i, j.\n--And patients with evaluable responses were classified as responders (TRG0, 1) and\nnon-responders (TRG2, 3) based on the National Comprehensive Cancer Network\n(NCCN) TRG criteria. [Ajani, J. A. et al. Esophageal and Esophagogastric Junction\nCancers, Version 2.2023, NCCN Clinical Practice Guidelines in Oncology. J Natl\nCompr Canc Netw 21, 393-422, doi:10.6004/jnccn.2023.0019 (2023).]. We have\nclarified it in the revised manuscript (line 92).\n--We sincerely apologize for the confusion caused by the inaccurate description. The\ntumor remission rate is not a continuous variable, so correlation coefficient analysis\ncannot be applied to this metric.\n--In the revised manuscript, we have included the correlation coefficients using Pearson\nanalysis to examine both positive and negative correlations, along with their\ncorresponding significance values. These changes are reflected in the updated figures\nand figure legends (Fig.1h, 2a and b, 4p, 7f and g, Supplementary Fig. 3m, r and 7e).\n--The authors have used YTDHF3 mutant. Are they dominant negative? If they are not\nwhat is the rationale of using them, when the authors have already used shRNA and\nmock sh RNA for their study. The authors have calculated mitotic sister chromatid\nseparation and nuclear envelop breakdown. Why the authors used both the methods?\nIs it to rule out mitotic slippage? Better explanation is needed.\nResponse: Thank you for your valuable comment.\n-- The YTHDF3 mutant acts as a dominant negative regulator of INCENP and CDCA8\nexpression, thereby impairing cell division. YTHDF3 contains two hydrophobic\nresidues, W438 and W492, which are essential for specific recognition of m6A, and the\nmutant form of YTHDF3 lacks this m6A-binding capability. While we have previously\nused YTHDF3 shRNA to study its effect on cell division and the regulation of INCENP\nand CDCA8, in order to investigate whether YTHDF3 regulates INCENP and CDCA8\nin an m6A-dependent manner, we constructed a YTHDF3 mutant, and further\noverexpressed the mutated YTHDF3 in YTHDF3 KD cells. This demonstrated that the\nmutant YTHDF3 lost its regulatory effect on INCENP and CDCA8, thereby disrupting\ncell division in an m6A-dependent manner.\n--During mitosis, sister chromatid separation, nuclear envelope breakdown, and mitotic\nslippage are key events. However, our proteomics analysis of shYTHDF3 cells enriched\nonly the pathways related to sister chromatid separation and nuclear envelope\nbreakdown. Therefore, we focused on these two pathways for our study, our intention\nwas not to exclude or rule out mitotic slippage. In our study, differential gene\nenrichment analysis identified INCENP and CDCA8 as core components of the\nchromosomal passenger complex (CPC), which is essential for proper chromosome\nalignment, segregation, and cytokinesis. Downregulation of either INCENP or CDCA8\nleads to defects in these processes, resulting in the formation of multipolar spindles,\ndisrupting mitotic progression, and potentially contributing to poor chemotherapy\nresponse.\n--The authors did not address the significance of the pathways impacted by YTDF3\ndownregulation, beyond those related to cell division. Given the availability of RNA\nsequencing data, it would be valuable to explore additional effects on other signaling\npathways, such as those involved in cell migration or epithelial-to-mesenchymal\ntransition (EMT) and mesenchymal-to-epithelial transition (MET). A more thorough\nexamination of these pathways could enhance the novelty of the manuscript. A\nsignificant limitation of the paper is the authors' focus solely on INCENP and CDCA8.\nWhile the data clearly demonstrate effects on the cell division pathway, it's important\nto note that the average cell division time in humans is around 90 days, with only about\n2% of cells being mitotic at any given time. Additionally, Paclitaxel is administered at\nmaximum tolerable doses, leading to only a limited number of mitotic blocks in vivo\nwithin the treatment window. This context may explain why the IHC data from patients\ndo not show an increased propensity for mitotic blockage. I encourage the authors to\ndiscuss how YTHDF3 downregulation may affect or sensitize other antitumorigenic\npathways.\nResponse: We appreciate your suggestion to explore other signaling pathways. In our\nproteomics analysis following YTHDF3 knockdown, we observed changes in the\nexpression of cell adhesion molecules, suggesting that YTHDF3 may also influence\ncell migration or EMT/MET pathways. However, KEGG pathway enrichment analysis\nranked these pathways relatively low in significance, whereas cell cycle-related\npathways, particularly those involving mitotic regulation, were consistently ranked\namong the top five (Supplementary Fig. 1c). Given this clear enrichment and the strong\ncorrelation with the observed multipolar spindle formation in YTHDF3 KD cells, we\nprioritized the mechanistic dissection of the cell division pathway in this study. We\nacknowledge your point that only about 2% of cells are in mitosis at any given time in\nhumans, which may limit the overall impact of mitotic blockage by YTHDF3, INCENP\nand CDCA8. However, our findings show that these proteins are significantly\nupregulated in non-responder patients, making them particularly effective as predictive\nbiomarkers for NACT sensitivity. Our primary purpose is to identify reliable\nbiomarkers to help avoiding the unnecessary toxic effects of NACT.\nWe have now included a discussion on the potential roles of YTHDF3 in regulating\nadditional antitumorigenic pathways, such as EMT and cell migration. We fully agree\nthat a more thorough examination of these pathways could enhance the novelty and\nbreadth of the manuscript (line 422).\nReviewer #4 (Remarks to the Author):\nThe work described in the manuscript seeks to identify biomarkers predictive of clinical\nresponse to neoadjuvant chemotherapy (NACT) in patients with esophageal squamous\ncell carcinoma (ESCC) before surgery. Using transcriptomic analyses in tumors from\npatients who responded to paclitaxel-based NACT vs the non-responders, they\nidentified an enrichment for YTHDF3 transcript in the non-responders. YTHDF3 is a\ncytoplasmic regulator of m6A (N6-methyladenosine). YTHDF3 was also upregulated in\ncancer versus adjacent normal tissue, and correlated with higher clinical tumor stage\nand tumor infiltration depth. They used many cell biology and multi-omics tools to\nelaborate on a potential mechanism by which YTHDF3 induces the resistance\nphenotype. Authors show that high levels of YTHDF3 upregulate the translation of\nINCENP and CDCA8 transcripts (components of the chromosomal passenger complex)\nby recruiting eID3A proteins to its target mRNAs. Authors speculate that using Ebselen,\na chemical inhibitor of YTHDF3, will induce defects in the mitotic spindle and sensitize\nESCC patients' response expressing high levels of YTHDF3 to paclitaxel. However,\nthere are major concerns for this manuscript as outlined below.\nMajor concerns:\n1. Taxanes are mostly used in combination with cisplatin or carboplatin in the\nneoadjuvant setting for ESCC. So, the correlations in the current study between the\nYTHDF3 transcript and response to NACT treatment, refer to clinical response to the\ndrug cocktail used and not to paclitaxel specifically. Thus, the current study by\naddressing mechanisms relevant to taxanes only, do not really address the clinical\nproblem of NACT failure in patients with ESCC.\nResponse: Thank you for your thoughtful and constructive comment. We fully agree\nwith taxanes are commonly used in combination with cisplatin or carboplatin in the\nneoadjuvant setting for ESCC. To address this concern, we have systematically revised\nour study design to specifically focus on the paclitaxel (taxanes) plus cisplatin\ncombination regimen throughout our investigation. This refinement ensures that our\nfindings directly address the mechanisms underlying NACT response and the critical\nclinical challenge of treatment failure in ESCC patients receiving this standard\ntherapeutic regimen.\na. Thus, in Figure 1 clinical response does not reflect paclitaxel-only response and also\nif the transcript is associated with response to treatment, overall survival is not the\nappropriate metric. Instead they should use pathologic response prior to surgery.\nResponse: Thank you for your valuable suggestions. In the previous manuscript, Figure\n1 did not reflect the paclitaxel-only response. In the revised manuscript, we focused on\nthe paclitaxel plus cisplatin regimens of NACT throughout our investigation, which\nnow accurately reflects the paclitaxel and cisplatin NACT response. Additionally, in the\nprevious manuscript, we acknowledged that overall survival was not an appropriate\nmetric. Instead, we analyzed transcript expression associated with pathologic response\nprior to surgery, as shown in Figure 1g, 1j, and Supplementary Figure 7d. Upregulation\nof YTHDF3, INCENP, and CDCA8 contributes to poor NACT response in ESCC.\nb. In Figure 1O- time to death should be displayed as opposed to death alone-also the\nauthors do not mention if the patients received additional treatment after surgery and\nhow such treatment is weighing into their correlations.\nResponse: Thank you for your thoughtful comments and valuable suggestions. We\nhave conducted additional survival analyses of INCENP and CDCA8 expression,\nincorporating time to death in Supplementary Fig. 1k. Our analysis demonstrates that\nhigh expression levels of INCENP, CDCA8, and YTHDF3 in non-responders are\nassociated with shorter survival times (line 144).\nWe agree that patients often receive additional adjuvant treatments after surgery, and\nthat the 5-year survival rate is influenced by multiple factors, while there is no clear\nconsensus in the literature regarding which specific adjuvant therapies significantly\nimprove the 5-year survival rate after NACT. However, it is well established that\nachieving pathological complete response (pCR) after NACT is a strong and reliable\nprognostic indicator. Numerous studies have reported that pCR is associated with\nsignificantly improved 5-year survival rates and better overall prognosis. In line with\nthis, our clinical information analysis data also demonstrated that responders exhibit\nsignificantly longer overall survival (OS) and progression-free survival (PFS)\n(Supplementary Fig. 7b, c). Therefore, the biomarkers we identified for predicting the\npotential for pCR after NACT, and their relationship to patient prognosis. These\nbiomarkers may provide valuable insights into which patients are most likely to benefit\nfrom NACT, helping to personalize treatment strategies and ultimately improve patient\noutcomes. We clarified this concern in manuscript discussion (line 350).\nReference:\n--Meredith KL, Weber JM, Turaga KK, et al. Pathologic response after neoadjuvant\ntherapy is the major determinant of survival in patients with esophageal cancer. Ann\nSurg Oncol 2010;17:1159–1167.\n--E Ancona, A Ruol, S Santi, et al. Only pathologic complete response to neoadjuvant\nchemotherapy improves significantly the long term survival of patients with resectable\nesophageal squamous cell carcinoma: final report of a randomized, controlled trial of\npreoperative chemotherapy versus surgery alone. Cancer 2001;91:2165–2174.\n--Farrukh Hassan Rizvi, Aamir Ali Syed, Shahid Khattak, et al. Complete pathological\nresponse after neoadjuvant treatment in locally advanced esophageal cancer predicts\nlong term survival: a retrospective cohort study. Int J Surg. 2014;12(6):621-5.\n2. The authors claim that YTHDF3 KD or OE modulates the sensitivity of paclitaxel.\nUnfortunately, there are important concerns in the data that do not support this\nstatement. Several important controls are missing, thereby diminishing the\nexperimental rigor of these results\n- Is there any correlation between the basal expression levels of the YTHDF3 with\ntaxane sensitivity/resistance in the KYSE cell panel?\nResponse: Thank you for your value suggestions. To investigate the correlation\nbetween basal expression levels of YTHDF3 and sensitivity to paclitaxel and cisplatin\nin ESCC cell lines, we treated a panel of KYSE cells with these drugs. Results revealed\nthat higher basal expression of YTHDF3 was associated with increased IC50 values,\nindicating reduced drug sensitivity (Supplementary Fig. 3l, m, line192).\n-To determine the role of YTHDF3 in taxane resistance in vitro, they treat different\nESCC cell lines (parental vs. shYTHDF3 (Fig.2A) and vs. Ebselen treated (unknown\nconcentration) (Fig.2C) at different concentrations of paclitaxel. The ranges of\n[paclitaxel] used in these experiments to determine the IC50 are inappropriate. For\nexample, they use a range from 0 to 8nM paclitaxel to estimate an IC50 of 92nM.\nResponse: Thank you for your detailed and helpful suggestions. We refined the\nresearch method by testing a wide range of paclitaxel and cisplatin concentrations to\nevaluate the effects of YTHDF3 knockdown (KD) and Ebselen on drug sensitivity. The\nresults demonstrated that YTHDF3 KD and Ebselen treatment significantly increased\ncellular sensitivity to paclitaxel and cisplatin, reducing their IC50 values (Fig. 3c-f).\n-The colony formation assay shows very modest differential response to paclitaxel\ntreatment in YTHDF3 KD (Fig 2B) and cells treated with Ebselen (unknown\nconcentration) (Fig 2D). The authors also need to test the effect of Ebselen on the cell\nviability of YTHDF3 KD cell lines. If the inhibition is through the same pathway of\nYTHDF3, no impact or small additive effect should be produced by Ebselen treatment\nin YTHDF3 KD cells.\nResponse: Thank you for your thoughtful and constructive comment. In\nSupplementary Figure 3o, we refined research method of plate colony formation assay,\nresults showed that 5μM Ebselen treatment significantly enhanced the sensitivity of\nESCC cells to paclitaxel and cisplatin compared to the control.\nAdditionally, the target specificity of Ebselen was systematically validated by assessing\nthe viability of YTHDF3 KD cells following Ebselen treatment. The results revealed\nno significant impact on the viability of YTHDF3 KD cells from Ebselen treatment\n(Supplementary Fig. 3e, f, line185).\n-In Figure 2E, the IC50 fitting of the experimental data to calculate the IC50 needs to\nbe revised. A more precise fitting needs to be performed, which might generate different\noutcomes. Also, they need to validate the expression levels of shYTHDF3 EO by Western\nblot and their cytoplasmic distribution by immunofluorescence\nResponse: Thank you for your thoughtful observation. we refined research method and\nfound that YTHDF3 OE obviously increased IC50 of paclitaxel and cisplatin in ESCC\ncells and have precise fitting curves (Fig. 3g). Supplementary Fig. 3p validated the\n validated the\nexpression levels of YTHDF3 OE in KYSE450 cells by Western blot. Additionally,\nimmunofluorescence shows that YTHDF3 is distributed in the cytoplasm of cells, as\nshown in the eIF3A and YTHDF3 colocalization experiment (Supplementary Fig. 5f).\n-In Fig 2G, I assumed that the drug concentrations of Ebselen + paclitaxel administered\nto the animals are identical to the ones in a single treatment. That needs to be clarified\nin the manuscript\nResponse: Thank you for your insightful suggestions. We clarify that “the drug\nconcentrations of 20 mg/kg, 5 times/week, Ebselen plus 10 mg/kg/week, paclitaxel or\ncisplatin administered to the animals are identical to the ones in a single treatment in\nmethod (line 563).\n-In Figure 2M, authors need to perform densitometry analysis to quantify if YTHDF3\nKD induces the decrease of protein expression of G2 phase regulators. It is unclear if\nthis is the case in this figure.\nResponse: Thank you for your constructive feedback. We acknowledged that the\ninclusion of appropriate statistics is important to enhance the clarity and robustness of\nthe results. We have presented the data in Supplementary Figure 3a and b, which are\nstatistically validated and clearly interpreted.\n-In Supplementary Fig.2. the authors rescue the knockdown of YTHDF3 in KYSE-30\nand -150 cells by OE of YTHDF3 wt and mutant. I couldn’t find in the text which\nshRNAs were used to KD endogenous proteins without affecting the expression of the\nectopically expressed proteins. More experimental details are needed.\nResponse: Thank you for your valuable feedback. Regarding the rescue experiments in\nSupplementary Fig. 2, we used specific shRNA#1 targeting the 3'UTR of YTHDF3,\nwhich selectively knockdown the endogenous YTHDF3 without affecting the\nectopically expressed YTHDF3 (WT and mutant). Exogenous expression was validated\nby WB through detecting Flag tag (Fig.4a). We have clarified in the figure legend that\nshYTHDF3#1, which targets the 3'UTR of YTHDF3, was used for the rescue assay.\nshRNA#1-Forward oligo:\n5’ CCGGTAAGTCAAAGAAGACGTATTACTCGAGTAATACGTCTTCTTTGACTTATTTTTG\n3’\nshRNA#1-Reversed oligo:\n5’ AATTCAAAAATAAGTCAAAGAAGACGTATTACTCGAGTAATACGTCTTCTTTGACTTA\n3’\n-In Figure 4, to demonstrate that INCENP and CDCA8 OE abolish the effect of\nYTHDF3 KD on ESCC cells in the presence of paclitaxel treatment (fig 4K and fig4L /\n1nM paclitaxel), they need to perform a cell viability assay similar to the ones shown\nin Fig 2a.\nResponse: Thank you for your detailed and helpful suggestions. We performed cell\nviability assay similar to the ones shown in Fig 2a-d and verified that overexpression\nof INCENP and CDCA8 after knocking down YTHDF3 reverse the sensitizing effect\nof paclitaxel and cisplatin drugs on ESCC (Fig. 3o, p).\n-In Figure 5, authors claim that INCENP and CDCA8 proteins were evidently reduced\nin YTHDF3 KD cells (Fig 5A and 5B). They need to provide a better-quality WB that\nproves that together with quantification. The same case is true for the OE of\nYTHDF3 in 5a (right panel). It is not evident that the OE of YTHDF3 increases the\nexpression levels of INCENP and CDCA8. Images in 5B are too small, and they need to\ndescribe how the images were collected and quantified.\nResponse: Thank you for highlighting it. We have carefully addressed your concerns\nregarding the WB data in Figure 5a and b. To improve the quality and clarity of the data,\nwe repeated experiment and with higher-quality WB images along with proper\nquantification of the bands (Supplementary Figure 1e-h). We have also adjusted IF\nimages size in Fig 5a and included a more detailed description of how the images were\ncollected, processed, and quantified to provide a clearer explanation of the experimental\nprocedures in method. Images were captured by Nikon confocal microscope. The\nrelative protein expression of IF images was quantified using ImageJ software.\nFluorescence intensity was measured and normalized by calculating the average\nintensity per cell or per unit area, effectively controlling for variations in cell numbers.\nComparisons between experimental groups were made to assess relative changes in\nprotein expression levels (line 594).\n-In Figure 8: It must be clarified if the retrospective samples from cohort 2 were\nobtained pre- or post-placitaxel treatment. Do the grey bars mean that there is no\nsample information? What is the reason for the number of grey bars in the responder\npatients?\nResponse: Thank you for your value suggestions. The retrospective samples in cohort\n2 were obtained post-paclitaxel plus cisplatin regimes of NACT treatment, we have\nnow explicitly noted this in the revised Figure 7 legend for clarity. The grey bars in\nFigure 7a specifically indicate tumor tissues from responders who achieved a\npathological complete response (TRG0), showing no residual tumor cells upon\nhistological examination, we have clarified it in the revised Figure 7 legend.\nMinor points:\nCited references do not always correspond to the text.\nResponse: Thank you for your observation. We have thoroughly reviewed the\nmanuscript and carefully checked the cited references to ensure that they correspond\ncorrectly to the text.\nLow quality of figure. Figures need to be bigger and more accessible to interpret. There\nare missing units in most of the graph axis, none of the western blots are quantified,\nand confocal images are too small.\nResponse: Thank you for your valuable feedback. We have carefully revised the figures\nto improve their quality. The figures have been resized to ensure they are larger and\neasier to interpret. We have also added the missing units on the graph axes where\nappropriate. Furthermore, we have included quantification for the Western blot results\nto enhance clarity and accuracy (Supplementary Fig. 2-6). The confocal images have\nbeen resized to a larger scale to ensure better visibility. We believe these revisions\nsignificantly improve the presentation and accessibility of the figures. Thank you again\nfor your constructive comments.\nWe believe that the revisions made in response to the reviewer’s comments have\nsignificantly improved the manuscript, enhancing the quality, depth, and novelty of the\nstudy. We respectfully ask the Editor to reconsider our manuscript for publication in\nNature Communications.\nWith best regards,\nSincerely\nZigang Dong(dongzg@zzu.edu.cn)\nZhengzhou·University\nApril 16. 2025"}, {"round": 2, "reviewer_comments": {"reviewer_1": "While authors revised the manuscript substantially, there are still several concerns.\n1, The upregulation of YTHDF3 in cancer and its regulation .of gene expression in an m6A-dependent manner are not novel at all. As both INCENP and CDCA8 are critical regulator of cell division, KD or OE is not surprising to affect the chemosensitivity. Therefore, the mechanistic insights are quite limited to achieve a conceptual advance for the field.\n2, Authors emphasizes on the clinical relevance which support their submission to this journal. However, this part are much weak even non-sense at all. Analysis based on post-surgical samples are completely misleading. How can it PREDICT the response of NACT which are given before the operation? Pre-treatment biopsy tissues, rather than post-surgical samples, should be used.\n3, How dare to use “pa-cancer” in the title while most studies are using ESCC cell lines and ESCC tissues?\n4, What is m6A methylation of INCENP (at alanine 113) and CDCA8 (at alanine 1041) transcripts？", "reviewer_2": "The authors have largely addressed my previous concerns.", "reviewer_3": "The authors have successfully defended my comments, and I do not have any more queries. I recommend acceptance.", "reviewer_4": "The authors have successfully addressed most of the reviewers' comments and have revised the manuscript to improve clarity, flow and readability. We have no further comments. The revised manuscript should be acceptable for publication.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nWhile authors revised the manuscript substantially, there are still several concerns.\n1, The upregulation of YTHDF3 in cancer and its regulation of gene expression in an m6A-\ndependent manner are not novel at all. As both INCENP and CDCA8 are critical regulator of\ncell division, KD or OE is not surprising to affect the chemosensitivity. Therefore, the\nmechanistic insights are quite limited to achieve a conceptual advance for the field.\nResponse: We thank the reviewer for taking the time to assess our manuscript. We\nrespectfully need to highlight the following key points regarding the novelty and\nsignificance of our study:\n1. Unique Contribution of INCENP and CDCA8 in Clinical Significance:\nResistance to neoadjuvant chemotherapy (NACT) poses a major clinical challenge,\nand reliable biomarkers to guide patient selection are urgently needed. Here, our study\nidentifies INCENP and CDCA8 as previously unrecognized modulators of\nchemotherapy sensitivity in ESCC. Functional experiments, followed by in-depth\nmechanistic investigations, reveal their critical roles in mediating the cellular response\nto chemotherapy, thereby establishing a direct functional link between these genes and\nchemotherapy sensitivity. Importantly, this association is further validated in\nindependent cohorts from lung and breast cancers, underscoring the potential of\nINCENP and CDCA8 as predictive biomarkers for chemotherapy response beyond\nESCC.\n2, Novel mechanism\nThis study uncovers a novel mechanism that INCENP and CDCA8 transcripts\nundergo m6A modification, a key epigenetic regulation, and are recognized by\nYTHDF3, which promotes their translation by interacting with eIF3A, driving NACT\nresistance. Crucially, the efficacy of these chemotherapeutic agents is dependent on the\nm6A modification status of INCENP and CDCA8, marking a previously unrecognized\nregulatory layer in chemotherapy response. Inhibition of INCENP and CDCA8 leads to\nthe formation of multipolar spindle, sensitizing cells to NACT in both in vitro and in\nvivo models. These findings highlight how m⁶A epigenetic modification of INCENP\nand CDCA8 transcripts leads to mitotic dysregulation, which collectively contributes to\nNACT response.\n3. Novel Role of YTHDF3 in esophageal squamous cell carcinoma:\nWe need to emphasis that the role of YTHDF3 in ESCC has not been previously\ncharacterized. Our work is the first to reveal its upregulation and mitosis-regulated\nfunction in ESCC, especially in the context of chemotherapy response.\nTaken together, these findings reveal novel mechanisms driving NACT resistance\nand identify reliable predictive biomarkers, highlighting the clinical relevance of\nINCENP and CDCA8 in guiding personalized NACT strategies. This is particularly\nvaluable given the current lack of reliable predictors for chemosensitivity in cancer\npatients. The integration of mitotic gene expression into clinical decision-making\nrepresents a step forward in precision oncology.\n2, Authors emphasizes on the clinical relevance which support their submission to this\njournal. However, this part are much weak even non-sense at all. Analysis based on post-\nsurgical samples are completely misleading. How can it PREDICT the response of NACT which\nare given before the operation? Pre-treatment biopsy tissues, rather than post-surgical\nsamples, should be used.\nResponse: We appreciate the reviewer’s insightful comment. We fully agree that pre-\ntreatment biopsy samples are critical for assessing predictive biomarkers of NAC\nresponse. To address this, we have analyzed the expression levels of INCENP and\nCDCA8 in pre-treatment biopsy tissues from ESCC, lung and breast cancer patients\nundergoing neoadjuvant chemotherapy, the results revealed significantly higher\nexpression levels of INCENP, CDCA8 and YTHDF3 in non-responders compared to\nresponders across all three cancer types. Notably, in ESCC, non-responders with high\nexpression of these genes exhibited markedly shorter progression-free survival (PFS).\nFurthermore, receiver operating characteristic (ROC) curve analyses demonstrated that\nYTHDF3, INCENP and CDCA8 possess significant predictive potential for NACT\nresponse in ESCC, breast, and lung cancers. These findings support the clinical utility\nof INCENP and CDCA8 as predictive biomarkers across cancer types. We have revised\nthe manuscript (line 341) and the corresponding results in Figure. 7 and supplementary\nfigure. 7.\n3, How dare to use “pa-cancer” in the title while most studies are using ESCC cell lines and\nESCC tissues?\nResponse: We thank the reviewer’s thoughtful feedback. Our study indeed investigates\nthe roles of INCENP and CDCA8 as predictive factors for neoadjuvant chemotherapy\nsensitivity, not only in ESCC but also in lung and breast cancer. Hence, the term \"pan-\ncancer\" was used to reflect the broader scope of our findings across different cancer\ntypes. However, we accept your suggestion and revise the title to \"INCENP and CDCA8\npredict neoadjuvant chemotherapy response and outcomes in esophageal squamous\ncell carcinoma\" as you recommended.\n4, What is m6A methylation of INCENP (at alanine 113) and CDCA8 (at alanine 1041)\ntranscripts？\nResponse: We appreciate the reviewer’s detailed and helpful suggestions. Our previous\ndescription may have caused misunderstanding. To clarify, we revised to convey that\n“the 5’UTR (GGACT at position 113) of INCENP and the 3’UTR (GGACT at position\n1041) of CDCA8 undergo m⁶A methylation and are recognized by YTHDF3”. (line30)\nReviewer #2 (Remarks to the Author):\nThe authors have largely addressed my previous concerns.\nResponse: We sincerely thank the reviewer for the positive evaluation and for\nacknowledging our revisions. We truly appreciate the time and effort dedicated to\nreviewing our work.\nReviewer #3 (Remarks to the Author):\nThe authors have successfully defended my comments, and I do not have any more queries.\nI recommend acceptance.\nResponse: We sincerely thank the reviewer for the positive feedback and the\nrecommendation for acceptance. We are truly grateful for the time, constructive\ncomments, and support throughout the review process.\nReviewer #4 (Remarks to the Author):\nThe authors have successfully addressed most of the reviewers' comments and have revised\nthe manuscript to improve clarity, flow and readability. We have no further comments. The\nrevised manuscript should be acceptable for publication.\nResponse: We sincerely thank the reviewer for the positive and encouraging feedback.\nWe truly appreciate the time and effort dedicated to reviewing our work.\nReviewer #6 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts."}, {"round": 3, "reviewer_comments": {"reviewer_1": "In the revision, authors applied biopsy tissues to evaluate the predication value of YTHDF3/CDCA8/INCENP expression. Strangely, how can maximal tumor diameter be evaluated in biopsy tissues? More importantly, both histological types and TNM stages were claimed to predicate the efficacy of NACT. So, how about the ROC analysis of these two parameters? Are they good enough to predicate the response to NACT?"}, "author_response": "Response: We appreciate the reviewer’s detailed and helpful suggestions.\n-- We realize that previous description of maximal tumor diameter may have caused\nsome confusion, and we sincerely apologize for the misunderstanding. The maximal\ntumor diameter was not assessed based on biopsy tissues, but was determined from the\nresected tumor specimens obtained after surgery.\n-- In response to the comment regarding histological types and TNM stages, we clarify\nthat these parameters were analyzed for their correlation with NACT response, but were\nnot used as predictive variables. The ROC analysis was conducted solely based on the\nexpression of YTHDF3/CDCA8/INCENP to assess their potential in predicting\nsensitivity to NACT. This point has been clarified in the revised manuscript (line 342)."}]

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