Targeting SPAK suppresses progression and averts an immune exhaustive microenvironment in hepatocellular carcinoma

Domain: neuroscience
Published: 2026-01-12
Editorial outcome: major_revision

Abstract
Protein kinases contribute to hepatocellular carcinoma (HCC) development and immune evasion, posing major challenges for HCC management. Here we show STE20/SPS1-related proline/alanine-rich kinase (SPAK) as a candidate immune exhaustion–associated gene identified through a pooled screen of protein kinases. By integrating bioinformatic analyses, data from patient cohorts, and functional studies in mouse models and cell lines, we demonstrate that elevated expression of SPAK promotes HCC progression, enhances stemness, drives immune exhaustion, and contributes to resistance to targeted therapies. Mechanistically, SPAK phosphorylates GSK3β at Ser9, thereby inhibiting proteasome-mediated degradation of c-Jun and PD-L1. Additionally, we find that DNMT3B-dependent intragenic methylation of SPAK contributes to its high expression in HCC. Notably, the SPAK inhibitor exhibits potent inhibitory effects and synergizes with PD-1 blockade to enhance antitumor efficacy. In summary, these findings establish SPAK as a driver of oncogenesis and immune exhaustion in HCC and highlight dual inhibition as a potential therapeutic strategy.

Introduction
Liver cancer is the third leading cause of cancer-related mortality, with hepatocellular carcinoma (HCC) accounting for approximately 90% of cases1,2. Over the past decade, immunotherapy has revolutionized HCC treatment, particularly through anti-PD-(L)1 therapies, which have benefited patients with advanced disease3–5. A comprehensive understanding of the antitumor immune response and the interactions between HCC cells and their TIME is critical for biomarker discovery and the development of combination immunotherapies6. Previous studies have classified HCCs into inflamed and non-inflamed subtypes based on immune microenvironment characteristics. Inflamed HCCs are generally responsive to anti-PD-(L)1 monotherapy and combination therapies7–10. In contrast, non-inflamed HCCs can be further categorized into intermediate and immune-exclusion subtypes, with β-catenin signaling being the primary driver of immune exclusion11. Our previous study identified MMP9 as a promising therapeutic target for overcoming immune exclusion in HCC, as MMP9-mediated shedding of slingshot protein phosphatase 1 from CD8⁺ T cells impaired cytotoxic T-cell activation12,13. Inflamed HCCs can be further subdivided into immune-active, immune-exhausted, and immune-like subtypes. Notably, the immune-exhausted subtype is characterized by severe T-cell dysfunction, impaired cytotoxic activity, and poor prognosis, highlighting the urgent need to investigate its underlying mechanisms and therapeutic targets.

Protein kinases (PKs) play a pivotal role in HCC progression and immune evasion. Several receptor tyrosine kinases, including FGFR4, MET, and PTK7, have been shown to promote HCC growth and induce an immunosuppressive TIME by modulating PD-L1 stability or TGF-β signaling14–17. Additionally, inhibition of serine/threonine kinases such as AURKA, PKCα, and PIM2 has been reported to enhance the efficacy of anti-PD-(L)1 therapy in HCC18–20. Despite the presence of 514 eukaryotic PKs, the roles of most PKs in HCC remain unexplored.

In this study, we screen PKs based on high expression levels, association with poor prognosis, and enrichment in the immune-exhausted subtype using HCC databases. We identify STE20/SPS1-Related Proline-Alanine-Rich Protein Kinase (SPAK) as an immune-exhaustion-associated protein and a predictive biomarker of poor prognosis in HCC. Functionally, SPAK promotes tumorigenesis, stemness, and immune exhaustion, rendering high-SPAK-expressing HCCs resistant to sorafenib, lenvatinib, and donafenib. Mechanistically, SPAK inactivates GSK3β, leading to increased stability of PD-L1 and c-Jun. Furthermore, combined targeting of SPAK and PD-(L)1 exhibits a promising therapeutic effect in immune-exhausted HCCs with high SPAK expression.

Results
High SPAK expression is associated with poor prognosis and immune exhaustion in HCC
To identify protein kinases associated with immune exhaustion in HCC, we compiled a list of 514 human protein kinases from The Human Kinome21 and analyzed their expression levels in HCC and adjacent noncancerous tissues using TCGA and ICGC datasets. Differential gene expression analysis identified 47 differentially expressed kinase genes in the TCGA cohort and 18 in the ICGC cohort, with 14 overlapping kinases highly expressed in HCC (Fig. 1a and Supplementary Fig. 1a). We then examined the immune profiles of HCC samples from these cohorts based on Sia’s immune classification of HCC and assessed the expression of these 14 kinases across three immune subtypes7,8. SPAK and PTK7 emerged as differentially expressed genes (Fig. 1a). Given that immune exhaustion is characterized by T cell dysfunction, we performed a correlation analysis between kinase expression and T cell exhaustion scores, finding that SPAK exhibited the strongest correlation in both the TCGA (Pearson’s r=0.40 vs 0.17 for PTK7, respectively) and ICGC (Pearson’s r=0.37 vs 0.26) cohorts (Fig. 1b)22. PTK7 has been reported to regulate immune exhaustion-associated TGF-β or PD-1 signaling17, whereas no studies have reported a role for SPAK in immune evasion, particularly in HCC. Thus, we focused on investigating SPAK’s role in HCC.

We next examined the expression pattern and functional role of SPAK in HCC. Using Sia’s inflamed HCC classification in TCGA-LIHC and ICGC cohorts, we found that SPAK expression was significantly elevated in the immune-exhausted subgroup compared to other immune-associated subgroups (Supplementary Fig. 1b, c). Consistently, mIF staining of patient samples (from Tongji cohort 1) also revealed elevated SPAK expression in tumors classified as immune-exhausted subtype, further validating this subtype-specific enrichment at the protein level (Supplementary Fig. 1d). Analyzing the TCGA Pan Cancer cohort revealed that SPAK was highly expressed in most cancers (Supplementary Fig. 2a). A comprehensive analysis of 92 HCC cohorts showed that 55 cohorts reported significantly increased SPAK expression in HCC tissues (Fig. 1c and Supplementary Fig. 2b). Consistently, in three independent Tongji HCC cohorts (CCGLC-002 study), SPAK was highly expressed in cancer tissues at both mRNA and protein levels (Fig. 1d–f, and Supplementary Fig. 3a). Similar findings were observed in the CPTAC proteomic cohort and TCGA-LIHC matched tumor and non-tumor tissues (Supplementary Fig. 3b, c). Further analysis of liver disease datasets (GSE9843, GSE6764, GSE114564, and GSE89377) demonstrated a gradual increase in SPAK expression from liver cirrhosis to early- and late-stage liver cancer (Supplementary Fig. 3d–g). Additionally, SPAK expression positively correlated with tumor histological grade in human clinical HCC specimens, with high SPAK expression associated with poorly differentiated tumors (Supplementary Fig. 4a, b). SPAK was also highly expressed in TCGA-iCluster1 and Hoshida S2 subtypes, which are characterized by aggressive phenotypes, activation of MAPK, AKT, and Myc signaling, and TP53 mutations23–26 (Supplementary Fig. 4c, d). Survival analysis across multiple HCC cohorts (Tongji cohort 1, TCGA-LIHC, GSE14520, and HCCDB25) indicated that high SPAK expression correlated with poorer overall survival (OS) and disease-free survival (DFS) (Fig. 1g and Supplementary Fig. 5a–c). Multivariate logistic regression confirmed that high SPAK expression was an independent predictor of postoperative OS and DFS (Fig. 1h and Supplementary Fig. 5d).

To determine the primary cell type exhibiting increased SPAK expression in HCC, we examined mRNA and protein levels of SPAK in various human tissues using the Genotype-Tissue Expression tool and ProteomicsDB, revealing relatively low SPAK levels in normal liver tissue (Supplementary Fig. 6a, b). Single-cell RNA sequencing (scRNA-seq) data from the Human Protein Atlas further confirmed low SPAK expression in hepatocytes (Supplementary Fig. 7a). Spatial transcriptomic analysis (HCCDB-HRA000437) identified cancer cells as the primary source of SPAK expression in HCC (Supplementary Fig. 8a). Collectively, these findings suggest that high SPAK expression in HCC is associated with an aggressive phenotype, poor prognosis, and an immune-exhausted tumor microenvironment.

SPAK promotes tumorigenesis, stem cell properties and resistance to multikinase inhibitors of HCC
To investigate the role of SPAK in HCC, we generated mice with hepatocyte-specific SPAK deletion (Spakf/f; Alb-Cre) (Fig. 2a and Supplementary Fig. 9a–c), and established DEN/CCl4-induced HCC models. SPAK knockout in hepatocytes reduced hepatocarcinogenesis, accompanied by decreased expression of HCC markers (Fig. 2b, c and Supplementary Fig. 10a, b). To further investigate this, we generated shNC hep mice and shSpak hep mice, and established hydrodynamic gene delivery-based mouse HCC models by introducing myr-AKT and N-RasV12 or MYC and sgP53, to resemble Hoshida S2 and TCGA-iCluster1 HCC subtypes, and found similar results that SPAK depletion significantly attenuated oncogene-driven hepatocarcinogenesis, accompanied by decreased expression of HCC markers (Fig. 2d–f and Supplementary Fig. 10c–i). Consistently, in vitro CCK-8 and clone formation assays, as well as in vivo subcutaneous and orthotopic xenograft HCC models in nude mice, demonstrated that SPAK enhanced HCC growth (Supplementary Figs. 11a–e, 12a–d, and 13a–d). Furthermore, treatment with the selective SPAK inhibitor ZT-1a, which targets phosphorylation at serine 371 (S371)27, significantly reduced HCC growth (Supplementary Fig. 14a, b). We then generated HCC cells with SPAK S371A mutant and demonstrated that wild-type SPAK promoted orthotopic tumor growth, while the S371A mutant mitigated these effects (Supplementary Fig. 14c, d).

Given that liver cancer stem cells (LCSCs) were enriched in Hoshida S2 and TCGA-iCluster1 subtypes25,26, we explore the role of SPAK in regulating stem cell properties in HCC. Correlation analyses using the HCCDB database revealed a positive association between SPAK and cancer stem cell (CSC) markers, including EpCAM, PROM1 (CD133), THY1 (CD90), SOX9, CD47, ICAM1, and KLF4, and a negative correlation with hepatic lineage genes such as HNF4A and ALB (Supplementary Fig. 15a). Consistently, EpCAM+ HCCs exhibited higher SPAK expression than EpCAM− HCCs (Supplementary Fig. 15b). To validate these findings in a human context, we analyzed a previously published scRNA-seq dataset profiling human liver development28. Cell clusters were categorized into hepatoblast (HB), fetal hepatocyte (FH), and adult hepatocyte (AH) (Fig. 2g). Using CytoTRACE29,30, we observed a progressive decline in developmental potential from HB to FH to AH. SPAK expression was enriched in the less differentiated HB/FH clusters and reduced in the mature AH cluster, and correlated with progenitor-associated but not mature hepatocyte-associated markers (Fig. 2h, i). These results suggest that SPAK is preferentially expressed in less differentiated hepatic cells. Similarly, analysis of previously published single-cell HCC datasets (GSE202642, GSE151530, GSE206325, GSE149614, GSE156625) revealed a subset of malignant hepatocytes with high SPAK expression that exhibited the greatest developmental potential (Supplementary Fig. 15c). ssGSEA analysis of the TCGA-LIHC cohort further confirmed enrichment of stem cell signatures in tumors with high SPAK expression (Fig. 2j). Overexpression of SPAK in HCC cells led to increased expression of CSC-associated markers, including EpCAM, PROM1, and SOX9, a pattern that was also observed in vivo (Supplementary Fig. 15d–g). Sphere formation assays demonstrated that SPAK overexpression enhanced spheroid-forming efficiency, whereas its depletion significantly impaired this ability (Fig. 2k, l). A limiting dilution assay in nude mice revealed that SPAK knockdown markedly reduced tumor-initiating potential and stem cell frequency (Fig. 2m).

LCSCs were reported as characteristics of drug resistance. Among first-line systemic therapies for treating advanced HCC, sorafenib and lenvatinib are approved by the US Food and Drug Administration (FDA) and National Medical Products Administration (NMPA), and donafenib is a deuterated form of sorafenib approved by NMPA. To assess whether SPAK expression affects sensitivity to these multikinase inhibitors, we conducted drug sensitivity assays and found that SPAK overexpression significantly increased resistance to sorafenib, lenvatinib, and donafenib, whereas SPAK silencing restored sensitivity (Supplementary Fig. 15h, i). Analysis of 17 HCC datasets from lenvatinib- or sorafenib-resistant cell lines, xenografts, tissues, and organoids showed that SPAK mRNA levels were significantly elevated in 76% (13/17) resistant groups (Supplementary Fig. 15j). Taken together, these data indicate that SPAK promotes hepatocarcinogenesis, growth, stem cell properties, and resistance to sorafenib, lenvatinib and donafenib in HCC.

SPAK promotes exhaustion and inhibit the cytotoxic effect of cytotoxic T cells in HCC
Building on our previous bioinformatic analyses, which revealed a significant correlation between SPAK expression and immune exhaustion in HCC (Supplementary Fig. 1a, b), we sought to investigate the underlying mechanisms driving this association. Therefore, we established subcutaneous xenograft tumor models using Hepa1-6 cells in both immunodeficient nude mice and immunocompetent C57BL/6 mice. Notably, SPAK overexpression moderately promoted tumor growth in nude mice, whereas this effect was substantially enhanced in immunocompetent C57BL/6 mice (Supplementary Fig. 16a–d), indicating a potential role of the immune system. This finding prompted further investigation into the underlying mechanisms, particularly regarding immune modulation.

To further explore the role of SPAK in the HCC immune microenvironment, we performed sample stratification based on tumor SPAK expression, dividing patients into high- and low-SPAK expression groups, and analyzed publicly available single-cell RNA-sequencing data from previously published studies (Supplementary Fig. 17a, b)31. Four distinct CD8+ T subpopulations were characterized, including proliferating CD8+ T cells, effectors_like CD8+ T cells, exhausted_like CD8+ T cells, and naive/activation CD8+ T cells. Notably, HCC cells with high expression of SPAK showed reduced subpopulation of effectors_like CD8+ T cells and increased the exhausted_like CD8+ T cells (Fig. 3a and Supplementary Fig. 17c). In addition, we examined the correlation between SPAK levels and immune-related signatures in TCGA-LIHC and ICGC-LIRI-JP cohorts22,32,33 and found that HCC with high SPAK expression were enriched by exhausted cytotoxic T cells, and high levels of exhausted immune checkpoints (Supplementary Fig. 17d, e).

We then analyzed tumor tissues from the hydrodynamic-induced HCC model and hepatic orthotopic transplant model by multicolor flow cytometry (Supplementary Fig. 18a). SPAK knockdown resulted in a slight increase in cytotoxic CD8+ T cells abundance, as indicated by granzyme B and interferon-γ levels, whereas exhausted CD8+ T cells, marked by PD-1 and TIM-3, were significantly decreased (Fig. 3b). Conversely, SPAK overexpression led to a reduction in cytotoxic CD8+ T cells and an increase in exhausted CD8+ T cells, while overexpression of a kinase-inactive mutant (S371A) reversed these effects (Fig. 3c). These findings were further corroborated by multiplex immunofluorescence in tumor tissues from the hydrodynamic injection liver cancer model, DEN/CCl4-induced HCC model and hepatic orthotopic transplant model (Fig. 3d–g, and Supplementary Fig. 19a, b). To evaluate SPAK’s effect on T-cell-mediated cytotoxicity, we established a co-culture system of Hepa1-6 cells and CD8+ T cells (Fig. 3h). Compared with control Hepa1-6 cells (Hepa1-6-Control), Hepa1-6 cells overexpressing SPAK (Hepa1-6-SPAK) were significantly more resistant to T-cell mediated killing (Fig. 3i). Moreover, SPAK overexpression promoted CD8⁺ T cell exhaustion and impaired cytotoxic function, as indicated by elevated expression of exhaustion markers (PD-1 and TIM-3) and reduced levels of cytolytic molecules (granzyme B and interferon-γ) in CD8⁺ T cells within the co-culture system (Fig. 3j). Given the high expression of SPAK in hepatic T cells, ex vivo activated mouse CD8⁺ T cells were treated with the SPAK inhibitor ZT-1a, which did not significantly affect their cytotoxicity or exhaustion status (Supplementary Fig. 19c, d). Collectively, these findings demonstrate that SPAK induces T cell exhaustion and impairs the cytotoxic activity of cytotoxic T cells in HCC.

SPAK deactivates GSK3β and stabilizes c-Jun to facilitate HCC progression and resistance to multikinase inhibitors
To explore the underlying mechanism of SPAK in regulating the tumorigenesis and immune exhaustion of HCC, we performed phospho-kinase arrays to determine the downstream signaling cascades. Following SPAK overexpression, we found that among the top five phosphorylated proteins ranked by mean pixel density ratio, GSK3β at Serine 9 (p-GSK3βS9) exhibited the highest phosphorylation level. Additionally, phosphorylated c-Jun at Serine 63 (p-c-JunS63) was elevated, consistent with c-Jun acting as a direct downstream target of GSK3β34 (Fig. 4a, b, Supplementary Fig. 20a, and Supplementary Dataset 1). Consistently, the high SPAK expression group exhibited marked enrichment of the GSK3 and Jun pathways (Fig. 4c). Proteomic analysis of CPTAC-HCC patient samples further revealed a positive correlation between SPAK and c-Jun protein levels (Fig. 4d), and validation analyses of western blotting demonstrated that SPAK overexpression increased the levels of c-Jun, p-c-JunS63, and p-GSK3βS9 with total GSK3β levels unchanged. In contrast, phosphorylation of c-Jun at Ser243 (p-c-JunS243), a site phosphorylated by GSK3β that facilitates c-Jun ubiquitination and degradation34, was significantly reduced upon SPAK overexpression. Treatment with ZT-1a or expression of the S371A-mutant SPAK reversed this effect (Fig. 4e and Supplementary Fig. 20b). Conversely, SPAK knockdown decreased the protein levels of c-Jun, p-c-JunS63, and p-GSK3βS9, while increasing p-c-JunS243 levels (Fig. 4f and Supplementary Fig. 20c). IHC analysis of HCC tissues showed that SPAK knockout or knockdown lowered p-GSK3βS9 and c-Jun expression, while SPAK overexpression increased their levels; this effect was abolished by the S371A mutant (Supplementary Fig. 20d–g). To further confirm the regulatory effects of SPAK in GSK3β and c-Jun signaling, we generated a SPAK knockout HLF cell line using CRISPR‒Cas9 system (Fig. 4g and Supplementary Fig. 20h). Overexpression of SPAK in two independent HLF-sgSPAK clones restored the levels of c-Jun, p-c-JunS63, and p-GSK3βS9, whereas overexpression of the S371A mutant abolished these effects (Fig. 4h and Supplementary Fig. 20i).

Previous studies have shown that c-Jun activation drives HCC initiation, self-renewal, and resistance to sorafenib and lenvatinib35,36. To determine the role of c-Jun in SPAK-mediated HCC progression, we conducted rescue experiments using T-5224, a c-Jun inhibitor37. Western blot analysis confirmed that T-5224 did not reduce total c-Jun protein levels, consistent with its mechanism of action as a transcriptional inhibitor (Supplementary Fig. 21a). Treatment with T-5224 reversed SPAK-induced tumor formation, sphere formation, upregulation of CSC-associated markers, and drug resistance (Fig. 4i, j and Supplementary Fig. 21b, c). Next, we investigated the molecular mechanisms underlying SPAK-mediated c-Jun and p-GSK3βS9 upregulation. Protein synthesis and degradation assays showed that SPAK promoted the stability of c-Jun (Fig. 4k and Supplementary Fig. 22a). Treatment with the proteasome inhibitor MG132 counteracted the reduction in c-Jun levels induced by SPAK knockdown, suggesting that SPAK stabilizes c-Jun via a proteasome-dependent mechanism (Fig. 4l and Supplementary Fig. 22b). Consistently, c-Jun ubiquitination was inhibited by SPAK overexpression and enhanced by SPAK knockdown (Fig. 4m, n). However, when S371A was overexpressed, SPAK-inhibited ubiquitination of c-Jun was abolished (Supplementary Fig. 22c). Previous studies have established that phosphorylation of GSK3β at Ser9 inhibits its kinase activity, reducing c-Jun phosphorylation and preventing its degradation by the ubiquitin ligase FBXW734. Based on these findings, we hypothesized that SPAK upregulates c-Jun by increasing p-GSK3βS9 levels. Treatment with lithium chloride (LiCl), a GSK3β inhibitor, restored SPAK-mediated regulation of c-Jun (Supplementary Fig. 22d). Additionally, when FBXW7 levels were reduced, SPAK was still able to regulate c-Jun (Supplementary Fig. 22e, f). Furthermore, SPAK binding to c-Jun was dependent on SPAK expression levels (Supplementary Fig. 22g–j). Taken together, these results indicate that SPAK stabilizes c-Jun by interrupting GSK3β/FBXW7-mediated ubiquitination and degradation, thereby promoting HCC progression.

SPAK deactivates GSK3β by direct binding and promoting its phosphorylation at Ser9
Given that SPAK is a serine/threonine (S/T) kinase that upregulates GSK3βS9, we speculated that SPAK directly binds to and phosphorylates GSK3β. To test this, we performed coimmunoprecipitation (co-IP) and confocal microscopic analyses, confirming the colocalization and interaction of SPAK and GSK3β in HCC cells (Fig. 5a–c and Supplementary Fig. 23a). Mutated truncation experiments revealed that amino acid regions 347-456 of SPAK interact with amino acid regions 340-434 of GSK3β (Fig. 5d–g). Structural modeling and molecular docking simulations further corroborated this interaction, revealing potential contact sites within these regions that may mediate binding (Fig. 5h). Given that GSK3β is a well-established substrate of AKT1—an upstream kinase that phosphorylates GSK3β at Ser9, thereby inhibiting its activity—we next compared the binding characteristics of SPAK and AKT1 to GSK3β. Interestingly, although AKT1 showed a slightly stronger affinity to GSK3β (KD (M)=5.83E-09), the binding response and stability of SPAK-GSK3β complex were also notably high (KD (M)=6.96E-09), indicating SPAK may serve as a potential functional interactor of GSK3β (Fig. 5i). Phosphorylation IP experiments confirmed that SPAK promotes the S/T phosphorylation of GSK3β (Fig. 5j), and this phosphorylation was independent of AKT1 (Supplementary Fig. 23b–e). Consistently, treatment with the AKT1 inhibitor MK2206 did not alter SPAK-induced phosphorylation of GSK3β at Ser9, further confirming that this modification occurs independently of AKT1 (Supplementary Fig. 23f). Subsequent phosphosite analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) revealed that SPAK overexpression induced phosphorylation at serine 9 of GSK3β (Fig. 5k), and phospho-IP confirmed that SPAK-mediated S/T phosphorylation of GSK3β was abolished in cells overexpressing GSK3β serine 9 mutants (S9A) (Fig. 5l, m), and this finding was further validated by in vitro kinase assays (Fig. 5n–p). Collectively, these data suggest that SPAK directly interacts with and phosphorylates the ninth serine of GSK3β.

SPAK stabilizes PD-L1 to promote immune exhaustion of HCC
GSK3β was reported to regulate the stability of PD-L1 in cancer cells38–40. To explore whether SPAK regulates PD-L1 through GSK3β in HCC, we performed tissue microarray analyses of HCC and found significant positive correlations among SPAK, GSK3βS9, c-Jun and PD-L1 (Supplementary Fig. 24a, b). Notably, HCCs with high SPAK expression exhibited an immune-exhausted microenvironment, characterized by high PD-L1 expression, and low levels of GZMB+CD8+ cells (Supplementary Fig. 24c). Given that intrinsic PD-L1 expression in tumor cells has been reported to play a critical role in driving immune exhaustion in HCC15,41–43, analysis of previously published single-cell RNA-seq datasets further revealed that HCC cells with high CD274 expression displayed a reduced proportion of effector-like CD8+ T cells and an increased proportion of exhausted-like CD8+ T cells (Supplementary Fig. 25a). Consistently, Western blot analysis of HCC cells further confirmed that SPAK upregulated PD-L1 protein levels, an effect that was abolished by overexpression of the S371A mutant or treatment with ZT-1a (Fig. 6a–c and Supplementary Fig. 25b–g). Consistent results were obtained from flow cytometry and immunofluorescence staining of PD-L1 in HCC cells (Fig. 6d–f and Supplementary Fig. 25h). To further investigate the role of PD-L1 in SPAK-mediated immune evasion in HCC, we conducted rescue experiments using PD-L1 antibodies. Blocking PD-L1 reversed SPAK-induced reductions in cytotoxic CD8⁺ T cells, increases in exhausted CD8⁺ T cells, and tumor progression (Fig. 6g–k and Supplementary Fig. 25i). Given the weak correlation between SPAK and CD274 mRNA levels in the HCC databases, we explored post-transcriptional mechanisms of SPAK in the expression of PD-L1, and found that SPAK maintained the stability of PD-L1 in the presence of CHX (Fig. 6l, m and Supplementary Fig. 26a). Moreover, the proteasome inhibitor MG132, but not the lysosomal inhibitor chloroquine (CQ) or pan-caspase inhibitor Q-VD-OPh, reversed the SPAK-induced increase in PD-L1 levels, suggesting that SPAK regulates PD-L1 stability in a proteasome-mediated manner (Fig. 6n and Supplementary Fig. 26b). In line with this, SPAK inhibited PD-L1 ubiquitination, an effect abolished by shRNAs against SPAK, or SPAK-S371A mutant (Fig. 6o, p and Supplementary Fig. 26c), and treatment with the LiCl (GSK3β inhibitor) restored the regulatory effect of SPAK and ZT-1a on PD-L1 protein levels in HCC (Supplementary Fig. 26d, e). Previous studies have shown that β-TrCP is the ubiquitin ligase that mediates the degradation of PD-L1 by GSK3β44 thus, we hypothesized that SPAK stabilizes PD-L1 by interfering with β-TrCP-mediated degradation. Supporting this, SPAK’s regulation of PD-L1 was dependent on β-TrCP expression (Supplementary Fig. 27a, b). Furthermore, SPAK overexpression weakened the interaction between GSK3β and PD-L1, an effect reversed by S371A overexpression (Supplementary Fig. 27c, d). Conversely, SPAK knockdown enhanced the GSK3β–PD-L1 interaction (Supplementary Fig. 27e). In summary, these findings suggest that SPAK stabilizes PD-L1 by interrupting the GSK3β-/β-TrCP-mediated PD-L1 ubiquitination and degradation and thus promotes the formation of immune exhaustive microenvironment in HCC.

Intragenic hypermethylation of SPAK contributed to its high expression in HCC
To investigate the mechanisms underlying high SPAK expression in HCC, we first analyzed the frequency of SPAK alterations in the TCGA-LIHC cohort, and found only three patients with SPAK mutations (V206L, R353I, and G144) and low-level gains (roughly four copies) of SPAK allele were detected in the TCGA-LIHC cohort of 366 patients (Supplementary Fig. 28a, b). Subsequently, we explored the role of epigenetic modifications, particularly DNA methylation, in regulating SPAK expression. Analysis of TCGA-LIHC methylome data revealed 37 CpG sites within the SPAK gene: six in the promoter region, two in the first exon, and 29 in the gene body (Supplementary Fig. 28c). Notably, the methylation levels of CpG sites within the gene body were significantly higher than those in the promoter region (Fig. 7a). Recent studies reported that gene body methylation in cancer cells can promote active transcription45–47. Furthermore, we observed a positive correlation between the overall methylation level of the SPAK gene and its expression (Supplementary Fig. 28d). We also found that SPAK is highly expressed in HCCs with high CpG island methylator phenotype (CIMP-H) subtype, which is characterized by extensive CpG island methylation and poor prognosis, as previously described by our group (Supplementary Fig. 28e)48. When analyzing individual CpG sites, most gene body methylation sites showed a strong positive correlation with SPAK expression, whereas a few promoter region sites exhibited a negative correlation (Fig. 7a). Additionally, the majority of CpG sites in the gene body were associated with poor prognosis in HCC patients (Fig. 7b).

To further determine whether SPAK expression is regulated by its gene body methylation, we treated HCC cell lines (PLC/PRF/5 and HLF) with DNA methyltransferase inhibitor 5-aza-2’-deoxycytidine (5-aza-dC) and found this treatment significantly reduced both mRNA and protein levels of SPAK in HCC cells (Fig. 7c, d). We then investigated specific DNA methyltransferases (DNMTs) involved in this process, and found that Nanaomycin A, a DNMT3B inhibitor, significantly reduced SPAK expression in PLC/PRF/5 and HLF cells, whereas DNMT1 and DNMT3A inhibitors (GSK-3685032 and DNMT3A-IN-1, respectively) had no effect (Fig. 7e–g). Furthermore, DNMT3B knockdown via shRNA transfection also downregulated SPAK expression in HCC cell lines (Fig. 7h, i). We analyzed the co-expression of SPAK and DNMT3B in 92 HCC datasets and found a positive correlation across multiple datasets (Supplementary Fig. 29a), indicating that DNMT3B plays a key role in SPAK gene body methylation. Previous studies have shown that H3K36me3 facilitates DNMT3B binding and promotes de novo DNA methylation within actively transcribed gene bodies in cancer cells45,49. By examining the ChIP-seq profiles of hepatoma cells (HepG2) in the ENCODE, we found that H3K36me3 and DNMT3B are enriched on the SPAK gene body (Fig. 7j). To further confirm this, genome-wide CUT&Tag analysis demonstrated significant overlap of DNMT3B peaks on the SPAK gene body in HLF and PLC/PRF/5 cells (Fig. 7k and Supplementary Fig. 29b). Taken together, these findings suggest that DNMT3B-dependent hypermethylation of the SPAK gene body promotes its expression in HCC.

Combined targeting SPAK and PD-1 showed enhanced efficacy in HCC
To further validate the upregulation of SPAK and its association with the immunotherapy response in HCC, we analyzed SPAK expression levels in a spatial transcriptome dataset. Our results showed that SPAK was highly expressed in HCC cells from responders to anti-PD-1 therapy but weakly expressed in adjacent normal tissues and non-responders (Fig. 8a)31. Similar findings were observed in another dataset of HCC patients who received immunotherapy (Supplementary Fig. 30a). Additionally, in a retrospective analysis of a prospective cohort of patients undergoing anti-PD-1 neoadjuvant therapy (Tongji cohort 4), SPAK expression was significantly higher in samples from responders (Fig. 8b). These results uncovered SPAK level as a potential predictor of response to ICIs in HCC.

Inspired by these observations, we investigated whether improving the efficacy of anti-PD-1 therapy in HCC could be achieved by targeting the immunosuppressive interactions between tumor cells and CD8+ T cells through SPAK inhibition. To this end, we established preclinical HCC mouse models using DEN induction, hydrodynamic injection of myrAKT and N-RasV12, and orthotopic xenograft models with Hepa1-6-luc cells. We then administered anti-PD-1 antibodies, the SPAK inhibitor ZT-1a, or a combination of both. Notably, the combination therapy significantly enhanced and prolonged antitumor responses, as evidenced by gross morphology, luciferase flux, H&E staining, liver weight, and the liver-to-body weight ratio (Fig. 8c–m). Together, our study highlights the critical role of SPAK in HCC progression and identifies a key signaling axis that can be pharmacologically targeted, offering a promising avenue for combination therapy in HCC patients.

Methods
Ethics statement
The research adhered to the ethical standards of the 1975 Declaration of Helsinki and the International Conference on Harmonization’s Good Clinical Practice guidelines. Approval was granted by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (Wuhan, China), under approval No. TJ-IRB20210924. Clinical data were collected from HCC patients who underwent curative hepatectomy at Tongji Hospital. All participants provided written informed consent.

All animal care and research in this study were conducted in accordance with the guidelines for the care and use of experimental animals of the National Institutes of Health and approved by the ethics committee of Tongji Hospital, Huazhong University of Science and Technology (No. TJH-202106010). In accordance with the Laboratory animal—Guideline for ethical review of animal welfare (GB/T35892-2018), the committee requires that tumor burden in animals not exceed 10% of body weight and that average tumor diameter remain below 20 mm. Throughout the study, tumor sizes in all mice adhered to these guidelines. All mice used were male.

Study design
This study aimed to identify protein kinases that could be targeted to impede the progression and immune evasion of HCC. Additionally, it sought to evaluate the potential of targeting the identified protein kinase (SPAK), either alone or in combination with anti-PD-(L)1 antibodies, in preclinical HCC models. To achieve this, we performed transcriptomic and methylomic analyses, as well as clustering using nearest template prediction algorithms and immune-related signatures, applied to large-scale human HCC datasets, including bulk RNA-seq, scRNA-seq, spatial transcriptomics, and microarray data. Multiple models were used to investigate SPAK’s biological role in tumor progression and immune evasion, such as carcinogen-induced liver cancer, Sleeping Beauty transposon-mediated liver cancer, xenograft mouse models, flow cytometry, multiplex immunofluorescence, sphere assays, and in vitro co-culture systems. We also evaluated the therapeutic efficacy of SPAK inhibitors combined with anti-PD-1 antibodies in murine HCC models. Microarray analysis was employed to investigate the pathological relevance of the SPAK/p-GSK3β/c-Jun & PD-L1 signaling axis. To explore the mechanisms underlying SPAK’s function, we utilized molecular and biochemical techniques, including kinase antibody arrays, in vitro protein kinase assays, CUT&Tag, and mass spectrometry. For the animal studies, mice were randomly assigned to treatment groups, with investigators blinded to group assignments. Experimental endpoints were predefined before study initiation.

Patients and specimens
This study utilized a tissue microarray consisting of 119 paired primary HCC tissue samples from patients who underwent curative resection at Tongji Hospital, Huazhong University of Science and Technology, between February 2012 and April 2014 (referred to as the Tongji cohort 1). Cohort 1 comprised 119 pairs of HCC tissues (pairs consisting of tumor tissue and adjacent tissue from the same patient). The cohort included 99 men and 20 women, with 61 patients older than 50 years and 58 patients aged 50 years or younger. Overall survival (OS) was defined as the time from treatment to death from any cause or the last recorded observation, while disease-free survival (DFS) was the period from randomization until disease recurrence or death due to HCC progression. To assess SPAK mRNA expression in HCC tissues, samples were randomly selected from an additional cohort of 116 patients who also underwent curative resection at the same hospital between February 2017 and January 2020 (Tongji cohort 2). Cohort 2 comprised 116 pairs of HCC tissues (pairs consisting of tumor tissue and adjacent tissue from the same patient). The cohort included 102 men and 14 women, with 72 patients older than 50 years and 44 patients aged 50 years or younger. To evaluate SPAK protein levels, another set of specimens was obtained from 144 patients who underwent curative resection during the same period (Tongji cohort 3). Cohort 3 comprised 144 pairs of HCC tissues (pairs consisting of tumor tissue and adjacent tissue from the same patient). The cohort included 120 men and 24 women, with 98 patients older than 50 years and 46 patients aged 50 years or younger. All HCC samples were verified by pathologists at Tongji Hospital. No local or systemic treatments were administered prior to surgery, and no anticancer therapies were given before disease recurrence after surgery. Additionally, a group of 19 HCC patients who received PD-1 monoclonal antibody therapy (tislelizumab) at Tongji Hospital between January 2020 and December 2022 was included (Tongji cohort 4). Cohort 4 comprised 19 HCC tissues. The cohort included 15 men and 4 women, with 14 patients older than 50 years and 5 patients aged 50 years or younger.

Data from all HCC patients across these cohorts were retrospectively analyzed as part of the CCGLC-002 study (Registered at clinicaltrials.gov: NCT05520801), a longitudinal cohort investigation focused on the clinicopathological features, treatment strategies, and prognosis of hepatobiliary cancer patients. This study was led by the Chinese Collaborative Group of Liver Cancer (CCGLC) and the Chinese Chapter of the International Hepato-Pancreato-Biliary Association, and conducted across Tongji Hospital’s main campus and three branches in Wuhan, China.

Statistical analysis
Statistical analyses were performed using GraphPad Prism 9 (GraphPad, CA, USA) and R software (version 4.4.0, R Foundation for Statistical Computing). Continuous variables following a normal distribution were compared using Student’s t test, while the Mann–Whitney U test was applied for non-normally distributed data. Survival analysis was conducted using the Kaplan–Meier method. Pearson’s correlation test was used to assess associations between continuous variables. A P<0.05 was considered statistically significant. Results are presented as mean ± SD.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I've been concentrating on the bioinformatics analyses aspects of this manuscript.\nThe study utilized publicly available data to identify protein kinases SPAK as a potential\nbiomarker for hepatocellular\ncarcinoma (HCC). Additionally, SPAK’s functional role and expression profile in HCC were explored.\nWhile this project represents an excellent example of using publicly available data, the analyses methods section is not\nreproducible due to incompleteness. Below are unclear parts and suggestions for improvement:\n1. Analysis Workflow Details:\n• Provide a thorough and logical description of the analysis steps used for each data type in the methods section.\n• Specify the number of datasets obtained (cases/cohorts) from each database (e.g., clarify the number of ICGC cohorts\nobtained, and detail the number of RNA-seq and microarray datasets retrieved).\n• Clearly define the type of data obtained from the database (e.g., normalized matrix, raw data, single-cell object, or raw cell\n× gene matrix), and include the input dimensions\n• Provide details of database versions, access dates, and proper citations.\n• Specify the versions of all packages used, and ensure they are properly cited\n2. Single-Cell Data in the methods section:\n• Indicate what type of data was obtained from the database, from which database and provide accession number and\ncitation\n• Mention the number of cells in the matrix before filtering.\n• Specify the Seurat package version, as only the R version is currently specified.\n• Clarify whether the Harmony algorithm was implemented or used as a tool.\n• For cell-type listings, include genes highly expressed in each cluster and cite supporting literature for these annotations.\n• Address discrepancies in figure axes where UMAP is described in the methods section, but certain figures are labelled as\ntSNE.\n3. Ambiguities in Manuscript:\n• Line 181: Clearly specify whether this analysis is part of the current study or sourced from another study.\n• Line 189: Confirm whether this data comes from a previous study.\n4. Data Source Clarity:\n• The manuscript wording needs adjustment to ensure consistency. At times, it appears the authors generated and analyzed\ndata; other times, it seems to have been sourced from a public database. Make it clear what was sequenced by you and\nwhat was obtained from the data base\n5. Mass Spectrometry Details:\n• Specify the type of mass spectrometry used (e.g., LC-MS/MS).\n• Proper reference SpecAlly Life Technology Co., Ltd\n• Type of data obtained after SpecAlly\nSummary:\n• Provide proper documentation of the analysis workflow in the methods section for each dataset\n• List all software tools and algorithms used with their respective versions, include access dates if version is not available\nand citation\n• Ensure all data sources are explicitly described.\n• Revise the text for consistency to avoid ambiguity about data generation versus retrieval.", "reviewer_2": "The authors have screened human hepatocellular carcinoma (HCC) specimens for over expressed kinase and identified\nSPAK as a protein kinase whose expression is significantly unregulated in HCC. Using mouse models they show that\nSPAK over expression enhance s HCC growth and that SPAK knockdown or ablation slows down HCC growth in two\ndistinct models: DEN+CCl4 and Myr-AKT + NRAS + sh-P53. They then proceed to show that SPAK inhibits HCC killing by\ntumoricidal CD8 positive T cells. Mechanistically they show that SPAK can phosphorylate GSK3b at Ser9 which inhibits its\nbinding to substrate in this case c-JUN and PD-L1.\nMajor Critiques: Although SPAK may be unregulated in human HCC and its ablation may slow down HCC development in\nmice, the biochemical explanation for SPAK's pro-tumorigenic activity is highly problematic. The authors use over\nexpression to show that SPAC can phosphorylates GSK3b at Ser9, a well established AKT site. AKT is overactive in the\nmajority of HCCs and thus the authors need to show by knockdown experiments that SPAK is more critical for GSK3 S9\nphosphorylation than AKT, which they dont do. Moreover, one of their HCC induction models is based on over expression of\nactive Myr-AKT. If the critical target of SPAC is GSK3b, they need to show that SPAK ablation blocks GSK3b\nphosphorylation in AKT over expressing hepatocytes. They also claim that GSK3 phosphorylates c-JUN at Ser63, which is a\nwell established JNK site involved in stimulation of c-JUN transcriptional activity. GSK3 also phosphorylates c-JUN but\nthese phosphoacceptor sites are located in the C terminus and do not include S63, as claimed by the authors. These major\nproblems make me have little confidence in many of the results reported in this manuscript. Another major problem is the\nassignment of PD-L1 as another target for SPAK regulated GSK3b. While GSK3b may phosphorylate PD-L1, the authors\nprovide no evidence that PD-L1 expression by HCC cells is critical and that HCC cells are the major site of PD-L1\nexpression. Many previous reports have shown that PD-L1 is mainly expressed by immune cells in the liver. If PD-L1 on\nhepatocytes/HCC is important the authors need to knock it out in these cells and show that in the absence of PD-L1, SPAC\nno longer affects HCC growth.\nSpecific comments: 1. The relative affinity of SPAK, AKT,PKA and S6K to GSK3b needs to be measured. 2.The major\nH3K36me3 methyltransferase is SETD2 whose knockout results in spontaneous HCC. What happens to SPAC expression\nin this model? 3.According to single cell RNAseq studies, T and NK cells express high amounts of SPAK. How do these\ncells respond to systemic SPAC inhibition? 4. Previous studies have explored the role of the SPAC-NKCC axis in HCC. The\nauthors need to explain why they ignored these previous studies.5. The authors need to show changes in GSK3b\nphosphorylation in SPAC overexpressing HCC and demonstrate that inhibition or ablation of AKT has no effect on S9\nphosphorylation in such cases. 6.The authors attributed the role of SPAK in HCC progression to increased c-Jun and PD-L1\nin HCC cells. Discussion lines 381-385: “Based on these findings, we hypothesize that targeting PD-L1 alone may be\ninsufficient to counteract SPAK-driven oncogenesis in high-SPAK-expressing HCC”. However, in Figure 6I, targeting PD-L1\nalone seems sufficient 7. If genes body methylation controls SPAC expression in HCC, the authors need to perform\nmethylation IP or sequencing experiments to establish this point more directly. 8.In vitro gene knockout mediated by Cas9\nrequires at least two independent clones to avoid off-target effects (Figure 4G). 9. The correlation coefficient beween SPAK\nand immune exhaustion doesnot seem very high r<0.4 10.The effect of SPAK ablation on HCC development is poorly\ndocumented. No effect on tumor multiplicity, size and volume is shown. The effect of SPAK ko on HCC markers not shown\neither.11. The link between SPAK expression and immunosuppression is poorly defined. The studies in which SPAK\nsilencing enhace killing by T cells in vitro are not very well controlled. Is the effect unique to CD8 or does SPAK also have\nan effect on CD4 T cells and NK cells? Does it affect expression of exhaustion markers in the vitro system?", "reviewer_3": "1.Line69-70 “non- inflamed HCCs can be further categorized into intermediate and immune-exclusion subtypes.”\nHowever in line 73-75: “Inflamed HCCs can be further subdivided into immune-active, immune-exhausted. ”\nThese two statements are contradictory. Does immune exhaustion belong to non-inflamed HCCs or inflamed HCCs?\n2.Line 110-112: “we found that SPAK expression was significantly elevated in the immune-exhausted subgroup compared\nto other immune- associated subgroups.”\nPlease validate these findings using immunohistochemical (IHC) methods. Please provide the characteristic expression\nprofiles of different subtypes of human HCC and the expression patterns of SPARK in various HCC subtypes. It is\nrecommended to capture images from consecutive sections of the same regions at 40x, 100x, and 200x magnifications.\n3.Line 123-125:“SPAK was also highly expressed in TCGA-iCluster1 and Hoshida S2 subtypes, which are characterized by\naggressive phenotypes, activation of MAPK, AKT, and Myc signaling, and TP53 mutations”.\nAs for TCGA-iCluster1 and Hoshida S2, how were these two subtypes determined? What are their specific biological\nmarkers, and why are they considered aggressive subtypes?\n4.Line 144-147 ” We then generate shNChep mice and shSPAK hep mice, and established hydrodynamic gene delivery\nbased mouse HCC models by introducing myr-AKT and N-RasV12 or MYC and sgP53, to resembling Hoshida S2 and TCGA-\niCluster 1 HCC subtypes”\nPlease validate the successful establishment of animal models of Hoshida S2 and TCGA iCluster 1 HCC subtypes through\nimmunohistochemistry (IHC), Western blot (WB), and PCR experiments.\n5.Line 156-157 “Given that liver cancer stem cells (LCSCs) were enriched in Hoshida S2 and TCGA-iCluster 1 subtypes.”\nTo demonstrate that animal models of Hoshida S2 and TCGA iCluster 1 HCC subtypes exhibit stem cell-like features using\nIHC, WB, and PCR experiments, follow this experimental framework.\n6.Line 163-164 “Overexpression of SPAK in HCC cells upregulated CSC-associated markers, including EpCAM, PROM1,\nand SOX9 (Fig. 2K).\nPlease validate that overexpression of SPAK in HCC cells upregulated CSC-associated markers through animal\nexperiments.\n7.Line 165-168 “Sphere formation assays demonstrated that SPAK overexpression enhanced spheroid-forming efficiency,\nwhereas its depletion significantly impaired this ability (Fig. 2, L and M). A limiting dilution assay in nude mice revealed that\nSPAK knockdown markedly reduced tumor-initiating potential and stem cell frequency (Fig. 2N)”\nThese two experiments do not sufficiently demonstrate that SPARK overexpression is associated with stem cell\ncharacteristics\n8.Line 183-187 “Notably, SPAK overexpression moderately enhanced tumor growth in nude mice, while this effect was\nmarkedly amplified in immunocompetent C57BL/6 mice (fig. S16, A to D).”\nPlease provide immunohistochemistry (IHC), Western blot (WB) and PCR data and images demonstrating the relationship\nof the expression of SPARK and immune exhaustion-related factors. The IHC images should illustrate SPARK expression in\nboth cancer cells and the immune microenvironment\n9.Line 197-206 These findings were further corroborated by multiplex immunohistochemistry in tumor tissues from the\nhydrodynamic injection liver cancer model, DEN/CCl4-induced HCC model and hepatic orthotopic transplant model (Fig. 3,\nF to I, and fig. S19, A and B).\nPlease supplement with immunohistochemical (IHC) double-staining for CD8+GZMB and CD8+PD-1/TIM3. Only co-\nexpression staining (i.e. markers localized to the same cells) would be conclusive. Conventional immunofluorescence (IF)\nstaining, which often shows separate expression patterns, is insufficient to validate these findings.\n10.Line 206-211” Collectively, these findings demonstrate that SPAK induces T cell exhaustion and impairs the cytotoxic\nactivity of cytotoxic T cells in HCC \"\nThis experiment demonstrates T lymphocyte-mediated killing of cancer cells but does not establish that SPARK directly\ncauses T cell exhaustion.\n11.Line 234-235 “T-5224 treatment reversed SPAK-induced tumor formation, sphere formation, upregulation of CSC-\nassociated markers, and drug resistance”\nPlease supplement the Western blot (WB) data for SPARK + T5224, including controls for SPARK and c-Jun.\n12.Line 269-272\nPlease provide IHC images of S24A at 100x and 200x magnification, as well as HE-stained images. Additionally, provide\nthe expression levels and positive rates of four specific markers in both HCC cancer cells and tumor microenvironment-\ninfiltrating T cells, include an analysis of correlations between these markers.\n13.Line 276-281\nPlease provide immunohistochemical (IHC) staining images for CD8, PD-L1, and SPARK at magnifications of 40×, 100×,\nand 200×.\n14.Line 296-298 “In summary, these findings suggest that SPAK stabilizes PD-L1 by degradation and thus promotes the\nformation of immune exhaustive microenvironment in HCC”\nThis conclusion is overly speculative. Experiments analyzing the tumor microenvironment should be needed to strengthen\nthe evidence.\nimmune exhaustion. The authors comprehensively analyze multiple functional roles of SPARK in HCC. SPAK promotes\nhepatocarcinogenesis, enhances stem cell properties, drives immune exhaustion, and confers resistance to lenvatinib,\nsorafenib, and donafenib. Mechanistically, SPAK directly interacts with and phosphorylates GSK3β at Ser9, thereby\ninhibiting GSK3β-dependent, proteasome-mediated degradation of c-Jun and PD-L1. Additionally, it also explored the\nintragenic methylation of SPAK.\nOverall, while the article provides a comprehensive investigation into the functional roles of SPARK in HCC, this broad\nscope dilutes the clarity of its central theme and reveals insufficient mechanistic depth in exploring the underlying pathways."}, "author_response": "Dear Reviewers,\nWe sincerely thank the Reviewers for their valuable feedback for our manuscript\nentitled \"Targeting SPAK suppresses progression and averts an immune exhaustive\nmicroenvironment in hepatocellular carcinoma\" (Manuscript ID: NCOMMS-25-\n18337-T). In response, we have carefully revised the manuscript to improve its clarity\nand enhance readability.\nThe reviewers' comments are listed in order below, with specific issues numbered.\nOur responses are presented in blue font, and changes to the manuscript are highlighted\nin orange font. We hope that the revisions satisfactorily address the comments and\nconcerns of the reviewers. Our point-by-point responses are presented below.\nDetailed point-by-point responses to comments from reviewers\nReviewer #1\nI've been concentrating on the bioinformatics analyses aspects of this manuscript. The\nstudy utilized publicly available data to identify protein kinases SPAK as a potential\nbiomarker for hepatocellular carcinoma (HCC). Additionally, SPAK’s functional role\nand expression profile in HCC were explored. While this project represents an excellent\nexample of using publicly available data, the analyses methods section is not\nreproducible due to incompleteness. Below are unclear parts and suggestions for\nimprovement.\nMajor comments\n1. Analysis Workflow Details:\n• Provide a thorough and logical description of the analysis steps used for each data\ntype in the methods section.\n• Specify the number of datasets obtained (cases/cohorts) from each database (e.g.,\nclarify the number of ICGC cohorts obtained, and detail the number of RNA-seq and\nmicroarray datasets retrieved).\n• Clearly define the type of data obtained from the database (e.g., normalized matrix,\nraw data, single-cell object, or raw cell × gene matrix), and include the input dimensions.\n• Provide details of database versions, access dates, and proper citations.\n• Specify the versions of all packages used, and ensure they are properly cited.\nResponse: We thank the reviewer for emphasizing the importance of a detailed and\nreproducible description of our analysis workflow. In response, we have thoroughly\nrevised the methods section to enhance clarity and transparency. We now provide a\ncomprehensive description of the analytic steps used for each data type, including\n‘Identification of candidate protein kinases’, ‘scRNA-seq data processing’, ‘Cell-\ntype annotation’, ‘Sample stratification based on SPAK expression’, ‘Spatial\ntranscriptomics data analyses’, ‘Cell stemness inference’, ‘Stemness Signature\nScoring and Correlation Analysis’, ‘Gene Set Enrichment Analysis’, ‘Cleavage\nUnder Targets and Tagmentation (CUT&Tag) and data analyses’, ‘DNA\nMethylation Data Processing and Analysis’, ‘Surface Plasmon Resonance (SPR)’,\n‘Surface Plasmon Resonance (SPR)’, ‘Gene Set Enrichment Analysis’, ‘DNA\nMethylation Data Processing and Analysis’, ‘Molecular docking’, and\n‘Phosphosite liquid chromatography-tandem mass spectrometry (LC-MS/MS)’.\nThese revisions can be found in the revised Supplementary Methods section.\nSpecifically, we have clarified the number and detail of datasets obtained from each\ndatabase. For each dataset, we now specify the data type, as well as the sample size\nused in the analysis. These revisions can be found in the revised Supplementary Tables\nsection (Table S2).\nIn addition, we have included the database version, data access dates, and relevant\nreferences for each resource. All computational analyses were conducted using R and\nassociated packages, the names and versions of which have now been clearly stated and\nappropriately cited. These revisions can be found in the revised Supplementary Tables\nsection (Table S7).\n2. Single-Cell Data in the methods section:\n• Indicate what type of data was obtained from the database, from which database and\nprovide accession number and citation\n• Mention the number of cells in the matrix before filtering.\n• Specify the Seurat package version, as only the R version is currently specified.\n• Clarify whether the Harmony algorithm was implemented or used as a tool.\n• For cell-type listings, include genes highly expressed in each cluster and cite\nsupporting literature for these annotations.\n• Address discrepancies in figure axes where UMAP is described in the methods section,\nbut certain figures are labelled as tSNE.\nResponse: We appreciate the reviewer’s attention to the details of the single-cell RNA-\nseq analysis. In response to these valuable suggestions, we have revised the methods\nsection to provide a more comprehensive and transparent description. We now clearly\nindicate that the single-cell RNA-seq dataset was obtained from the Mendeley data\n(skrx2fz79n/1), and have included the accession number and proper citation. A total of\n38,429 cells were included prior to quality control filtering. We have specified that the\nanalysis was performed using the Seurat package (v4.4.0) in R version 4.3.3, and this\ninformation has been added accordingly. We further clarified that the Harmony was\nused as a tool in our study via the RunHarmony function, not implemented by us from\nscratch. For cell-type annotations, we listed representative marker genes that were\nhighly expressed in each cluster and provide references to the literature that support\nthese annotations. Lastly, we acknowledge that there was an error in the Methods\nsection, where UMAP was incorrectly stated as the dimensionality reduction method.\nIn fact, tSNE was used throughout the analysis. We have carefully revised the\nmanuscript to correct this discrepancy—the Methods section now correctly states tSNE,\nand all relevant figure legends and axis labels have been double-checked to ensure\nconsistency. These revisions can be found in the revised Supplementary Methods\nsection (‘scRNA-seq data processing’, ‘Cell-type annotation’, and ‘Sample\nstratification based on SPAK expression’).\n3. Ambiguities in Manuscript:\n• Line 181: Clearly specify whether this analysis is part of the current study or sourced\nfrom another study.\n• Line 189: Confirm whether this data comes from a previous study.\nResponse: We thank the reviewer for pointing out the ambiguity in the manuscript. To\nclarify, the analysis described in Line 181 is part of the current study and corresponds\nto our transcriptomic analysis results presented in Figure 1A and 1B. This analysis was\nconducted using publicly available transcriptomic datasets from TCGA-LIHC and\nICGC-LIRI cohorts, through which we identified a significant association between\nSPAK expression and immune exhaustion in HCC. Regarding Line 189, we confirm\nthat the single-cell RNA-sequencing data analyzed in this section were obtained from\npreviously published studies and are publicly available in the Mendeley Data\n(skrx2fz79n/1). We now clearly distinguish between datasets generated by us and those\nobtained from public repositories. Specifically, detailed information on previously\npublished datasets has been included in the Supplementary Tables, while the accession\nnumbers for the datasets generated in this study are provided in the Data Availability\nsection.\n4. Data Source Clarity:\n• The manuscript wording needs adjustment to ensure consistency. At times, it appears\nthe authors generated and analyzed data; other times, it seems to have been sourced\nfrom a public database. Make it clear what was sequenced by you and what was\nobtained from the data base.\nResponse: We thank the reviewer for highlighting this important point. We have\ncarefully reviewed the entire manuscript and revised the wording to ensure clarity and\nconsistency regarding data sources. We now clearly distinguish between datasets\ngenerated by us and those obtained from public repositories. Specifically, detailed\ninformation on previously published datasets has been included in the Supplementary\nTables (Table S2), while the accession numbers for the datasets generated in this study\nare provided in the Data Availability section.\n5. Mass Spectrometry Details:\n• Specify the type of mass spectrometry used (e.g., LC-MS/MS).\n• Proper reference SpecAlly Life Technology Co., Ltd.\n• Type of data obtained after SpecAlly.\nResponse: We appreciate the reviewer’s valuable suggestion. As requested, we have\nclarified the details related to the mass spectrometry analysis in the revised manuscript.\nSpecifically, LC-MS/MS was performed using an Orbitrap Exploris 480 mass\nspectrometer coupled with an EASY-nLC 1200 system (Thermo). The analysis was\nconducted by SpecAlly Life Technology Co., Ltd. (Wuhan, China), and the data were\nprovided in raw file format. Raw data files obtained from SpecAlly were deposited in\ntheProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via\nthe iProX partner repository with the dataset identifier PXD062063. This information\nhas been added to the Supplementary Methods section (‘Phosphosite liquid\nchromatography-tandem mass spectrometry (LC-MS/MS)’) and Data Availability\nsection.\n6. Summary:\n• Provide proper documentation of the analysis workflow in the methods section for\neach dataset\n• List all software tools and algorithms used with their respective versions, include\naccess dates if version is not available and citation\n• Ensure all data sources are explicitly described.\n• Revise the text for consistency to avoid ambiguity about data generation versus\nretrieval.\nResponse: We thank the reviewer for this valuable summary and constructive\nsuggestions. In the revised manuscript, we have provided a more detailed description\nof the analytical workflows for each dataset in the Methods section. In addition, we\nhave compiled a comprehensive list of all software tools and R packages used, along\nwith their versions and relevant references, which is now included as a supplementary\ntable (Table S7). All external datasets used in this study have been clearly indicated,\nproperly cited, and summarized in an additional supplementary table for clarity (Table\nS2). We have also revised relevant parts of the manuscript to eliminate any ambiguity\nbetween data generated in the current study and data retrieved from public databases.\nThese improvements have helped enhance the transparency and reproducibility of our\nstudy.\nReviewer #2\nMajor comments:\n1. Although SPAK may be unregulated in human HCC and its ablation may slow down\nHCC development in mice, the biochemical explanation for SPAK's pro-tumorigenic\nactivity is highly problematic. The authors use over expression to show that SPAC can\nphosphorylates GSK3b at Ser9, a well established AKT site. AKT is overactive in the\nmajority of HCCs and thus the authors need to show by knockdown experiments that\nSPAK is more critical for GSK3 S9 phosphorylation than AKT, which they dont do.\nMoreover, one of their HCC induction models is based on over expression of active\nMyr-AKT. If the critical target of SPAC is GSK3b, they need to show that SPAK\nablation blocks GSK3b phosphorylation in AKT over expressing hepatocytes. They\nalso claim that GSK3 phosphorylates c-JUN at Ser63, which is a well established JNK\nsite involved in stimulation of c-JUN transcriptional activity. GSK3 also\nphosphorylates c-JUN but these phosphoacceptor sites are located in the C terminus\nand do not include S63, as claimed by the authors. These major problems make me have\nlittle confidence in many of the results reported in this manuscript. Another major\nproblem is the assignment of PD-L1 as another target for SPAK regulated GSK3b.\nWhile GSK3b may phosphorylate PD-L1, the authors provide no evidence that PD-L1\nexpression by HCC cells is critical and that HCC cells are the major site of PD-L1\nexpression. Many previous reports have shown that PD-L1 is mainly expressed by\nimmune cells in the liver. If PD-L1 on hepatocytes/HCC is important the authors need\nto knock it out in these cells and show that in the absence of PD-L1, SPAC\nno longer affects HCC growth.\nResponse: We thank the reviewer for these thoughtful and constructive comments. We\nagree that Ser9 is a canonical AKT phosphorylation site on GSK3β. Our study aimed\nto investigate whether SPAK a modulator of GSK3β activity independent of AKT in\nHCC.\nTo address this, we analyzed GSK3β Ser9 phosphorylation across multiple HCC\nmodels, including AKT driven HCC model (AKT + N-RasV12 hydrodynamic model),\nand AKT independent HCC models including MYC + sgTp53 hydrodynamic model,\nDEN + CCl₄ induced HCC model, and an orthotopic implantation model using Hepa1-\n6 cells. IHC analysis of HCC tissues showed that SPAK knockout or knockdown\nreduced p-GSK3βS9 and c-Jun expression, while SPAK overexpression increased their\nlevels, this effect was abolished by the S371A mutant (fig. S20, D to G). In addition,\nknockdown of SPAK in HLF and HCCLM3 cells led to a marked reduction in GSK3β\nSer9 phosphorylation, supporting a regulatory role of SPAK in this context (Fig. 4F\nand fig. S20C).\nfig. S20. (D-G) Representative immunostaining (scale bars, 100 μm) and IHC score of\np-GSK3βS9 and c-Jun in indicated HCC mice model (n = 6).\nFig. 4F fig. S20C\nFig. 4F and fig. S20C Western blot analysis of the phosphorylation level of GKS3β\nand c-Jun in indicated HCC cells.\nFor the regulation of c-Jun by SPAK, we found that SPAK overexpression increased\ntotal c-Jun protein levels, along with increased phosphorylation at Ser63. This result\nmay lead to misunderstanding that c-Jun Ser63 is directly phosphorylated by GSK3β.\nWe have revised related description in the part of Results in the revised manuscript.\nSince GSK3β is known to phosphorylate c-Jun at Ser243 to promote its degradation,\nin the revised manuscript we examined SPAK’s effect on the phosphorylation of c-Jun at\nSer243, and found that SPAK overexpression reduced Ser243 phosphorylation of c-Jun\nas assessed by immunoblotting. In addition, treatment with ZT-1a or overexpression of\nthe S371A-mutant SPAK reversed these effects (Fig. 4E and fig. S20B). Conversely,\nSPAK knockdown reduced the protein levels of c-Jun, p-c-JunS63, and p-GSK3βS9,\nwhile increasing p-c-JunS243 levels (Fig. 4F and fig. S20C).\nFig. 4E and fig. S20B Western blot analysis of the phosphorylation level of GKS3β\nand c-Jun in indicated HCC cells.\nFig. 4F and fig. S20C Western blot analysis of the phosphorylation level of GKS3β\nand c-Jun in indicated HCC cells.\nWe appreciate the reviewer’s comment and agree that PD-L1 is expressed by multiple\ncell types within the liver, including both tumor and immune cells. However, increasing\nevidence also suggests the role of tumor cell–intrinsic PD-L1 in modulating\nproliferation and immune evasion, and thus influencing tumor progression in HCC and\nother cancers1-7. In support of this, our analysis of previously published single-cell\nRNA-seq datasets revealed that, compared with tumor cells with low PD-L1 expression,\nthose with high PD-L1 expression were associated with a higher proportion of\nexhausted CD8+ T cells and a lower proportion of effector CD8+ T cells, further\nindicating a close relationship between tumor cell–intrinsic PD-L1 and an immune-\nexhausted microenvironment (fig. S25A). We have added the relevant citations and\nsupplementary results to the revised manuscript. Our findings add to this body of\nknowledge by suggesting that SPAK may regulate PD-L1 at the post-translational level\nvia GSK3β, thereby providing a mechanistic link between SPAK activity and immune\nmodulation.\nfig. S25A The distribution of CD8+ T clusters in patients with high or low CD274\nexpression (skrx2fz79n/1, Mendeley Data).\nReferences\n[1] Wu M, Xia X, Hu J, Fowlkes NW, Li S. WSX1 act as a tumor suppressor in\nhepatocellular carcinoma by downregulating neoplastic PD-L1 expression. Nat\nCommun. 2021. 12(1): 3500.\n[2] Zhang Y, Wang M, Ye L, et al. HKDC1 promotes tumor immune evasion in\nhepatocellular carcinoma by coupling cytoskeleton to STAT1 activation and PD-L1\nexpression. Nat Commun. 2024. 15(1): 1314.\n[3] Zhang J, Bu X, Wang H, et al. Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin\n3-SPOP to control cancer immune surveillance. Nature. 2018. 553(7686): 91-95.\n[4] Xie M, Lin Z, Ji X, et al. FGF19/FGFR4-mediated elevation of ETV4 facilitates\nhepatocellular carcinoma metastasis by upregulating PD-L1 and CCL2. J Hepatol. 2023.\n79(1): 109-125.\n[5] Yi C, Chen L, Lin Z, et al. Lenvatinib Targets FGF Receptor 4 to Enhance\nAntitumor Immune Response of Anti-Programmed Cell Death-1 in HCC. Hepatology.\n2021. 74(5): 2544-2560.\n[6] Yu W, Hua Y, Qiu H, et al. PD-L1 promotes tumor growth and progression by\nactivating WIP and β-catenin signaling pathways and predicts poor prognosis in lung\ncancer. Cell Death Dis. 2020. 11(7): 506.\n[7] Dong P, Xiong Y, Yue J, Hanley S, Watari H. Tumor-Intrinsic PD-L1 Signaling in\nCancer Initiation, Development and Treatment: Beyond Immune Evasion. Front Oncol.\n2018. 8: 386.\nSpecific comments:\n1. The relative affinity of SPAK, AKT, PKA and S6K to GSK3b needs to be measured.\nResponse: We appreciate the reviewer’s insightful comment. Among the candidate\nkinases, AKT1 is the most extensively studied upstream kinase of GSK3β, with\nnumerous studies documenting its direct phosphorylation of GSK3β at Ser9 as a key\nregulatory mechanism across various physiological and pathological contexts. Given\nits well-established role and the emerging implication of SPAK in modulating GSK3β\nactivity, we performed surface plasmon resonance (SPR) assays to evaluate and\ncompare the relative binding affinities of AKT1 and SPAK to GSK3β. Specifically,\npurified AKT1 and SPAK proteins were individually tested for binding to purified\nGSK3β protein. The results revealed that both AKT1 and SPAK directly interact with\nGSK3β, with dissociation constants (K_D) of 5.83E-09M and 6.96E-09M, respectively\n(Fig. 5I). As expected, AKT1 exhibited a stronger binding affinity to GSK3β, consistent\nwith its role as a canonical upstream regulator. Notably, however, SPAK also showed a\nrelatively strong affinity toward GSK3β, suggesting that it may participate in GSK3β\nregulation through direct interaction. In addition, we re-performed molecular docking\nanalyses to provide updated structural insights into the binding interfaces between\nGSK3β and SPAK (Fig. 5H). These new experimental results have been added to the\nrevised manuscript, and the relevant methods and reagents can be found in the revised\nSupplementary Methods section (‘Surface Plasmon Resonance (SPR)’ and\n‘Molecular docking’).\nFig. 5I Surface plasmon resonance assays to evaluate and compare the relative binding\naffinities of AKT1 and SPAK to GSK3β.\nFig. 5H The crucial amino acids for the interaction between SPAK and GKS3β were\nspeculated using three-dimensional structures.\n2.The major H3K36me3 methyltransferase is SETD2 whose knockout results in\nspontaneous HCC. What happens to SPAC expression in this model?\nResponse: We thank the reviewer for this insightful question. A previous study reported\nthat knockout tumor suppressor SETD2 results in spontaneous HCC1, we thus analyzed\nSPAK levels in previously published transcriptomic data from SETD2 KO mouse\nmodels of HCC, and we did not observe statistically significant changes in SPAK\nexpression between SETD2-wt and SETD2-cKO liver tumors, suggesting that SPAK\nmay not be directly regulated by SETD2 at the transcriptional level.\nIn addition, previous studies have reported that SETD2 and its catalytic product\nH3K36me3 can play tumor-promoting roles in HCC2-3. These findings suggest that the\nrole of SETD2 and H3K36me3 in liver cancer may be context-dependent and\npotentially influenced by factors such as genetic background, experimental variation,\nor tumor heterogeneity. Further investigation using SETD2 knockout models will be\nvaluable for clarifying whether the loss of H3K36me3 has any direct impact on SPAK\nregulation.\nReferences\n[1] Li XJ, Li QL, Ju LG, et al. Deficiency of Histone Methyltransferase SET Domain-\nContaining 2 in Liver Leads to Abnormal Lipid Metabolism and HCC. Hepatology.\n2021. 73(5): 1797-1815.\n[2] Yang Y, Zhang L, Munyurangabo G, et al. Deficiency of the histone H3K36\nmethyltransferase SETD2 inhibits the proliferation and migration of hepatocellular\ncarcinoma cells. J Cancer. 2024. 15(20): 6479-6489.\n[3] Wang T, Han L, Huo Y, et al. H3K36me3-Guided m6A Modification of Oncogenic\nL1CAM-AS1 Drives Macrophage Polarization and Immunotherapy Resistance in\nHepatocellular Carcinoma. Adv Sci (Weinh). 2025 : e14909.\n3.According to single cell RNAseq studies, T and NK cells express high amounts of\nSPAK. How do these cells respond to systemic SPAC inhibition?\nResponse: We thank the reviewer for this important question. To assess the potential\ne\npotential effects of SPAK inhibition on immune cells, we isolated CD8⁺ T cells from mouse\nspleens and treated them with the SPAK inhibitor ZT-1a in vitro. We found that ZT-1a\ntreatment did not lead to significant changes in CD8⁺ T cell viability, cytotoxic function,\nor exhaustion marker expression. These results suggest that systemic SPAK inhibition\nmay have minimal impact on CD8⁺ T cell activity under our experimental conditions\n(fig. S19, C and D).\nC\nD\nfig. S19, C and D Cytotoxicity and exhaustion markers in vivo–activated mouse CD8⁺\nT cells treated with SPAK inhibitor ZT-1a, analyzed by flow cytometry (n = 3)\n4. Previous studies have explored the role of the SPAC-NKCC axis in HCC. The authors\nneed to explain why they ignored these previous studies.\nResponse: We thank the reviewer for pointing out this. In the revised manuscript, we\nhave incorporated a discussion of these studies and their relevance to our findings1.\nSpecifically, we acknowledge the established role of SPAK in regulating NKCC1\nactivity and the potential contribution of this axis to tumor progression. This addition\nprovides further context for understanding the mechanisms through which SPAK may\ninfluence HCC biology. The relevant references have been cited accordingly in the\nrevised Discussion section.\nReference\n[1] Sie ZL, Li RY, Sampurna BP, et al. WNK1 Kinase Stimulates Angiogenesis to\nPromote Tumor Growth and Metastasis. Cancers (Basel). 2020. 12(3).\n5. The authors need to show changes in GSK3b phosphorylation in SPAC\noverexpressing HCC and demonstrate that inhibition or ablation of AKT has no effect\non S9 phosphorylation in such cases.\nResponse: We thank the reviewer for this important suggestion. In response, we\nperformed additional experiments to evaluate GSK3β phosphorylation in SPAK-\noverexpressing HCC cells. Our results showed a significant increase in GSK3β Ser9\nphosphorylation upon SPAK overexpression. Furthermore, we treated these cells with\nan AKT inhibitor (MK2206) and found that Ser9 phosphorylation remained elevated,\nindicating that SPAK-induced GSK3β phosphorylation at Ser9 occurs independently of\nAKT signaling (fig. S23F).\nfig. S23F Western blot analysis of the phosphorylation level of AKT1 and GSK3β in\nindicated HCC cells treated with or without MK2206 (10 μm)\n6.The authors attributed the role of SPAK in HCC progression to increased c-Jun and\nPD-L1 in HCC cells. Discussion lines 381-385: “Based on these findings, we\nhypothesize that targeting PD-L1 alone may be insufficient to counteract SPAK-driven\noncogenesis in high-SPAK-expressing HCC”. However, in Figure 6I, targeting PD-L1\nalone seems sufficient.\nResponse: We thank the reviewer for this insightful comment and fully acknowledge\nthe inconsistency between our interpretation and the results shown in Figure 6I. Indeed,\nin this experiment, PD-L1 blockade alone elicited a significant anti-tumor effect in the\nSPAK-overexpressing HCC model, suggesting that targeting PD-L1 can be effective\nunder certain conditions. However, we would like to clarify that this result reflects the\ntherapeutic response within a controlled preclinical setting, which may not fully\nrecapitulate the complexity of human HCC. In clinical scenarios, SPAK may drive\noncogenesis not only through immune evasion via PD-L1 upregulation, but also\nthrough additional pathways such as c-Jun activation and downstream transcriptional\nprograms, which could contribute to tumor progression and therapeutic resistance.\nTo address this discrepancy, we have revised the Discussion section to adopt a more\ncautious and nuanced tone. Rather than stating that PD-L1 blockade alone may be\ninsufficient, we now emphasize that while PD-L1 inhibition demonstrates therapeutic\nefficacy in our model, SPAK-driven oncogenic signaling may involve multifaceted\nmechanisms. These may ultimately limit the durability or breadth of response to PD-\nL1 monotherapy, particularly in heterogeneous human tumors.\n7. If genes body methylation controls SPAK expression in HCC, the authors need to\nperform methylation IP or sequencing experiments to establish this point more directly.\nResponse: We appreciate the reviewer’s insightful suggestion. While we fully agree that\nmethylation IP or bisulfite sequencing would provide more direct evidence to support\nthe role of gene body methylation in regulating SPAK expression, there are technical\nchallenges associated with these approaches in our specific context. Notably, the gene\nbody of SPAK spans over 300 kb, which poses considerable difficulties in designing\ntargeted methylation assays with sufficient resolution and coverage.\nIn our current study, we therefore relied on publicly available DNA methylation\ndatasets (e.g., TCGA-LIHC) and observed a strong positive correlation between SPAK\nexpression and gene body methylation levels. In addition, treatment with the DNA\nmethylation inhibitor 5-Aza led to decreased SPAK expression in HCC cell lines,\nfurther supporting a functional relationship. Importantly, we also performed CUT&Tag\nexperiments and found that DNMT3B is enriched at the exon regions of the SPAK gene,\nsuggesting that DNMT3B may directly bind to and regulate gene body methylation of\nSPAK.\nTaken together, these findings suggest that gene body methylation may contribute to\nthe regulation of SPAK expression. We acknowledge the value of more direct\nexperimental validation and hope to address this in future work, potentially with\nimproved targeted methylation profiling technologies.\n8.In vitro gene knockout mediated by Cas9 requires at least two independent clones to\navoid off-target effects (Figure 4G).\nResponse: We appreciate the reviewer’s concern regarding the potential for off-target\neffects in CRISPR/Cas9-mediated gene knockout experiments. In response, we\nperformed additional validation using a second independent single-cell clone derived\nfrom the same CRISPR targeting strategy. This clone was originally generated together\nwith the first one and retained under identical conditions. The key in vitro experiments\nshown in Figure 4G and Figure 6C were repeated in this additional clone, and we\nobserved consistent phenotypes, which supports the specificity of the observed effects\n(fig. S20, H and I, and fig. S25C).\nfig. S20 (H) Western blot analysis of the SPAK expression levels in HLF sgSPAK-1,\nHLF sgSPAK-2, and HLF-sgNC cells. (I) Western blot analysis of the phosphorylation\nlevel of GKS3β and c-Jun in indicated HLF sgSPAK-2 cells.\nfig. S25C Western blot analyses showing PD-L1 expression levels in indicated HCC\ncells.\n9. The correlation coefficient between SPAK and immune exhaustion does not seem\nvery high r<0.4.\nResponse: We thank the reviewer for this valuable observation. We agree that the\ncorrelation coefficient between SPAK expression and immune exhaustion markers is in\nthe moderate range (r < 0.4). However, given the inherent heterogeneity of the tumor\nimmune microenvironment and the multifactorial nature of immune exhaustion, such\ncorrelation values are frequently observed in cancer studies and may still reflect\nbiologically relevant associations. Importantly, the consistent pattern observed across\nmultiple independent cohorts supports the robustness of this link and suggests that\nSPAK may contribute to the immunosuppressive landscape in HCC.\n10.The effect of SPAK ablation on HCC development is poorly documented. No effect\non tumor multiplicity, size and volume is shown. The effect of SPAK ko on HCC\nmarkers not shown either.\nResponse: We thank the reviewer for this important comment. Due to space limitations\nin the original submission, we had not included data on tumor multiplicity. In the\nrevised manuscript, we have now added this information to the Supplementary\nMaterials, showing that SPAK knockout significantly reduces tumor multiplicity (Max\ntumor volume and Tumor number) in our HCC model. However, as the tumors induced\nby the hydrodynamic AKT+NRasV12-induced HCC model are diffusely distributed\nthroughout the liver, it is technically challenging to accurately quantify tumor number\nand maximum size. Therefore, we have included supplementary data from the\nDEN+CCL4-induced HCC model and the hydrodynamic MYC+sgP53-induced HCC\nmodel, in which tumor burden (Max tumor volume and Tumor number) can be more\nreliably assessed (fig. S10, A and H). Additionally, we have performed further\nexperiments to assess the effect of SPAK knockout or knockdown on the expression of\nclassical HCC markers, including AFP, and GPC3 (fig. S10, B, D, and I). The results\nindicate a marked reduction in these markers upon SPAK ablation, suggesting a\nsuppressive effect on tumor progression. We appreciate the reviewer’s suggestion,\nwhich has helped us strengthen the evidence for the role of SPAK in HCC development.\nFig. S10. (A) Tumor burden (n = 12) of SPAKf/f and SPAKf/f; Alb-Cre mice in indicated\nmodel. (B) Representative immunostaining (scale bars, 100 μm) and IHC score of AFP\nand GPC3 in indicated mice HCC model (n = 6). (D) Representative immunostaining\n(scale bars, 100 μm) of AFP and GPC3 in indicated mice HCC model (n = 6). (H)\nTumor burden (n = 8) of pT3-shSPAK and pT3-empty mice in indicated model. (I)\nRepresentative immunostaining (scale bars, 100 μm) of Ki-67 (n = 4), AFP (n = 6), and\nGPC3 (n = 6) in indicated mice HCC model;\n11. The link between SPAK expression and immunosuppression is poorly defined. The\nstudies in which SPAK silencing enhace killing by T cells in vitro are not very well\ncontrolled. Is the effect unique to CD8 or does SPAK also have an effect on CD4 T cells\nand NK cells? Does it affect expression of exhaustion markers in the vitro system?\nResponse: We thank the reviewer for these important and constructive comments. In\nresponse, we optimized our in vitro co-culture system to better evaluate the role of\nSPAK in immunosuppression. Specifically, we established a co-culture system of CD8⁺\nT cells isolated from mouse spleens with Hepa1-6 HCC cells, with or without SPAK\noverexpression. In this system, we assessed both tumor cell apoptosis and CD8⁺ T cell\nfunction. Our results show that SPAK overexpression in HCC cells significantly\nreduces CD8⁺ T cell-mediated killing, as evidenced by decreased tumor cell apoptosis.\nOur results showed that SPAK overexpression in HCC cells markedly reduced CD8⁺ T\ncell-mediated cytotoxicity, as indicated by decreased levels of effector molecules\nGZMB and IFN-γ in CD8⁺ T cells, and simultaneously promoted T cell exhaustion,\nevidenced by upregulation of PD-1 and TIM-3 (Fig. 3, H to J). These findings suggest\nthat SPAK expression in tumor cells impairs CD8⁺ T cell function through both\ndiminished cytotoxicity and increased exhaustion. We appreciate the reviewer’s\ninsightful question regarding the effects of SPAK on CD4⁺ T cells and NK cells.\nAlthough these aspects were not addressed in our current study, we fully agree they are\nimportant and plan to investigate them in our future research.\nFig. 3 (H) A representative histogram showing the proportion of Annexin V–positive\ncells in each group. (I) The percentage of Annexin V–positive cells in the indicated\ngroups (n = 3) and the relative cytotoxic capacity of T cells was determined using the\nformula: Relative T-cell killing ability = [(SPAK group with T cells – without T cells)\nAnnexin V%] / [(Control group with T cells – without T cells) Annexin V%]. (J) Flow\ncytometry analysis of GZMB+IFN-γ+CD8+ T cells and PD-1+TIM-3+CD8+ T cells in\nthe coculture system (n = 3).\nReviewer #3\nSpecific comments:\n1.Line69-70 “non- inflamed HCCs can be further categorized into intermediate and\nimmune-exclusion subtypes.”\nHowever in line 73-75: “Inflamed HCCs can be further subdivided into immune-active,\nimmune-exhausted. ”\nThese two statements are contradictory. Does immune exhaustion belong to non-\ninflamed HCCs or inflamed HCCs?\nResponse: We thank the reviewer for highlighting this point. According to the\nclassification proposed in the previous study ((Sia’s reformed immune classification of\nHCC)1, immune-exhausted HCCs are indeed categorized under the inflamed subtype,\nwhile immune-excluded HCCs are considered non-inflamed. These two subtypes\nexhibit distinct immune features: immune exhaustion is characterized by abundant but\ndysfunctional T cell infiltration, whereas immune exclusion reflects the presence of\nimmune cells retained in the peritumoral stroma with limited intratumoral infiltration.\nTherefore, to clarify, immune exhaustion belongs to inflamed HCCs.\nReferences\n[1] Montironi C, Castet F, Haber PK, et al. Inflamed and non-inflamed classes of\nHCC: a revised immunogenomic classification. Gut. 2023. 72(1): 129-140.\n2.Line 110-112: “we found that SPAK expression was significantly elevated in the\nimmune-exhausted subgroup compared to other immune- associated subgroups.”\nPlease validate these findings using immunohistochemical (IHC) methods. Please\nprovide the characteristic expression profiles of different subtypes of human HCC and\nthe expression patterns of SPAK in various HCC subtypes. It is recommended to capture\nimages from consecutive sections of the same regions at 40x, 100x, and 200x\nmagnifications.\nResponse: We thank the reviewer for the helpful suggestion. To validate the\ntranscriptomic findings, we performed multiplex immunofluorescence staining on\nhuman HCC tissue specimens using both immune exhaustion markers (PD-1, TIM-3,\nand PD-L1) and an immune infiltration marker (CD3), along with SPAK. Samples with\nhigh expression of CD3, PD-1, TIM-3, and PD-L1 were classified as belonging to the\nimmune-exhausted subtype, whereas those with low expression of these markers were\nconsidered as the rest subtype1. SPAK staining was concurrently evaluated in the same\ntissue sections. Consistent with our transcriptomic analysis, we observed that SPAK\nexpression was markedly higher in the immune-exhausted subtype compared to the rest\nsubtype (fig. S1D). These results further support the association of SPAK with the\nimmune-exhausted phenotype in HCC.\nfig. S1D Representative mIF staining images of CD3, PD-1, TIM-3, PD-L1, SPAK in\nthe human HCC tumors tissues (scale bars, 100 μm).\nReferences\n[1] Sia D, Jiao Y, Martinez-Quetglas I, et al. Identification of an Immune-specific Class\nof Hepatocellular Carcinoma, Based on Molecular Features. Gastroenterology. 2017.\n153(3): 812-826.\n3.Line 123-125: “SPAK was also highly expressed in TCGA-iCluster1 and Hoshida S2\nsubtypes, which are characterized by aggressive phenotypes, activation of MAPK, AKT,\nand Myc signaling, and TP53 mutations”.\nAs for TCGA-iCluster1 and Hoshida S2, how were these two subtypes determined?\nWhat are their specific biological markers, and why are they considered aggressive\nsubtypes?\nResponse: We thank the reviewer for this insightful question. The TCGA-iCluster1\nsubtype was originally defined in the TCGA-LIHC integrative clustering analysis,\nwhich incorporated multiple omics layers, including somatic mutations, copy number\nvariation, DNA methylation, and mRNA expression profiles1. iCluster1 in HCC is\ncharacterized by frequent TP53 mutations, chromosomal instability, and activation of\nproliferation-related signaling pathways, such as AKT, MAPK, and Myc. These\nmolecular features have been associated with poor prognosis and aggressive tumor\nbehavior2-3.\nThe Hoshida S2 subtype was defined in an integrative transcriptome analysis by\nHoshida et al.4, which categorized HCC into S1–S3 based on gene expression patterns.\nNotably, the S2 subtype shares transcriptional features with TCGA-iCluster12-3.\nTogether, these observations support the classification of both iCluster1 and S2 as\naggressive HCC subtypes. We have now included the relevant citations in the revised\nmanuscript.\nReferences\n[1] Cancer GARNEawe, Network CGAR. Comprehensive and Integrative Genomic\nCharacterization of Hepatocellular Carcinoma. Cell. 2017. 169(7): 1327-1341.e23.\n[2] Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev\nDis Primers. 2021. 7(1): 6.\n[3] Calderaro J, Ziol M, Paradis V, Zucman-Rossi J. Molecular and histological\ncorrelations in liver cancer. J Hepatol. 2019. 71(3): 616-630.\n[4] Hoshida Y, Nijman SM, Kobayashi M, et al. Integrative transcriptome analysis\nreveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res.\n2009. 69(18): 7385-92.\n4.Line 144-147” We then generate shNChep mice and shSPAK hep mice, and\nestablished hydrodynamic gene delivery-based mouse HCC models by introducing\nmyr-AKT and N-RasV12 or MYC and sgP53, to resembling Hoshida S2 and TCGA-\niCluster 1 HCC subtypes”\nPlease validate the successful establishment of animal models of Hoshida S2 and TCGA\niCluster 1 HCC subtypes through immunohistochemistry (IHC), Western blot (WB),\nand PCR experiments.\nResponse: We appreciate the reviewer’s thoughtful comment. In response, we have\nperformed additional experiments to validate that the hydrodynamically induced mouse\nHCC models recapitulate key molecular features of the Hoshida S2 and TCGA-\niCluster1 subtypes. Specifically, we conducted IHC analyses to assess the expression\nof representative markers associated with these subtypes, including RAS, AKT, and c-\nMyc. We found that the AKT + NRAS–driven HCC model exhibited high expression\nof p-AKT (S473) and NRas (fig. S10C), whereas the c-MYC + sgP53–driven HCC\nmodel showed strong nuclear c-Myc expression and reduced TP53 expression (fig.\nS10E).\nfig. S10 (C) Representative immunostaining (scale bars, 400 μm (L), 200 μm (R)) of p-\nAKT (S473) and N-Ras in indicated model. (E) Representative immunostaining (scale\nbars, 400 μm (L), 200 μm (R)) of c-Myc and P53 in indicated model.\n5.Line 156-157 “Given that liver cancer stem cells (LCSCs) were enriched in Hoshida\nS2 and TCGA-iCluster 1 subtypes.”\nTo demonstrate that animal models of Hoshida S2 and TCGA iCluster 1 HCC subtypes\nexhibit stem cell-like features using IHC, WB, and PCR experiments, follow this\nexperimental framework.\nResponse: We thank the reviewer for the constructive suggestion. As previously\nreported, Hoshida S2 and TCGA-iCluster1 subtypes are known to be enriched for\nstemness-related signatures based on prior studies1-2, which we have now cited in the\nrevised manuscript. To validate that our hydrodynamic HCC models representing\nHoshida S2 and TCGA-iCluster1 subtypes exhibit liver cancer stem cell (LCSC)-like\nfeatures, we performed immunohistochemistry (IHC) staining for established stemness\nmarkers, including EpCAM, CD133, and SOX9. The results demonstrated elevated\nexpression of these markers in both models, supporting the presence of stem-like\ncharacteristics, while SPAK knockdown significantly reduced the expression levels of\nthese stemness markers (highlighted by green boxes in fig. S15, E and F).\nfig. S15, E and F Representative immunostaining (scale bars, 100 μm) of EPCAM,\nCD133, and SOX9 in AKT + NRAS– and MYC + sgP53–driven HCC model.\nReferences\n[1] Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis\nPrimers. 2021. 7(1): 6.\n[2] Calderaro J, Ziol M, Paradis V, Zucman-Rossi J. Molecular and histological\ncorrelations in liver cancer. J Hepatol. 2019. 71(3): 616-630.\n6.Line 163-164 “Overexpression of SPAK in HCC cells upregulated CSC-associated\nmarkers, including EpCAM, PROM1, and SOX9 (Fig. 2K).\nPlease validate that overexpression of SPAK in HCC cells upregulated CSC-associated\nmarkers through animal experiments.\nResponse: We thank the reviewer for the helpful suggestion. To validate that SPAK\noverexpression promotes the expression of cancer stem cell (CSC)-associated markers\nin vivo, we performed immunohistochemistry (IHC) staining on tumor tissues from\nSPAK-overexpressing mouse HCC models. The results confirmed that overexpression\nof SPAK led to increased protein levels of EpCAM, PROM1 (CD133), and SOX9,\nconsistent with our in vitro findings, while S371A significantly reversed these (fig.\nS15G).\nfig. S15G Representative immunostaining (scale bars, 100 μm) and IHC score of\nEPCAM, CD133, and SOX9 in Hepa1-6 induced HCC mice model.\n7.Line 165-168 “Sphere formation assays demonstrated that SPAK overexpression\nenhanced spheroid-forming efficiency, whereas its depletion significantly impaired this\nability (Fig. 2, L and M). A limiting dilution assay in nude mice revealed that SPAK\nknockdown markedly reduced tumor-initiating potential and stem cell frequency (Fig.\n2N)”\nThese two experiments do not sufficiently demonstrate that SPARK overexpression is\nassociated with stem cell characteristics\nResponse: We thank the reviewer for this insightful comment and fully agree that\nadditional evidence is needed to substantiate the association between SPAK and cancer\nstemness in HCC. In our current study, we have already provided several lines of\nevidence supporting this relationship. Specifically, SPAK overexpression upregulated\nCSC-associated markers (EpCAM, PROM1, and SOX9), enhanced the spheroid-\nforming ability of HCC cells, increased tumor-initiating potential in limiting dilution\nassays, and conferred resistance to tyrosine kinase inhibitors (TKIs).\nTo further strengthen this conclusion, we additionally performed IHC on tumor\ntissues from HCC mouse models, confirming the positive correlation between SPAK\nand stem cell markers EpCAM, PROM1, and SOX9 in vivo (fig. S15, E to G). To\nfurther confirm this in a human setting, we analyzed a previously published scRNA-\nseq dataset that profiled human liver development1. Cell clusters were annotated into\nhepatoblast (HB), fetal hepatocyte (FH), and adult hepatocyte (AH) groups (Fig. 2G).\nUsing CytoTRACE2-3, we observed a progressive decrease in developmental potential\nfrom HB to FH to AH. SPAK expression was enriched in the low differentiated HB/FH\nclusters and reduced in the mature AH cluster, and correlated with progenitor- but not\nmature hepatocyte- associated markers (Fig. 2, H and I). These results suggest that\nSPAK is preferentially expressed in less differentiated hepatic cells. Similarly, in the\nsingle-cell dataset of HCC, we identified a subset of malignant hepatocytes with high\nSPAK expression that exhibited the highest developmental potential (fig. S15C). These\nresults consistently support the role of SPAK in promoting stem-like characteristics in\nHCC.\nfig. S15, E to G Representative immunostaining (scale bars, 100 μm) of EPCAM,\nCD133, and SOX9 and IHC score in indicated HCC mice model.\nFig. 2 (G) Analysis of a human liver development single-cell atlas revealed three cell\nclusters: hepatoblasts (HB), fetal hepatocytes (FH), and adult hepatocytes (AH). (H)\nUMAP of CytoTRACE 2 predicted order (more to less differentiated) and SPAK\nrelative expression. (I) SPAK, hepatic progenitor and mature hepatocyte markers\nexpression in HB/FH and AH clusters.\nfig. S15C Using CytoTRACE on the integrated single-cell dataset of HCC (GSE202642,\nGSE151530, GSE206325, GSE149614, GSE156625), we identified a subset of\nmalignant hepatocytes with high SPAK expression exhibiting the highest\ndevelopmental potential.\nReferences\n[1] Wesley BT, Ross A, Muraro D, et al. Single-cell atlas of human liver development\nreveals pathways directing hepatic cell fates. Nat Cell Biol. 2022. 24(10): 1487-1498.\n[2] Kang M, Armenteros J, Gulati GS, et al. Mapping single-cell developmental\npotential in health and disease with interpretable deep learning. bioRxiv. 2024 .\n[3] Gulati GS, Sikandar SS, Wesche DJ, et al. Single-cell transcriptional diversity is\na hallmark of developmental potential. Science. 2020. 367(6476): 405-411.\n8.Line 183-187 “Notably, SPAK overexpression moderately enhanced tumor growth in\nnude mice, while this effect was markedly amplified in immunocompetent C57BL/6\nmice (fig. S16, A to D).”\nPlease provide immunohistochemistry (IHC), Western blot (WB), and PCR data and\nimages demonstrating the relationship of the expression of SPARK and immune\nexhaustion-related factors. The IHC images should illustrate SPAK expression in both\ncancer cells and the immune microenvironment\nResponse: We thank the reviewer for the thoughtful suggestion. We would like to clarify\nthat the sentence in lines 183–187 was intended as an initial observation, highlighting\nthe enhanced tumor-promoting effect of SPAK in immunocompetent mice compared to\nimmunodeficient ones. This served as a rationale for the subsequent mechanistic\ninvestigations. As shown in our single-cell RNA-seq analysis, SPAK-high tumors\nexhibited an increased proportion of exhausted T cell subsets. In addition, SPAK\nexpression showed a positive correlation with T cell exhaustion signatures in both the\nTCGA-LIHC and ICGC-LIRI cohorts. These findings were further supported by flow\ncytometry and multiplex immunofluorescence, which confirmed that SPAK\noverexpression is associated with increased T cell exhaustion and reduced effector\nmolecules. These results provide a mechanistic explanation for the more pronounced\ntumor-promoting effect of SPAK observed in immunocompetent C57BL/6 mice. We\nwill revise the text to make this progression of logic clearer to avoid potential confusion.\n9.Line 197-206 These findings were further corroborated by multiplex\nimmunohistochemistry in tumor tissues from the hydrodynamic injection liver cancer\nmodel, DEN/CCl4-induced HCC model and hepatic orthotopic transplant model (Fig.\n3, F to I, and fig. S19, A and B).\nPlease supplement with immunohistochemical (IHC) double-staining for CD8+GZMB\nand CD8+PD-1/TIM3. Only co-expression staining (i.e. markers localized to the same\ncells) would be conclusive. Conventional immunofluorescence (IF) staining, which\noften shows separate expression patterns, is insufficient to validate these findings.\nResponse: We thank the reviewer for the constructive comment and sincerely apologize\nfor the inaccurate terminology in our manuscript. The experiments in this section were\nperformed using multiplex immunofluorescence (mIF) based on tyramide signal\namplification (TSA) (see Methods section for details), not multiplex\nimmunohistochemistry (mIHC) as originally stated. We have corrected this\nterminology in the revised manuscript.\nWe fully agree with the reviewer that demonstrating co-expression of functional and\nexhaustion markers within the same CD8⁺ T cells is critical for accurate immune\nphenotyping. In our study, we employed TSA-based mIF, which has been shown to\noffer high spatial resolution and reliable detection of multiple markers within the same\ncell. In our analysis, we observed clear co-localization of CD8 with GZMB, PD-1, and\nTIM-3 in individual T cells in the tumor microenvironment across all four HCC models.\nThese co-expression events were confirmed by high-resolution imaging and manual\ninspection. To aid interpretation, representative co-expressing cells have been indicated\nby white arrows in the images.\nWe understand the reviewer’s concern regarding marker co-expression and\nappreciate the opportunity to clarify this point. While we acknowledge that\nconventional IF may have limitations, we respectfully note that TSA-based mIF has\nbeen widely adopted and validated for co-expression analysis by Taube et al.1-2 and\nfurther supported by Tan et al.3, mIF allows precise identification of cellular phenotypes.\nWe hope this explanation addresses the reviewer’s concern, and we remain open to\nfurther clarifications if needed.\nReferences\n[1] Taube JM, Akturk G, Angelo M, et al. The Society for Immunotherapy of Cancer\nstatement on best practices for multiplex immunohistochemistry (IHC) and\nimmunofluorescence (IF) staining and validation. J Immunother Cancer. 2020. 8(1):\ne000155.\n[2] Taube JM, Roman K, Engle EL, et al. Multi-institutional TSA-amplified\nMultiplexed Immunofluorescence Reproducibility Evaluation (MITRE) Study. J\nImmunother Cancer. 2021. 9(7).\n[3] Tan W, Nerurkar SN, Cai HY, et al. Overview of multiplex\nimmunohistochemistry/immunofluorescence techniques in the era of cancer\nimmunotherapy. Cancer Commun (Lond). 2020. 40(4): 135-153.\n10.Line 206-211” Collectively, these findings demonstrate that SPAK induces T cell\nexhaustion and impairs the cytotoxic activity of cytotoxic T cells in HCC \"\nThis experiment demonstrates T lymphocyte-mediated killing of cancer cells but does\nnot establish that SPAK directly causes T cell exhaustion.\nResponse: We thank the reviewer for the insightful comment. To address this point, we\nconducted co-culture experiments using murine CD8⁺ T cells isolated from spleens and\nHepa1-6 cells overexpressing SPAK. We found that, under SPAK-overexpressing\nconditions, CD8⁺ T cells displayed increased expression of exhaustion markers (e.g.,\nPD-1, TIM-3) and reduced levels of cytotoxic molecules such as GZMB and IFN-γ (Fig.\n3, H to J). These findings suggest that SPAK in tumor cells contributes directly to the\ninduction of T cell exhaustion and impairs their cytotoxic function.\nFig. 3 (H) A representative histogram showing the proportion of Annexin V–positive\ncells in each group. (I) The percentage of Annexin V–positive cells in the indicated\ngroups (n = 3) and the relative cytotoxic capacity of T cells was determined using the\nformula: Relative T-cell killing ability = [(SPAK group with T cells – without T cells)\nAnnexin V%] / [(Control group with T cells – without T cells) Annexin V%]. (J) Flow\ncytometry analysis of GZMB+IFN-γ+CD8+ T cells and PD-1+TIM-3+CD8+ T cells in\nthe coculture system (n = 3).\n11.Line 234-235 “T-5224 treatment reversed SPAK-induced tumor formation, sphere\nformation, upregulation of CSC-associated markers, and drug resistance”\nPlease supplement the Western blot (WB) data for SPARK + T5224, including controls\nfor SPARK and c-Jun.\nResponse: We thank the reviewer for the helpful suggestion. In response, we have\nperformed additional Western blot experiments to examine the expression levels of\nSPAK and c-Jun in three groups: control, SPAK-overexpressing, and SPAK + T-5224-\ntreated cells. While T-5224 does not reduce c-Jun protein levels—as expected given its\nmechanism of action as a transcriptional inhibitor (fig. S21A).\nfig. S21A Western blot analyses showing SPAK and c-Jun expression levels in\nPLC/PRF/5-Control, -SPAK, -SPAK treated with T5224 (10 μm).\n12.Line 269-272\nPlease provide IHC images of S24A at 100× and 200× magnification, as well as HE-\nstained images. Additionally, provide the expression levels and positive rates of four\nspecific markers in both HCC cancer cells and tumor microenvironment-infiltrating T\ncells, include an analysis of correlations between these markers.\nResponse: We thank the reviewer for the helpful and detailed suggestions. In response,\nwe have performed H&E staining on S24A tumor tissues and provided representative\nimages at both 100× and 400× magnifications. Additionally, we have included\nimmunohistochemistry (IHC) images for SPAK, p-GSK3BS9, c-JUN, and PD-L1, also\ncaptured at 100× and 400× magnifications. We also performed correlation analysis\namong these markers to explore their interrelationships (fig. S24, A and B).\nB\nfig. S24 (A) Representative H&E, IHC staining of SPAK, p-GKS3βS9, c-Jun and PD-\nL1 in serial tumor tissue sections (scale bars, 400 μm (L), 100 μm (R)). (B) Correlation\nanalysis of SPAK and other proteins based on IHC scores of HCC specimens (n = 110).\n13.Line 276-281\nPlease provide immunohistochemical (IHC) staining images for CD8, PD-L1, and\nSPAK at magnifications of 40×, 100×, and 200×.\nResponse: We thank the reviewer for this helpful suggestion. As requested, we have\nprovided immunohistochemical (IHC) staining images for CD8, PD-L1, and SPAK (fig.\nS25I).\nfig. S25I Representative immunostaining (scale bars, 100 μm) of SPAK, PD-L1, and\nCD8 in indicated HCC mice model\n14.Line 296-298 “In summary, these findings suggest that SPAK stabilizes PD-L1 by\ndegradation and thus promotes the formation of immune exhaustive microenvironment\nin HCC”\nThis conclusion is overly speculative. Experiments analyzing the tumor\nmicroenvironment should be needed to strengthen the evidence.\nResponse: We thank the reviewer for the insightful comment and fully agree that\nmechanistic conclusions should be supported by solid experimental evidence. In this\nstudy, we provide multiple lines of data suggesting that SPAK may contribute to PD-\nL1 stabilization and promote an immune-exhausted tumor microenvironment in HCC.\nSpecifically, we further analyzed single-cell RNA-seq data from HCC models, which\nrevealed that tumors with high SPAK expression exhibit an increased proportion of\nexhausted CD8⁺ T cells cluster. We further demonstrated that SPAK knockdown\ndecreases PD-L1 protein levels, whereas SPAK overexpression increases PD-L1\nstability, independent of its mRNA expression. We further showed that this regulation\nis dependent on GSK3B pathways, which are known to impact PD-L1 degradation.\nFunctionally, in vitro co-culture experiments of SPAK-overexpressing HCC cells with\nCD8⁺ T cells demonstrated impaired T cell cytotoxicity, consistent with SPAK\npromoting T cell exhaustion. Complementary in vivo analyses using flow cytometry\nand multiplex immunofluorescence confirmed increased PD-1⁺ and TIM-3⁺ CD8⁺ T\ncells and decreased effector molecule (e.g., GZMB) expression in the tumor\nmicroenvironment of SPAK-overexpressing mice. These findings were observed across\nmultiple HCC mouse models and further supported by transcriptomic analyses from\nboth TCGA-LIHC and ICGC-LIRI cohorts. We sincerely appreciate the reviewer’s\nconstructive suggestions, which have helped us clarify and strengthen our interpretation.\n15. Based on the title, this article primarily highlights the central role of SPARK in\nHCC development, progression, and immune exhaustion. The authors comprehensively\nanalyze multiple functional roles of SPARK in HCC. SPAK promotes\nhepatocarcinogenesis, enhances stem cell properties, drives immune exhaustion, and\nconfers resistance to lenvatinib, sorafenib, and donafenib. Mechanistically, SPAK\ndirectly interacts with and phosphorylates GSK3β at Ser9, thereby inhibiting GSK3β-\ndependent, proteasome-mediated degradation of c-Jun and PD-L1. Additionally, it also\nexplored the intragenic methylation of SPAK.\nOverall, while the article provides a comprehensive investigation into the functional\nroles of SPARK in HCC, this broad scope dilutes the clarity of its central theme and\nreveals insufficient mechanistic depth in exploring the underlying pathways.\nResponse: We sincerely thank the reviewer for their insightful and constructive\nfeedback. We fully agree that exploring multiple functional roles of SPAK in our initial\nmanuscript, while comprehensive, risked diluting the central theme and lacked\nsufficient mechanistic depth. To directly address this, we have significantly revised the\nmanuscript to sharply focus on the SPAK–GSK3β–c-JUN/PD-L1 axis as the core\nmechanism. We now explicitly position this axis as the primary driver linking SPAK to\nhepatocarcinogenesis, immune exhaustion, and drug resistance. Crucially, we have\nsubstantially deepened the mechanistic validation of this pathway, providing more\nrobust evidence for SPAK-mediated GSK3β phosphorylation at Ser9 and its consequent\nstabilization of c-JUN and PD-L1, leading to their downstream effects. The diverse\nfunctional roles are now clearly presented as logical consequences of this central axis,\nensuring a cohesive narrative. We believe these revisions greatly enhance the\nmanuscript's clarity and mechanistic insight, and we are deeply grateful for the\nreviewer's guidance in strengthening our work."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I’d like to thank the authors for addressing the comments. The readability and coherence of the manuscript have improved\nsubstantially. The addition of short introductory sentences and paragraphs has made manuscript clearer. I also appreciate\nthe inclusion of computational analyses and the detailed descriptions of each method, along with appropriate citations of the\ntools used. This not only clarifies the methodology and enhances reproducibility, but also appropriately acknowledges the\ncontributions of those who developed the tools, packages, and related resources.\nMain\nLine 167-168 and Fig. 2G – Please provide the marker genes that support the annotation of clusters as HB, FH, and AH, like\nhow it was presented in Supplementary lines 244-247. Including this info is important, as the UMAP shows cluster\ngranularity. If HB, FH, and AH are the main clusters, then the finer clusters likely represent subtypes. Since the focus isn’t on\nidentifying subpopulations, I’d simply present evidence supporting the annotation of the main clusters, supported by marker\ngenes.\nSupplementary\nPage 6, lines (190–199) and the following paragraph lines (200–205) are essentially describing the use of the same\nmethodology. In the first instance, the authors classify samples into a broad immune classification (Immune vs Rest). Then,\nusing the Immune class, they apply the same method to further classify samples into Active and Exhausted classes.\nPage 7, lines 206-214 For consistency with the rest of the supplementary material, please include package versions in the\nparagraph as well—even though they are listed in the table\nPage 7, lines 238-242 The authors should consider adding a figure legend (Figure S17B) at the end of the sentence to guide\nreaders to details on the marker genes presented in the dot plot\nPage 8, line 252 - Please provide the names of major cell atlases and include full citations\nPage 8, lines 253-266 Please refer to the figures, which illustrate the methodology described in this section\nPage 9, line 283-284 This plot does not appear to be included in the publication. Please clarify\nPage 11, line 379 – please add SPAK protein ID\nPage 11 “Molecular docking” section – please cite database and tools.", "reviewer_2": "The authors have made an attempt to address my critics. While they better explain their previously misinterpreted results\nregarding c-Jun phosphorylation by GSK3, the response re-enforces my view that the original manuscript was sloppily\nprepared and had many inherent mistakes. The new experiments re c-Jun phophorylation by GSK3 do not show that sPAK\ncontrols c-Jun ubiquitylation and turnover. Regarding the major issue whether SPAK or AKT account for GSK3 S9\nphosphorylation in human and mouse HCC, I find the new experiments rather superficial and I simply do not believe that\nSPAK phosphorylation of GSK3 has any biological relevance in those HCCs in which AKT is strongly activated, which\nappear to be the case for about 2/3 of human HCCs. Maybe SPAK is important in the HCCs in which AKT is not activated.\nThe new in vitro experiments of HCC cell line killing by CD8+ CTLs are superficial and do not show that the resistance to\nkilling by CTLs is due to PD-L1 expression by the HCC cells.", "reviewer_3": "I have no further suggestions for this revised manuscript."}, "author_response": "Dear Reviewers,\nWe sincerely appreciate the reviewers’ constructive comments on our manuscript,\nentitled \"Targeting SPAK suppresses progression and averts an immune exhaustive\nmicroenvironment in hepatocellular carcinoma\" (Manuscript ID: NCOMMS-25-\n18337-A). The issues raised have greatly helped us improve the clarity, readability, and\noverall presentation of the study.\nFor transparency, the reviewers’ comments are listed sequentially, with each point\nnumbered. Our responses are marked in blue font, and the corresponding revisions in\nthe manuscript are highlighted in orange font. We hope that these revisions adequately\naddress all concerns raised. A detailed point-by-point response is provided below.\nDetailed point-by-point responses to comments from reviewers\nReviewer #1\nI’d like to thank the authors for addressing the comments. The readability and coherence\nof the manuscript have improved substantially. The addition of short introductory\nsentences and paragraphs has made manuscript clearer. I also appreciate the inclusion\nof computational analyses and the detailed descriptions of each method, along with\nappropriate citations of the tools used. This not only clarifies the methodology and\nenhances reproducibility, but also appropriately acknowledges the contributions of\nthose who developed the tools, packages, and related resources.\nMain\nLine 167-168 and Fig. 2G – Please provide the marker genes that support the annotation\nof clusters as HB, FH, and AH, like how it was presented in Supplementary lines 244-\n247. Including this info is important, as the UMAP shows cluster granularity. If HB,\nFH, and AH are the main clusters, then the finer clusters likely represent subtypes. Since\nthe focus isn’t on identifying subpopulations, I’d simply present evidence supporting\nthe annotation of the main clusters, supported by marker genes.\nResponse: We thank the reviewer for the comment. We would like to clarify that HB,\nFH, and AH represent tissue types, not cell populations annotated from clustering. The\nclusters shown in the UMAP reflect tissue origin rather than subpopulations defined by\nmarker genes. The annotation of HB, FH, and AH is based on previously published\nliterature, which we have cited in the manuscript1.\nRef. 1. Wesley, B. T. et al. Single-cell atlas of human liver development reveals\npathways directing hepatic cell fates. Nat. Cell Biol. 24, 1487-1498 (2022).\nSupplementary\n1. Page 6, lines (190–199) and the following paragraph lines (200–205) are essentially\ndescribing the use of the same methodology. In the first instance, the authors classify\nsamples into a broad immune classification (Immune vs Rest). Then, using the Immune\nclass, they apply the same method to further classify samples into Active and Exhausted\nclasses.\nResponse: We thank the reviewer for this helpful observation. The two original\nparagraphs indeed described two related steps of our immune stratification workflow,\nwhich may have appeared repetitive in structure. To improve clarity and readability, we\nhave now merged the two descriptions into a single, streamlined paragraph. In the\nrevised version, we explicitly present the analysis as a two-step hierarchical\nclassification, where the first NTP classifier defines a broad Immune vs Rest distinction,\nand the second classifier further resolves immune-state heterogeneity into Active and\nExhausted subclasses. This integrated description eliminates redundancy, improves\nnarrative flow, and more clearly reflects the intended analytical framework. The revised\nparagraph is now provided in the Supplementary Methods (‘Identification of\ncandidate protein kinases’).\n2. Page 7, lines 206-214 For consistency with the rest of the supplementary material,\nplease include package versions in the paragraph as well—even though they are listed\nin the table\nResponse: We thank the reviewer for this helpful suggestion. To ensure consistency\nacross the supplementary materials, we have now added the corresponding package\nversions to the paragraph. The revised text has been updated accordingly in the\nSupplementary Methods (‘Identification of candidate protein kinases’).\n3. Page 7, lines 238-242 The authors should consider adding a figure legend (Figure\nS17B) at the end of the sentence to guide readers to details on the marker genes\npresented in the dot plot\nResponse: We thank the reviewer for this helpful suggestion. The Figure S17 legend\nhas been updated to clearly separate panels A and B, with panel A showing t-SNE plots\nand panel B presenting dot plots of marker genes. The revised paragraph is now\nprovided in the figure legend (Figure S17).\n4. Page 8, line 252 - Please provide the names of major cell atlases and include full\ncitations\nResponse: We thank the reviewer for the comment. Actually, we have previously\ndescribed the classification of CD8⁺ T cell subsets and cited the relevant references.\nSpecifically, CD8⁺ T cells were classified into functional subtypes based on established\nmarker genes: naive/activated (LEF1, TCF7, BCL2, JUND, XCL1, SLC7A1),\neffector-like (BBAF3, IFIT3, JUNB, IFIT1, JUN, FOSB), exhausted (CXCL13, FAM3C,\nRBPJ, TIGIT, HAVCR2, PDCD1), and proliferating (TOP2A, MKI67, TPX2, UBE2C,\nASPM) (refs. 5–8) (Supplementary Methods: Cell-type annotation). In addition, Fig. S17\nprovides a detailed list of marker genes for each cell cluster.\n5. Page 8, lines 253-266 Please refer to the figures, which illustrate the methodology\ndescribed in this section\nResponse: We thank the reviewer for the comment. In this section, our approach was to\nstratify patients based on tumor SPAK expression into high- and low-expression groups,\nand then analyze the CD8⁺ T cell subsets within each group. This methodology\nprimarily involves sample stratification and does not generate a figure. To avoid any\npotential misunderstanding, we have revised the corresponding section in the\nmanuscript for clarification.\n6. Page 9, line 283-284 This plot does not appear to be included in the publication.\nPlease clarify\nResponse: We thank the reviewer for the comment. The plot mentioned in lines 283–\n284 is indeed included in the publication and corresponds to Figure 8A (right panel).\n7. Page 11, line 379 – please add SPAK protein ID\nResponse: We thank the reviewer for the suggestion. The SPAK protein UniProtKB ID\n(Q9UEW8) has been added at the Supplementary Methods (‘Molecular docking’).\n8. Page 11 “Molecular docking” section – please cite database and tools.\nResponse: We appreciate the reviewer’s helpful comment. We have now added the\ncorresponding citations for the database and tools used in the Supplementary Methods\n(‘Molecular docking’), including the structural database (UniProtKB and RCSB\nProtein Data Bank) and docking software (SWISS-MODEL server and GRAMM-X).\nReviewer #2\nThe authors have made an attempt to address my critics. While they better explain their\npreviously misinterpreted results regarding c-Jun phosphorylation by GSK3, the\nresponse re-enforces my view that the original manuscript was sloppily prepared and\nhad many inherent mistakes. The new experiments re c-Jun phophorylation by GSK3\ndo not show that sPAK controls c-Jun ubiquitylation and turnover. Regarding the major\nissue whether SPAK or AKT account for GSK3 S9 phosphorylation in human and\nmouse HCC, I find the new experiments rather superficial and I simply do not believe\nthat SPAK phosphorylation of GSK3 has any biological relevance in those HCCs in\nwhich AKT is strongly activated, which appear to be the case for about 2/3 of human\nHCCs. Maybe SPAK is important in the HCCs in which AKT is not activated. The new\nin vitro experiments of HCC cell line killing by CD8+ CTLs are superficial and do not\nshow that the resistance to killing by CTLs is due to PD-L1 expression by the HCC\ncells.\nResponse: We sincerely thank the reviewer for the careful re-evaluation of our work\nand raising comments regarding this. We would like to clarify that our original\nmanuscript already included experimental evidence supporting the association between\nSPAK and c-Jun ubiquitylation and turnover, as shown in Figure S22.\nMeanwhile, we fully acknowledge that the regulatory relationships among SPAK,\nGSK3β, and c-Jun are complex and may differ across molecular subtypes of HCC. Our\nstudy does not aim to exclude the well-established role of AKT in mediating GSK3-S9\nphosphorylation, and we agree that in AKT-activated HCCs, AKT is the dominant\nregulator of GSK3β and this regulation contribute to AKT mediated\nhepatocarcinogenesis, as this point was supported by a recent study1. In the revised\nmanuscript, we have clarified this point in the Discussion section by explicitly\nacknowledging that the relevance of SPAK-mediated GSK3B phosphorylation may\ndepend on cellular contexts, including the level of AKT activation.\nRegarding the CD8⁺ CTL–mediated killing assays, we would like to note that\nblockade of PD-L1 partially restored CTL cytotoxicity against HCC cells, supporting\nthe conclusion that SPAK contributes to immune evasion at least partly through PD-\nL1–dependent mechanisms. To maintain scientific rigor, we have also expanded the\nDiscussion section to recognize that additional pathways may participate in SPAK-\nassociated immune resistance and that further investigation will be required.\nReviewer #3\nI have no further suggestions for this revised manuscript.\nResponse: We sincerely thank the reviewer for the positive evaluation and for taking\nthe time to assess our revised manuscript. We appreciate the reviewer’s supportive\ncomment."}]

Decision letter