Tumor-specific lncRNA IGF1R-AS1 trans-regulates chromatin interactions associated with oncogenic MYC signaling

Domain: chemistry
Published: 2026-03-19
Editorial outcome: major_revision

Abstract
LncRNAs have emerged as pivotal regulators in the development and progression of various human cancers. However, understanding the precise mechanisms by which lncRNAs influence cancer progression remains a substantial challenge, largely due to their cell type- and tissue-specific expression patterns and the lack of well-defined functional domains or motifs. In this study, we investigate the complex interplay between super-enhancers and lncRNAs through a comprehensive analysis of lncRNA expression in a cohort of metastatic castration-resistant prostate cancer patients. Our analysis identifies 1,344 lncRNAs, among which an antisense lncRNA in the IGF1R locus named IGF1R-AS1 displayed the strongest super-enhancer association. Through pan-cancer transcriptome analysis, we find that IGF1R-AS1 is specifically transcribed in tumor specimens and is overexpressed in prostate and lung cancers. Notably, we reveal a non-canonical trans-acting role for IGF1R-AS1 whereby it interacts with chromatin remodeling complexes and architectural proteins to facilitate long-range chromatin looping between distal MYC enhancers and its promoter, leading to MYC overexpression and enhanced tumorigenicity. Collectively, our findings elucidate a mechanism by which a tumor-specific trans-acting lncRNA modulates oncogenic MYC expression through long-range chromatin interactions, suggesting IGF1R-AS1 may play an important role in the pathogenesis of MYC-driven malignancies.

Introduction
Long non-coding RNAs (lncRNAs) play critical roles in the regulation of gene expression, particularly in cancer progression and lineage-specific oncogenic programs1. While high-throughput transcriptomic studies have uncovered thousands of lncRNAs, their context-specific functions and regulatory mechanisms remain incompletely understood. Many well-characterized cancer-associated lncRNAs, such as MALAT1, HOTAIR, and NEAT1, are broadly expressed across both normal and tumor tissues2. In contrast, some lncRNAs exhibit highly tissue- or tumor-specific expression patterns and tend to act locally (in cis), especially at super-enhancer regions where they can promote chromatin looping and transcriptional activation of nearby oncogenes3. For example, the colorectal cancer-associated lncRNA CCAT1, transcribed from a super-enhancer upstream of MYC, facilitates enhancer-promoter interactions that drive MYC overexpression4.

Despite such advances, the landscape of lncRNAs with tumor-specific expression, those absent in normal tissues but selectively activated in cancer, remains poorly characterized. This class of lncRNAs holds particular promise for cancer-specific targeting but is often missed in annotation databases due to their selective expression in tumor contexts5. A deeper understanding of tumor-specific lncRNAs and their mechanisms of action may offer unique opportunities for therapeutic intervention and disease monitoring.

While certain tumor-specific lncRNAs with cis-regulatory functions have been well characterized, the potential for tumor-specific lncRNAs to act in trans, regulating gene expression at distant genomic loci, remains less well understood. A particularly open question is whether trans-acting lncRNAs can modulate 3D chromatin architecture in a manner similar to their cis-acting counterparts. Although some lncRNAs have been shown to scaffold transcriptional regulators or influence RNA stability6, direct evidence of their role in mediating long-range chromatin interactions across genomic loci, especially in trans, is still limited.

In this study, we identify and characterize IGF1R-AS1, an antisense lncRNA with remarkably specific expression in prostate cancer and a subset of lung tumors. Unlike most lncRNAs that show expression across both normal and tumor tissues, IGF1R-AS1 is virtually absent from normal samples and significantly enriched in advanced disease, suggesting a tumor-specific and potentially oncogenic role. Functional analyses demonstrate that IGF1R-AS1 promotes cancer cell proliferation, migration, and survival, both in vitro and in xenograft models. Notably, IGF1R-AS1 depletion leads to downregulation of MYC and disruption of enhancer–promoter interactions at the MYC locus, without affecting IGF1R expression in cis. While the complete mechanism requires further investigation, our data supports a model where IGF1R-AS1 mediates long-range chromatin interactions between multiple cis-regulatory enhancer elements and the MYC promoter. These findings not only expand the catalog of tumor-specific lncRNAs but also reveal a mechanism through which non-coding RNAs can influence distal gene regulation and 3D genome architecture in cancer.

Results
Discovery of lncRNAs from transcriptome analysis of mCRPC
Understanding lncRNA-mediated transcriptional regulation in human cancers, particularly those lacking effective treatments, remains a critical research priority. To gain deeper insight into these regulatory mechanisms, we focused on metastatic castration-resistant prostate cancer (mCRPC), a lethal form of cancer, as our model system. We performed a meta-assembly of mCRPC transcriptomes by analyzing 101 poly-A purified RNA-seq samples obtained from the AACR-PCF Stand-Up-To-Cancer (SU2C) study7 (See Methods for details). This comprehensive analysis enabled us to detect recurrently expressed lncRNA transcripts in these mCRPC samples. We identified a total of 1,344 unannotated lncRNAs against the GENCODE gene annotation8 that were expressed in at least 5 of 101 (5%) mCRPC samples (Fig. 1a, Supplementary Data 1). These unannotated lncRNAs were evolutionarily less conserved than protein-coding genes but were comparable to annotated lncRNAs (Fig. 1b). To ensure that these unannotated lncRNA candidates did not encode proteins, we considered assembled transcripts as lncRNAs when they were predicted to have no coding potential by five widely used tools (See Methods for details). We observed the unannotated transcripts have a lower coding potential relative to protein-coding genes but similar coding potential to annotated lncRNAs (Supplementary Fig. 1a). In addition, these unannotated lncRNAs tended to have fewer exons and shorter transcript lengths than protein-coding genes (Supplementary Fig. 1b, 1c).

To further validate the expression of these unannotated lncRNAs, we leveraged the publicly available ENCODE ChIP-seq data of epigenetic signatures (H3K4me3, H3K27ac, Pol II binding) that are associated with active transcription start sites. We observed the H3K4me3 histone modification, an active promoter marker, was highly enriched at the predicted start sites of these lncRNAs (Fig. 1c). Moreover, these lncRNAs showed greater H3K4me3 enrichment in prostate cancer cells compared to other cancer types (Supplementary Fig. 1d), suggesting their potential association with prostate cancer. We further validated the transcription of these unannotated lncRNAs by analyzing RNA Pol II and H3K27ac ChIP-seq signals (Supplementary Fig. 1e-f) and by examining aggregate RNA-seq read density along the full length of all these lncRNAs (Supplementary Fig. 1g) in VCaP prostate cancer cells.

We adapted criteria from a previous study9 to classify these unannotated lncRNAs into four categories, which revealed that most of them are intergenic relative to protein-coding genes (Fig. 1d). In addition, we compared these unannotated lncRNAs with those reported by the MiTranscriptome study10, which is a catalog of human lncRNAs derived from computational analysis of RNA-seq data from over 6,500 samples spanning diverse cancer and tissue types. This comparison revealed that fewer than a quarter of these unannotated lncRNAs were captured by MiTranscriptome (Fig. 1e).

Identification of mCRPC-associated lncRNA IGF1R-AS1 through super-enhancer analysis
To investigate the functions of these unannotated lncRNAs in mCRPC, we compared RNA-seq data from our mCRPC cohort with RNA-seq data that were generated from normal prostate (n=52) and localized prostate cancer (n=500) by The Cancer Genome Atlas (TCGA) study11. We focused on the lncRNAs differentially expressed in mCRPC compared to normal prostate tissues, which revealed annotated mCRPC-associated lncRNAs as well as unannotated lncRNA candidates that were primarily highly expressed in mCRPC (Fig. 2a). Next, we sought to understand the potential functions of these unannotated lncRNAs by linking their expression to super-enhancers (SEs) in VCaP cells, which were identified by assessing the H3K27ac ChIP-seq density with the ROSE algorithm12. This analysis revealed SEs overlapping known prostate cancer protein-coding genes and lncRNAs (e.g., AR, TMPRSS2, FOXA1, ERG, PCAT2, NEAT1, PCAT1), as well as a subset of unannotated lncRNAs we had identified (n=111) (Fig. 2b). Among them, we focused on the highest-ranked SE-associated unannotated lncRNA, an antisense transcript located within the intronic region of IGF1R gene, hereafter named IGF1R-AS1 (Fig. 2b). IGF1R-AS1 was one of the most highly expressed SE-overlapping genes in comparisons of localized prostate cancer vs. benign tissue and mCRPC vs. localized prostate cancer (Fig. 2c), nominating it as an unannotated lncRNA for further investigation.

We noted that GENCODE annotated two antisense transcripts (ENSG00000287191) around the region where IGF1R-AS1 is located. However, these annotated transcripts displayed isoform structures distinct from the IGF1R-AS1 isoform predicted by StringTie (Fig. 2d). To address this discrepancy, we performed 3’ rapid amplification of cDNA ends (3’RACE) using a forward primer targeting exon 1 of IGF1R-AS1. We first conducted 3’RACE reactions with RNA from VCaP cells, which express high levels of IGF1R-AS1 (Supplementary Fig. 2a), and subjected the resulting 3’RACE products to PacBio Iso-seq long-read sequencing. This approach revealed several full-length IGF1R-AS1 isoforms (Supplementary Table 1), most of which shared a splicing junction between exon 1 and exon 2, with splice donor and acceptor sites consistent with those predicted by StringTie (Fig. 2d). We also analyzed the Nanopore direct RNA sequencing data in VCaP cells13 and confirmed the consistency of IGF1R-AS1 integrity and size (Supplementary Fig. 2b). Furthermore, we observed that the major IGF1R-AS1 isoforms identified in VCaP cells were also expressed in the lung cancer cell line H727, another cancer cell line with high endogenous expression of IGF1R-AS1 (Supplementary Fig. 2c, Supplementary Table 1). Collectively, these findings confirm that IGF1R-AS1 transcripts are expressed in multiple cancer cell lines.

To further explore the association of IGF1R-AS1 with SEs, we examined the H3K27ac ChIP-seq data of VCaP cells and identified a 20kb SE downstream of IGF1R-AS1, co-occupied by RNA Pol II and the transcriptional co-activator BRD4 (Fig. 2e). Furthermore, RNA Pol II ChIA-PET data from VCaP cells revealed direct interactions between this SE and the enhancer upstream of IGF1R-AS1, marked by H3K4me3 and H3K27ac modifications (Fig. 2e). However, this SE-enhancer interaction was not detected in LNCaP and DU145 cells based on RNA Pol II ChIA-PET data (Supplementary Fig. 2d), which may explain the absence of IGF1R-AS1 expression in these cells, as indicated by RNA-seq measurements (Supplementary Fig. 2a). To further confirm whether this SE induces IGF1R-AS1 expression, we treated VCaP cells with bromodomain inhibitor JQ1 to reduce the BRD4 occupancy in this SE region (Fig. 2f), thereby selectively disrupting the expression of genes associated with active SEs. Notably, JQ1 treatment led to a significant reduction in the expression levels of both IGF1R-AS1 and MYC, a well-characterized SE-associated gene, while IGF1R expression remained unaffected (Fig. 2g). Additionally, we analyzed existing RNA-seq data from JQ1-treated and untreated H727 lung cancer cells14. Consistent with our findings in VCaP cells, IGF1R-AS1 expression decreased in JQ1-treated H727 cells, whereas IGF1R expression remained stable (Supplementary Fig. 2e). This result was further validated by qPCR analysis (Supplementary Fig. 2f).

Next, we sought to identify transcription factors (TFs) targeting the IGF1R-AS1-associated SE to understand the regulatory mechanisms underlying its expression. Analysis of publicly available ChIP-seq binding sites revealed enrichment of androgen receptor (AR) and its co-factors (FOXA1 and HOXB13) in this SE region (Supplementary Fig. 2g). We confirmed the involvement of AR as a positive regulator of IGF1R-AS1 expression by suppressing AR with the antagonist enzalutamide and stimulating AR with the agonist dihydrotestosterone (DHT). However, we observed similar responses of IGF1R to these manipulations as IGF1R-AS1 (Supplementary Fig. 2h-i), suggesting that regulation of IGF1R-AS1 by AR is not exclusively mediated through this specific IGF1R-AS1-associated SE. Therefore, we investigated the next top-ranked TF, SMARCA4 (Supplementary Fig. 2g), a chromatin remodeler known to interact with AR15. We confirmed SMARCA4 binding to the IGF1R-AS1-associated SE in VCaP cells, while no binding was detected in the 22RV1 and LNCaP ChIP-seq data (Fig. 2h). This suggests that the hyper-expression of IGF1R-AS1 in VCaP cells may indeed be attributed to SWI/SNF-mediated SE regulation. To further validate this hypothesis, we examined the chromatin accessibility at the IGF1R-AS1-associated SE and its distal enhancer using publicly available ATAC-seq data. Our results showed that SMARCA4 depletion disrupted chromatin accessibility specifically at the IGF1R-AS1-associated SE (Fig. 2h). Consequently, SMARCA4 inactivation markedly disrupted the three-dimensional interactions between the IGF1R-AS1-associated SE and its distal enhancer (Fig. 2i), as shown by the analysis of a published H3K27ac HiChIP dataset upon treatment with a PROTAC SWI/SNF degrader (AU-15330)16. Additionally, ATAC-seq analysis revealed diminished accessibility at the IGF1R-AS1 promoter (Fig. 2i). This observation aligns with the reduction in IGF1R-AS1 expression upon SMARCA4 degradation (Supplementary Fig. 2j) or depletion, whereas IGF1R RNA and protein levels remained relatively stable in both VCaP and H727 cells (Supplementary Fig. 2k-n). These data collectively support a model in which IGF1R-AS1 expression is influenced by SWI/SNF complex-mediated SE regulation.

Cancer-specific IGF1R-AS1 functions in trans to upregulate MYC signaling independently of IGF1R
We hypothesized that the tissue- and disease-specific expression of lncRNAs are the basis for the absence of certain lncRNAs, such as IGF1R-AS1, from lncRNA databases. To investigate this further, we conducted a comprehensive analysis of IGF1R-AS1 expression across multiple tissues using RNA-seq data from the TCGA pan-cancer cohort. We compiled data from 9,057 RNA-seq samples across 22 cancer types, comprising 8,750 tumor samples and 307 normal samples. Our analysis revealed that IGF1R-AS1 exhibited pronounced specificity for prostate tissue and cancer, while also showing elevated expression in lung adenocarcinoma compared to other cancer types (Fig. 3a). Moreover, we examined the top three IGF1R-AS1-high TCGA prostate specimens for which tumor and matched adjacent normal tissue RNA-seq data were available. This tumor/normal matched comparison further confirmed the cancer-specific expression of IGF1R-AS1 (Fig. 3b), indicating its potential oncogenic functions. Consistent with this observation, we found that IGF1R-AS1 expression was significantly higher in localized and metastatic tumors compared to benign tissues (Fig. 3c). Assessment of the IGF1R-AS1 feature junction revealed increased prevalence in metastatic compared with localized tumors (Supplementary Fig. 3a).

To validate IGF1R-AS1’s oncogenic potential, we knocked down IGF1R-AS1 expression through RNA interference (shRNAs or siRNAs) in VCaP, 22RV1, and H727 cells (Fig. 3d and Supplementary Fig. 3b). Cell growth rates of VCaP, 22RV1, and H727 were decreased in IGF1R-AS1 knockdown cells compared to control cells (Fig. 3e and Supplementary Fig. 3c). Both cell migration and invasion were significantly reduced in IGF1R-AS1 knockdown cells compared to those in control cells (Fig. 3f-g and Supplementary Fig. 3d). Restoring IGF1R-AS1 expression in VCaP knockdown cells reversed the inhibited cell growth, migration, and invasion (Supplementary Fig. 3e-g). Overexpressing IGF1R-AS1 in low-expressing LNCaP cells resulted in a moderate increase in cell growth, migration, and invasion (Supplementary Fig. 3h-j). We then investigated whether IGF1R-AS1 affects the growth of VCaP cells grown as xenografts in immunocompromised mice. Growth of the IGF1R-AS1-knockdown xenografts was significantly suppressed compared to the growth of control xenografts (Fig. 3h), resulting in a delay in tumors reaching the experimental endpoint (Fig. 3i). Similar tumor growth suppression was also observed in 22RV1 xenografts with IGF1R-AS1 knockdown (Supplementary Fig. 3k-n). These results establish that IGF1R-AS1 is required for the growth of IGF1R-AS1-positive prostate and lung cancer cells in vitro and in vivo.

To dissect how IGF1R-AS1 contributes to cancer progression, we conducted RNA-seq in VCaP cells with IGF1R-AS1 knockdown and compared the results to control cells. Gene Set Enrichment Analysis (GSEA) revealed that genes down-regulated by IGF1R-AS1 knockdown were enriched in cell-cycle-related biological processes, including "G2M checkpoint”, "MYC targets”, and "E2F targets" (Fig. 3j). We further validated these GSEA findings by implementing a previously-reported TF activity analysis17, which nominated MYC as the primary dysregulated TF in IGF1R-AS1 knockdown cells (Fig. 3k). Additionally, we observed significant enrichment of MYC targets among IGF1R-AS1-regulated genes in prostate cancer tissue samples (Supplementary Fig. 3o). Consistent with RNA-seq analysis, both the transcript and protein levels of MYC decreased after IGF1R-AS1 knockdown in VCaP, 22RV1, and H727 cells, while the expression of IGF1R and IGF1R nearby genes SYNM and TTC23 were not dramatically affected (Fig. 3l-m, and Supplementary Fig. 3p-s). Reintroducing IGF1R-AS1 in IGF1R-AS1-knockdown VCaP cells rescued the expression of MYC (Supplementary Fig. 3t-u), and overexpressing IGF1R-AS1 in LNCaP cells also increased the expression of MYC (Supplementary Fig. 3v-w). Additionally, MYC-positive targets decreased significantly in IGF1R-AS1 knockdown cells, while MYC-negative targets increased (Supplementary Fig. 3x). These findings highlight a non-canonical role for lncRNA IGF1R-AS1, which does not regulate IGF1R in cis, but functions in trans to regulate MYC.

IGF1R-AS1 interactome analysis reveals its interaction with chromatin remodelers
To explore the molecular mechanisms underlying the oncogenic role of IGF1R-AS1, we began by assessing its subcellular localization using fluorescence in situ hybridization (FISH) with custom-designed probes targeting IGF1R-AS1 in VCaP cells (Fig. 4a). The FISH analysis revealed that IGF1R-AS1 is primarily localized in the nucleus (Fig. 4a), suggesting its potential involvement in nuclear processes such as chromatin regulation. To confirm the probe's specificity, we performed FISH after siRNA-mediated IGF1R-AS1 silencing, which showed an almost complete absence of nuclear signal, validating the initial observation (Fig. 4a, Supplementary Fig. 4a-b).

We next performed an RNA pull-down assay followed by mass spectrometry to define the IGF1R-AS1 interactome in VCaP cells (Fig. 4b). After excluding low-confidence candidates, we identified 302 proteins associated with biotin-labeled IGF1R-AS1 (Supplementary Data 2). Functional annotation of the IGF1R-AS1 interactome revealed the enrichment of ATP-dependent chromatin remodelers (Supplementary Fig. 4c), involving distinct chromatin remodeling complex subfamily members (Fig. 4c). Subsequent immunoblot analyses of the lncRNA-bound proteins (Fig. 4d and Supplementary Fig. 4d) and antibody-based RNA immunoprecipitation followed by qPCR (Fig. 4e and Supplementary Fig. 4e-f) validated the interactions between IGF1R-AS1 and both SMARCA4 and SMARCA1, representing the SWI/SNF and ISWI chromatin remodeling complexes, respectively. To determine whether these interactions are direct, we performed in vitro binding assays using biotin-labeled IGF1R-AS1 and recombinant His-tagged SMARCA4 or Flag-tagged SMARCA1 proteins, confirming direct binding (Fig. 4f–g). To map IGF1R-AS1 regions mediating these interactions, we predicted its secondary structure (Fig. 4h) and conducted RNA pull-down assays with truncated IGF1R-AS1 fragments (Fig. 4i). This analysis revealed that nucleotides 512–645 bind SMARCA4, whereas nucleotides 177–407 and 408–511 interact with SMARCA1 (Fig. 4i). Both SMARCA4 and SMARCA1 contain an ATPase domain within their central region. To identify the protein domains responsible for IGF1R-AS1 binding, we cloned the N-terminal, ATPase, and C-terminal domains of SMARCA4 and SMARCA1 into Flag-tagged lentiviral constructs, expressed them in VCaP cells, and performed RIP using anti-Flag antibody followed by qPCR (Fig. 4j–k and Supplementary Fig. 4g–h). IGF1R-AS1 was predominantly associated with the ATPase domain of SMARCA4 (Fig. 4j) and the C-terminal domain of SMARCA1 (Fig. 4k).

To examine whether IGF1R-AS1 regulates the MYC pathway through its interactions with the SWI/SNF and ISWI complexes, we conducted RNA sequencing in VCaP cells with SMARCA4 or SMARCA1 knockdown, alongside control cells. GSEA analysis revealed that MYC targets were among the top-ranked hallmark gene signatures associated with SMARCA4 and SMARCA1 (Fig. 4l-m). We further validated these findings by showing that depletion of either SMARCA4 or SMARCA1 significantly reduced MYC expression at both the transcriptional and protein levels in VCaP, H727, and 22RV1 cells (Fig. 4n-q, and Supplementary Fig. 4i-l). Additionally, knockdown of SMARCA4 or SMARCA1 led to a reduction in cell proliferation of VCaP, H727, and 22RV1 cells (Fig. 4r-s, and Supplementary Fig. 4m-n), consistent with previous reports16,18. These findings suggest that the modulation of MYC signaling by IGF1R-AS1 involves its interactions with chromatin remodelers.

IGF1R-AS1 expression induces extensive chromatin changes, with a predominant impact on the MYC promoter-enhancer interaction network
As IGF1R-AS1 interacts with chromatin remodelers, we hypothesized that it influences chromatin accessibility, potentially leading to aberrant MYC expression. To investigate this, we conducted ATAC-seq experiments in VCaP cells transfected with IGF1R-AS1 siRNA or control siRNA. We identified 12,808 sites with significantly increased accessibility and 9,244 sites with decreased accessibility due to siRNA-mediated IGF1R-AS1 depletion (Fig. 5a). To map which genomic regions are co-regulated by IGF1R-AS1 and chromatin remodelers, we leveraged publicly available SMARCA4 ChIP-seq data in VCaP cells that enabled genome-wide identification of direct SWI/SNF binding sites (Fig. 5b). By comparing ATAC-seq peak density at these SMARCA4 binding sites, we distinguished between accessible regions directly influenced by IGF1R-AS1's chromatin remodeling activity and those driven by other factors. Notably, 41.1% of the ATAC-seq peaks that were decreased by IGF1R-AS1 knockdown (3,800 sites) display a significant overlap with SMARCA4 binding sites (p < 0.0001, hypergeometric test). These decreased peaks exhibited the strongest association with SMARCA4 binding, surpassing both the increased and retained sites (Supplementary Fig. 5a). Additionally, we observed a widespread loss of ATAC-seq signals at the 3,800 genomic sites in VCaP cells that displayed a decrease in peak signal following treatment with a SWI/SNF PROTAC16 (Fig. 5b). Genomic annotation further revealed that over 86% of these sites are enhancers (distal intergenic and intronic regions) (Supplementary Fig. 5b), suggesting a role for IGF1R-AS1 in regulating SWI/SNF-dependent enhancer accessibility.

To identify genes regulated by IGF1R-AS1-associated enhancers, we developed an algorithm for constructing a gene-peak regulatory network. This approach integrated differentially accessible peaks from ATAC-seq data, differentially expressed genes from RNA-seq data, and enhancer-promoter interactions from high-throughput chromatin contact mapping (Fig. 5c, See Methods for details). Our analysis revealed 1,158 gene-peak connections involving 674 unique peaks and 665 genes genome-wide (Supplementary Fig. 5c). IGF1R-AS1 appeared to regulate gene-peak subnetworks characterized by multiple enhancer-promoter connections, forming promoter-enhancer hubs19. Of these, 176 genes (26.5%) were linked to five or more peaks (Supplementary Fig. 5d). To identify key promoter-enhancer hubs mediated by IGF1R-AS1, we assessed each network node's influence using eigencentrality analysis (See Methods for details). A high eigencentrality score indicates that a node has dense connections to other highly scoring nodes, signifying a hub of high transcriptional activity. MYC emerged as the highest-scoring gene, residing within a subnetwork containing 19 distinct enhancer nodes (Fig. 5d-e). Notably, 17 of these MYC-connected enhancer nodes ranked among the top 20 for eigencentrality across all enhancer nodes (Supplementary Fig. 5e). Similar network analysis of SMARCA4, the IGF1R-AS1 interacting partner, also identified MYC as the top-ranked gene (Supplementary Fig. 5f), reinforcing MYC's position as the primary promoter-enhancer hub regulated by IGF1R-AS1. Additionally, network analysis of SMARCA1 knockdown ATAC-seq similarly identified MYC among the top-ranked genes (Supplementary Fig. 5g), consistent with both SWI/SNF and ISWI complexes contributing to IGF1R-AS1-mediated regulation of MYC.

To validate IGF1R-AS1's regulation of MYC expression and enhancer activities across multiple cancer models, we analyzed a publicly available dataset containing paired ATAC-seq and RNA-seq data from 40 samples, including 22 mCRPC organoids, 6 patient-derived xenografts and 12 derived or traditional prostate cancer cell lines20. Focusing on the top 10 MYC-associated enhancers identified in the IGF1R-AS1 network, we classified samples into low and high MYC enhancer activity groups using K-Means clustering of ATAC-seq signals (Fig. 5f). Notably, IGF1R-AS1-positive samples predominantly clustered in the high MYC enhancer activity group (5/13, 39%, vs. 3/27, 11%), including VCaP cells. IGF1R-AS1 expression was significantly elevated in this group (Supplementary Fig. 5h). Consistently, IGF1R-AS1-positive samples displayed increased MYC enhancer accessibility and expression (Supplementary Fig. 5i-j), aligning with their higher representation in the high MYC enhancer activity group. Reconstructing the IGF1R-AS1 regulatory network using these samples’ ATAC-seq and RNA-seq data confirmed MYC as the primary hub gene (Supplementary Fig. 5k), consistent with findings in VCaP cells.

To evaluate the clinical relevance of IGF1R-AS1-regulated MYC transcription, we analyzed RNA-seq data from 499 mCRPC tumor biopsies profiled using the Tempus xR assay (Tempus AI, Inc., Chicago, IL)21,22. As this assay employs a hybrid capture approach to profile the transcriptome, it cannot directly detect IGF1R-AS1 expression due to the lncRNA’s unannotated status. To address this, we developed an IGF1R-AS1 activity score based on the gene signature derived from its regulatory network (See Methods for details). High IGF1R-AS1 activity was significantly associated with poor overall survival (p < 0.0001; Fig. 5g). Stratifying patients by both IGF1R-AS1 activity and MYC hallmark gene signature revealed that individuals with high levels of both had the worst clinical outcomes, with IGF1R-AS1 activity remaining a significant predictor of survival in this stratified analysis (p = 0.0011; Fig. 5h). Independent validation in a microarray based primary prostate cancer cohort23 and SU2C mCRPC cohort24 confirmed that high IGF1R-AS1 network activity predicts worse disease-free and overall survival, respectively (Supplementary Fig. 5l-m). Additionally, IGF1R-AS1 further stratified outcomes among MYC-high patients and showed superior prognostic performance compared to Prolaris cell-cycle progression markers25-28 (Supplementary Fig. 5m).

A prior study indicates that concurrent high MYC and low AR transcriptional programs promote prostate cancer progression toward metastatic disease29. Analysis of our Tempus mCRPC cohort confirmed these findings, with MYC-high/AR-low patients showing significantly worse survival (p < 0.0001; Supplementary Fig. 5n). Notably, IGF1R-AS1 activity was elevated in this aggressive subgroup (Supplementary Fig. 5o), suggesting that the IGF1R-AS1-mediated regulation of MYC transcription may contribute to driving this molecular subtype in mCRPC. Taken together, these findings suggest that IGF1R-AS1 amplifies the aggressiveness of MYC-driven tumors, identifying a subset of particularly high-risk patients.

IGF1R-AS1 orchestrates CTCF-anchored chromatin loops connecting MYC promoter with distal cis-regulatory enhancer elements
Given the role of IGF1R-AS1 in regulating MYC enhancer activities, we hypothesized that it modulates chromatin looping between MYC enhancers and its promoter. Our data showed that architectural proteins CTCF and cohesin subunit SMC1A were part of the IGF1R-AS1 interactome (Supplementary Data 2), indicating a potential link between CTCF/cohesin-mediated looping and IGF1R-AS1. To explore this, we conducted quantitative chromosome conformation capture (3C-qPCR) in IGF1R-AS1 knockdown and control VCaP cells to assess interaction frequencies between the MYC promoter and its enhancers. We chose two IGF1R-AS1-regulated MYC enhancers spanning the PCAT1-PRNCR1 loci (E1 and E2), which are known prostate cancer-associated cis-regulatory elements (CREs)30,31. We tested another enhancer region at the CCAT1 locus (E3), which is among the top five affected MYC enhancers along with E1 and E2 predicted by our network analysis (Supplementary Fig. 5e). We also included a negative control (NC) region lacking evidence of interaction with MYC promoter and a positive control (PC) region with a previously validated interaction with MYC promoter32. Quantitative 3C analysis revealed that IGF1R-AS1 depletion had differential effects on MYC promoter-enhancer interactions: while maintaining most interactions with PC, E1, and E2 regions, it caused a complete loss of interaction with the E3 enhancer region (Fig. 6a). These findings suggest that IGF1R-AS1 selectively influences long-range chromatin interactions within the MYC regulatory network, with particularly critical impact on the loop connecting the MYC promoter with the E3 enhancer in the CCAT1 locus.

A previous study in colon cancer demonstrated that CCAT1-L lncRNA, a CCAT1 isoform with longer extra 3’ extension, can interact with CTCF to enhance CTCF-mediated looping between the CCAT1 locus, where E3 is located, and the MYC locus4. Another study also reported that CCAT1-5L, a CCAT1 isoform with an extra 5′ extension, could regulate MYC transcription in HeLa cells via interaction with MYC promoter RNAs and eRNA PVT1 to modulate long-range chromatin looping33. However, CCAT1 and its long isoforms are not expressed in prostate cancer cell lines including VCaP cells (Supplementary Fig. 6a) and CCAT1 exhibits very low expression levels across prostate cancer tissue samples compared to other cancers (Supplementary Fig. 6b). Despite this, E3 accessibility remains correlated with MYC expression across TCGA pan-cancer cohorts, including prostate cancer, as demonstrated by the correlation analysis of publicly available ATAC-seq and RNA-seq data from TCGA samples34 (Supplementary Fig. 6c). Furthermore, E3 accessibility is significantly elevated in IGF1R-AS1 positive prostate cancer samples (Supplementary Fig. 6d). These findings suggest that the chromatin loop between E3 and the MYC locus in prostate cancer is likely trans-regulated by IGF1R-AS1, rather than driven by cis-regulation through CCAT1 or its isoforms. To assess whether this E3-MYC chromatin loop is CTCF-mediated, we analyzed publicly available ChIP-seq data for CTCF and the cohesin subunit RAD21. Our analysis revealed that both CTCF and cohesin bind to the E3 region across different cell types, with CTCF showing the strongest binding in VCaP cells (Supplementary Fig. 6e). Furthermore, CTCF HiChIP data from VCaP cells confirmed strong interactions between E3 and the MYC promoter (Fig. 6b), providing direct evidence that this long-range looping is CTCF-mediated.

To further investigate how IGF1R-AS1 impacts chromatin loops, we conducted ATAC-seq footprinting analysis for TFs using binding profiles annotated by the JASPAR database35. Aggregate footprint analysis showed that IGF1R-AS1 depletion globally reduced CTCF binding at open chromatin regions co-regulated by IGF1R-AS1 and SMARCA4 (Supplementary Fig. 6f). Given that chromatin remodelers regulate CTCF-mediated 3D architecture36,37 and trans-acting lncRNAs can direct the SWI/SNF complex to specific enhancers38, we hypothesized that IGF1R-AS1 enhances the recruitment of chromatin remodelers and CTCF to MYC enhancers, thereby facilitating CTCF/cohesin-mediated loops between MYC enhancers and promoters. Supporting this, analysis of CTCF ChIP-seq and HiChIP data from SWI/SNF PROTAC-treated VCaP cells showed that SWI/SNF inactivation notably disrupted CTCF binding and reduced CTCF-mediated chromatin loops connecting MYC promoter with distal CREs, including the E3 enhancer region (Fig. 6b). Consistently, IGF1R-AS1 knockdown reduced the binding of SMARCA4, SMARCA1, CTCF, and the cohesin subunit SMC1A at the E3 enhancer (Fig. 6c), without altering their protein abundance (Supplementary Fig. 6g). To more comprehensively evaluate CTCF-mediated chromatin interactions, we performed CTCF HiChIP in control and IGF1R-AS1–depleted VCaP cells. CTCF HiChIP confirmed that IGF1R-AS1 depletion disrupted MYC promoter–upstream enhancer loops (Fig. 6d). Genome-wide aggregate peak analysis (APA) demonstrated a significant reduction in CTCF loop strength following IGF1R-AS1 knockdown (Fig. 6e), accompanied by decreased CTCF signal enrichment across all chromosomes (Fig. 6f). Previous investigations demonstrated that CTCF and SMARCA4 physically interact and colocalize to enhancers and regulate gene expression36,39,40. Our IGF1R-AS1 interactome analysis indicates that CTCF binds IGF1R-AS1 (Supplementary Data 2). We validated this interaction through CTCF RIP-qPCR (Fig. 6g) and IGF1R-AS1 RNA pulldown, which further showed that IGF1R-AS1 interacts with CTCF through nucleotides 177-407 (Fig. 6h), whereas it interacts with SMARCA4 through nucleotides 512-645 (Fig. 4h). Furthermore, CTCF co-IP demonstrated that IGF1R-AS1 knockdown reduced SMARCA4–CTCF interaction (Fig. 6i), indicating that IGF1R-AS1 acts as a scaffold that stabilizes this interaction.

Collectively, these findings reveal a mechanism by which the trans-acting lncRNA IGF1R-AS1 promotes long-range chromatin looping to activate oncogenic MYC transcription by acting as a scaffold to increase the interaction between SMARCA4 and CTCF (Fig. 6j).

Methods
Transcriptome assembly of the SU2C cohort prostate cancer patient samples
The quality of the polyA-enriched RNA-seq data of 101 metastatic prostate cancer samples in the SU2C cohort was initially assessed using FastQC (version 0.11.8), with all raw reads meeting the required quality standards. The RNA-seq reads were then aligned to the human reference genome (hg38) using HISAT2 (version 2.2.1)50, with the strand parameter set to RF (option ‘rna-strandness’). Based on the aligned reads, the transcriptome was reconstructed using StringTie (version 1.3.6)51, with reference annotations (option ‘-G’) from GENCODE (v37) and the strand parameter (option ‘--rf’) set to RF for each of the 101 samples. StringTie’s merge mode was then used to integrate all 101 meta-assemblies into a final transcriptome.

LncRNA identification
To identify a high-confidence set of lncRNAs not annotated by GENCODE, several stringent filters were applied: 1) Filtering Overlapping Transcripts: Transcripts that overlapped with low complexity regions or ENCODE blacklist regions were discarded. The low complexity regions were sourced from a public dataset available at Figshare52 and ENCODE blacklist regions were obtained from ENCODE53; 2) Length and Strand Information: Transcripts shorter than 200 bp or lacking strand information were excluded from further analysis; 3) Expression Level: Transcript expression levels were calculated using StringTie. Transcripts with low expression levels (FPKM < 1.0) and those appearing in fewer than five samples were removed; This prevalence threshold was selected to focus on recurrently expressed lncRNAs across multiple patients while filtering sporadic detections likely representing technical noise. 4) Proximity Filtering: Single-exon transcripts proximal (within 2000 bp) to protein-coding genes or other noncoding RNAs on the same strand were filtered out to eliminate fragments of annotated RNA; 5) Overlap with Annotated Genes: Transcripts that overlapped with the exons of protein-coding genes or noncoding RNAs, including canonical noncoding RNAs and long lncRNAs annotated by GENCODE (v37), were removed. Canonical noncoding RNAs included rRNA, tRNA, miRNA, snRNA, snoRNA, misc_RNA, sRNA, scaRNA, vaultRNA, ribozyme, and mitochondrial tRNA and rRNA. Two biotypes defined as ‘long non-coding RNAs’ by GENCODE included: 'lncRNA' and ‘TEC’. Some lncRNAs have been reclassified as protein-coding genes in recent GENCODE annotation versions. GFFcompare54 was used to compare the assembled transcripts with the reference annotations; 6) Coding Potential Analysis: To verify that the candidate lncRNAs were non-coding, five computational methods were employed: CPAT55, CPC256, lncADeep57, CNIT58, and txCdsPredict from UCSC. CPC2, lncADeep, and CNIT provided predicted labels (coding/noncoding) for the transcripts. CPAT used a coding probability cutoff of 0.364, while txCdsPredict used a score cutoff of 800 to determine coding potential. Only transcripts consistently predicted to be noncoding by all five tools were retained.

Identified lncRNA were classified into four categories: intergenic, overlapping, intronic and exonic based on their relationship with protein-coding genes. The classification scheme was adopted from Derrien et al59. PhastCons scores60 obtained from the UCSC genome browser were used to evaluate the evolutionary conservation for protein-coding genes, annotated lncRNAs and unannotated lncRNAs.

Differentially Expressed lncRNA Identification in mCRPC Compared to Normal Prostate Tissues
RNA sequencing data from 101 mCRPC samples in the SU2C cohort were processed described above. Aligned BAM files from the TCGA-PRAD cohort were retrieved from the Genomic Data Commons (GDC)61 data portal. Gene-level read quantification was performed using FeatureCounts (version 2.0.3)62 software, utilizing GENCODE (v37) gene annotations. In addition to reference annotations, lncRNAs identified in this study were incorporated into the analysis. Differential gene expression analysis was conducted using the edgeR63 statistical package. LncRNAs were considered differentially expressed based on stringent criteria: false discovery rate (FDR) < 0.05 and absolute log2 fold change (|Log2FC|) > 1.5 when comparing mCRPC samples to normal prostate tissues.

ChIP-seq and super-enhancer analysis
All ChIP-seq datasets were reprocessed by aligning the raw reads to the GRCh38 reference genome using BWA-MEM64 (version 0.7.17) with default parameters. Normalized alignment coverage files were generated using the bamCoverage module from deepTools65 (version 3.5.1) with parameters: --binSize 20, --normalizeUsing CPM, and --smoothLength 60. The deepTools modules computeMatrix and plotProfile were used to visualize ChIP signal at the center of target regions, including peak summits and transcription start sites (TSS).

For integration with IGF1R-AS1 ATAC-seq analysis, SMARCA4 ChIP-seq data for VCaP was obtained from a previous study15. Raw FASTQ files were processed using the ENCODE ChIP-seq transcription factor pipeline (version 2.2.2). Reads were aligned with BWA-MEM, and peaks were called for each replicate using SPP66. Peak sets were analyzed and pooled for Irreproducible Discovery Rate (IDR) analysis, with optimal IDR peaks selected for downstream analyses.

Super-enhancers (SEs) were identified using the Rank Ordering of Super-Enhancers (ROSE) algorithm12. H3K27ac ChIP-seq peaks were initially used to define enhancers, applying the following criteria: peaks within ±3 kb of TSSs were excluded. Remaining H3K27ac peaks were designated as putative enhancers. Enhancers within ±12.5 kb of each other were stitched together, scored, and ranked based on H3K27ac ChIP-seq signal intensity. Enhancer rank was plotted against enhancer density, and regions above the inflection point of the curve were defined as SEs. ROSE algorithm default parameters were used to assign SEs and typical enhancers to genes. To visualize ChIP-seq signal at SE regions, averaged profiles were generated with ngsplot67. Genes were assigned to SEs using the ROSE program if their TSS was located within 50 kb of an SE.

IGF1R-AS1 3’ rapid amplification of cDNA ends (3’-RACE)
RNA extracted from VCaP and H727 cells was subjected to 3’-RACE using a second-generation 5’/3’ RACE kit (Roche, Cat. No. 03353621001) according to the manufacturer’s instructions. Briefly, 1 µg total RNA was used for first-strand cDNA synthesis using the oligo (dT)-anchor primer provided in the kit. An aliquot of the cDNA reaction was amplified by PCR using Quanta AccuStart II PCR SuperMix (Quanta Biosciences, 95137). The PCR utilized a forward primer anchored near the beginning of IGF1R-AS1 exon 1 (5’-ACTATGTTTCAGCGGCTGGT-3’) and a reverse primer provided by the kit (5’-GACCACGCGTATCGATGTCGAC-3’). The final 3’ RACE products were purified using a QIAquick PCR Purification Kit (Qiagen, 28104).

PacBio Iso-Seq sequencing and data analysis
The amplified 3′-RACE products from VCaP and H727 cells were submitted to the University of Minnesota Genomics Center, where they were converted into barcoded SMRTbell libraries using the Pacific Biosciences (PacBio) protocol for barcoded adapters for multiplex SMRT sequencing. The barcoded SMRTbell libraries were pooled and prepared for diffusion loading onto a PacBio Sequel instrument for sequencing. Raw data were processed using the CCS tool (version 6.0.0, https://ccs.how/) to generate circular consensus sequence (CCS) reads from the subreads generated during sequencing. LIMA (version 2.0.1, https://lima.how/) was then used for primer removal and demultiplexing, yielding full-length (FL) reads. ISOSEQ3 (version 3.4.0, https://isoseq.how/) was employed to refine these FL reads by removing polyA tails and artificial concatemers to produce full-length non-concatemer (FLNC) reads, which were further clustered into high-quality transcript isoforms. These isoforms were mapped to the GRCh38 human reference genome using minimap268 (version 2.17). Finally, we used SQANTI69 to classify and quantify the full-length transcript isoforms.

Expression analysis of IGF1R-AS1 and CCAT1 across cell lines and patient cohorts
The expression level of IGF1R-AS1 was quantified using RNA-seq data from tumor and normal tissue samples across TCGA cohorts, accessed via the GDC data portal61. Briefly, samples with IGF1R-AS1 characteristic junction (chr15: 98839896-98843675) were identified using RegTools (version 0.4.0)70. Next, for the samples with IGF1R-AS1 characteristic junction, the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) value of IGF1R-AS1 were calculated using FeatureCounts62 and in-house script based on the GTF annotation file concatenating GENCODE (v37) and IGF1R-AS1 isoforms identified from PacBio Iso-Seq data. Pan-cancer expression levels of CCAT1 were quantified using batch-corrected normalized expression values from UCSC Xena data platform71.

IGF1R-AS1 expression was further evaluated across prostate cancer cell lines (LNCaP, VCaP, 22RV1, PC3, DU145, NCI-H660, and MDA-PCa-2b) using RNA-Seq data obtained from the Cancer Cell Line Encyclopedia (CCLE)72. FPKM values were calculated using FeatureCounts (version 2.0.3), utilizing the IGF1R-AS1 isoform annotations derived from PacBio Iso-Seq data. CCAT1 expression in these cell lines was similarly quantified using the NCBI RefSeq annotation (GCF_000001405.40-RS_2024_08).

RNA-seq data generation and downstream analysis
Total RNA was extracted using the RNeasy Plus Kit (Qiagen, 74134) and sequenced on an Illumina HiSeq 2500 instrument in High-output Mode to generate strand-specific paired-end reads. Sequencing quality and data integrity were inspected, and adaptor-trimmed reads were aligned to the human reference genome (GRCh38) using HISAT2. Gene-level read counts were quantified using FeatureCounts based on GENCODE annotations. Differential gene expression analysis was performed with edgeR63, identifying differentially expressed genes between siRNA knockdown and control conditions at an adjusted p-value threshold of <0.05. Gene Set Enrichment Analysis (GSEA)73 was conducted using log2 fold change values as the ranking metric to identify gene signatures associated with IGF1R-AS1, SMARCA1, and SMARCA4 in VCaP cells. Hallmark gene sets, oncogenic signatures, and curated gene sets were utilized for the analysis. Transcription factor activity following IGF1R-AS1 knockdown was evaluated using a weighted mean approach as previously described17, based on the CollecTRI transcription factor regulon database74. RNA-seq data of H727 cells treated with JQ1 and DMSO were processed following the same workflow as described above.

ATAC-seq data generation and analysis
ATAC-seq was performed using ATAC-seq kit purchased from Active Motif. Briefly, 50,000 fresh cells with >90% viability were lysed to release nuclei. The nuclei pellet was treated with Tn5 transposase to simultaneously tag target DNA with sequencing adapters and fragment the DNA in a process termed “tagmentation”. Following library amplification and purification, ATAC-seq libraries were subjected to high-throughput sequencing. Raw sequencing reads in FASTQ format were processed using the ENCODE ATAC-seq pipeline (version 2.2.1)75. Reads were first trimmed using Cutadapt (version 1.9.1)76 and aligned to hg38 using Bowtie2 (version 2.2.6)77. Mapped reads overlapping blacklisted regions or mitochondrial DNA, as well as duplicate reads, were removed using SAMtools (version 1.7)78 and PICARD MarkDuplicates (version 1.26) respectively. Peaks were called for all replicates using MACS2 (version 2.1.0)79. Final peak sets for each condition were generated using the optimal Irreproducible Discovery Rate (IDR) cutoff calculated by the ENCODE pipeline. For comparative analysis of publicly available ATAC-seq data of VCaP cells treated with DMSO and AU-15330, raw FASTQ files were obtained from the original study16 and processed using the same pipeline described above.

Differential accessibility analysis between conditions was performed using the sliding windows approach implemented in the csaw R package (version 1.32)80. The genome was divided into 150 bp windows, with ENCODE hg38 blacklist regions excluded. ATAC-seq read counts were assigned to each window and normalized using a LOESS-based method81 to account for compositional biases. Differential accessibility was determined using csaw's negative binomial generalized linear model, and significant windows were merged based on default csaw parameters. Peaks from the optimal IDR peak set generated by the ENCODE pipeline were classified as differentially accessible if they overlapped with a differentially accessible window meeting the significance criteria (FDR < 0.05 and |Log FC| > 0.25).

Hi-C with chromatin immunoprecipitation (HiChIP)-seq data generation and analysis
HiChIP was performed following the published protocol82 with minor modifications as described83. Briefly, chromatin from 5 million cells was crosslinked with 1% formaldehyde for 10 minutes, digested with the MboI restriction enzyme, end-repaired with dNTP including biotin-labeled dATP, and ligated with T4 DNA ligase. Ligated chromatin was sonicated with Qsonica Q800 (1.5 minutes total sonication time, 50% amplitude) and processed with ChIP using an CTCF antibody (Cell Signaling, #3418, 15ul per Hi ChIP). ChIP-enriched DNA was de-crosslinked, purified, and processed with streptavidin beads to enrich ligated DNA fragments. Sequencing libraries were prepared using Illumina Tagment DNA Enzyme and Buffer Kit, and subjected to high-throughput sequencing. Raw sequencing reads in FASTQ format were processed using a custom nextflow pipeline (https://github.com/ylab-hi/nf_hichip). Briefly, reads were aligned using BWA mem (version 0.7.19) with the --5SP option. Aligned paired reads were annotated with pairtools (version 1.1.3) using the parse function with --min-mapq 40, --walks-policy 5unique --max-inter-align-gap 30. Duplicate reads were removed with pairtools sort and partools dedup tools with basic options to obtain valid pairs files. Valid pairs were then converted to .hic format using juicer_tools (version 1.6).

For HiChIP contact heatmaps, .hic files were uploaded to the WashU Epigenome Browser (https://epigenomegateway.wustl.edu/) and screenshots from MYC loci were downloaded using default viewing conditions. For the aggregate peak analysis (APA) plots, .hic files were first converted to cool files at 5kb resolution with VC_SQRT normalization using HiCExplorer (version 3.6) hicConvertMatrix. Aggregate contacts per chromosome for each replicate were then calculated using hicAggregateContacts function with parameters --range 50000:1000000, --numberOfBins 30, --operationType mean, --transform obs/exp. The resulting 31x31 matrix was averaged for over two replicates per condition (siIGF1R-AS1 and siNC. APA scores were calculated by taking the ratio between the signal intensity at the center pixel divided by the mean intensity of a 3x3 square of pixels in the lower left corner. Finally, 21x21 matrixes were plotted in R using ComplexHeatmap package (version 2.18) to highlight center enrichment.

Gene-peak regulatory network construction
To construct the de novo gene-peak regulatory network, nodes representing either ATAC-seq peak regions or gene promoters were selected. ATAC-seq peak regions overlapping SMARCA4 ChIP-seq peaks and significantly downregulated upon IGF1R-AS1 knockdown (FDR < 0.05, Log FC < -0.25) were included, alongside gene promoter regions located 2,000 bp upstream or 200 bp downstream of transcription start sites (TSS). To ensure that ATAC-seq peaks represented enhancers, only those overlapping H3K27ac ChIP-seq peaks were retained, resulting in 3,530 peaks (reduced from 3,800). Gene promoter nodes were limited to those associated with genes significantly downregulated in IGF1R-AS1 knockdown (FDR < 0.05, Log FC < -0.25).

Edges between nodes were established by integrating data from publicly available RNA Pol II ChIA-PET84, H3K27ac HiChIP16, and annotated prostate cancer-specific enhancer-gene links from EnhancerAtlas 2.085. Connections were defined as follows:
1. Peak-to-Gene Edges: A peak node was connected to a gene node if there was a validated link between the peak region and the gene promoter in at least one data source.
2. Peak-to-Peak Edges: Two peak nodes were connected if there was evidence linking their corresponding regions.
Edge weights were calculated based on two criteria:
1. Peak-to-Gene Edges:
- Ranked by the product of ATAC-seq and RNA-seq Log FC values in IGF1R-AS1 knockdown (highest product ranked as 1).
- Ranked by normalized ATAC-seq peak heights (highest value ranked as 1).
- Final weights were computed as the product of these ranks and scaled between 0 and 1.
2. Peak-to-Peak Edges:
- Ranked by the product of ATAC-seq Log FC values for both peaks in IGF1R-AS1 knockdown.
- Ranked by the mean normalized peak heights.
- Final weights were computed as the product of these ranks and scaled between 0 and 1.

To assess the influence of each node, the weighted eigencentrality was calculated using the eigen_centrality() function from the igraph R package (v2.0.3). For a node n, the eigencentrality Cn was computed as:
Cn = (1/λ) * Σ an,i * Ci
where N is the set of nodes connected to n, a is the edge-weighted adjacency matrix, and λ is a scaling constant. To ensure the network was strongly connected, a "head node" was added with weak connections (0.01 weights) to all nodes, representing IGF1R-AS1's regulatory influence. This adjustment minimally impacted eigencentrality calculations.

A parallel SMARCA4-based regulatory network was constructed using SMARCA4 knockdown ATAC-seq and RNA-seq data to define nodes and edges. For networks derived from castration-resistant prostate cancer (CRPC) models, RNA Pol II ChIA-PET data from ENCODE prostate cancer cell lines (DU145, LNCaP, PC-3, VCaP) were used to define gene-peak links. IGF1R-AS1-regulated genes were identified as upregulated in IGF1R-AS1-positive samples (Wilcoxon rank-sum test, p < 0.05), and associated peaks were identified based on significant correlation between ATAC-seq signal and IGF1R-AS1 expression (Pearson correlation > 0.5, p < 0.05). Log FC values of genes and Pearson correlation values of peaks were used to construct edges, replacing RNA-seq Log FC and ATAC-seq peak heights, respectively.

Gene activity quantification and survival analysis for the Tempus cohort
IGF1R-AS1 activity quantification: Since IGF1R-AS1 is an unannotated lncRNA not included in the Tempus xR hybrid capture panel, its expression could not be directly measured. To address this, a network activity scoring approach was developed to quantify IGF1R-AS1 activity based on the expression patterns of genes within its regulatory network. For each sample, expression values of the network genes were normalized by calculating z-scores. A composite score was then generated by summing these z-scores across all network genes. The resulting composite scores were further standardized to yield the final IGF1R-AS1 network activity score.

MYC and AR activity quantification: AR and MYC pathway activity in the Tempus cohort was quantified using a weighted mean approach as previously described, leveraging Hallmark MYC Targets V1 and Hallmark Androgen Response gene sets from the Molecular Signatures Database86.

Survival analysis: Overall survival was calculated from the date of biopsy collection (index date) to either the date of death or last follow-up, with a median follow-up time of 609 days. Patients alive at their last known follow-up were censored at that date. Survival analyses were performed using the R survival package (version 3.7). Initial Cox proportional hazards regression analyses were conducted using penalized splines (pspline) with 4 degrees of freedom to assess the relationship between variables and survival. For visualization and Kaplan-Meier analyses, patients were stratified into high and low groups using optimal cutpoint determination based on maximally selected rank statistics (survminer R package, version 0.4.9). Group differences were assessed using the log-rank test, with statistical significance set at p < 0.05. Validation in Taylor et al, 2010 and Abida et al, 2019 cohorts were performed similarly. The 46-gene Prolaris cell-cyle progression signature was obtained from a previous study28. Cell-cycle progression score was calculated in the same manner as IGF1R-AS1 activity score as described above using the 46-gene signature.

Cell culture
Prostate cancer cell lines VCaP, 22RV1, and LNCaP cells, lung cancer cell line H727, and 293T were purchased from the American Type Culture Collection (ATCC). VCaP and 293T cells were cultured in DMEM medium (GenIDEPOT, CM002-050) supplemented with 10% fetal bovine serum (FBS, GenDEPOT, F0901-050), and 100 × Antibiotic-Antimycotic (GenDEPOT, CA002-100). 22RV1, LNCaP, and H727 cells were cultured in RPMI1640 medium (GenDEPOT, CM059-050) supplemented with 10% FBS and 100 × Antibiotic-Antimycotic. VCaP cells were precultured in phenol red-free DMEM medium (GenDEPOT, CM004-050) containing 10% charcoal-stripped (CS) FBS (GenDEPOT, F0700-010) for 48h before DHT treatment. The cells were cultured in a 37°C incubator and a humidified atmosphere with 5% CO2. All cell lines were authenticated by short tandem repeat (STR) genotyping and were used within 2 months of continuous culturing. Cell lines were Mycoplasma negative as reported by routine laboratory tests.

Antibodies and reagents
Anti-SMARCA1 (MABE366) and anti-Flag (F1804) were purchased from MilliporeSigma. Anti-GAPDH (sc-47724) was purchased from Santa Cruz Biotechnology. Anti-c-Myc (#18583), anti-Histone 3 (#9715), anti-IGF1R β (#9750), anti-SMARCA1 (#12483), anti-CTCF (#3418), and anti-Flag (#14793) were purchased from Cell Signaling Technology. Anti-BRD4 (#A301-985A100) and anti-SMC1A (#A300-055A) were purchased from Bethyl Laboratories. Anti-SMARCA4 (ab110641) was purchased from Abcam. All antibodies used for Western blotting analysis were diluted to 1:1000. (+)-JQ1 (S7110) and Enzalutamide (S1250) were purchased from Selleck Chemicals. DHT (A8380) was purchased from Milliporesigma.

Plasmids, siRNAs and short hairpin RNA (shRNA) Oligos
Full length and fragments of SMARCA4 and SMARCA1 coding DNA sequences were cloned through PCR amplification from ORF clones of SMARCA4 (RG219258) and SMARCA1 (RC239926) purchased from OriGene and inserted into pCDH-EF1-IRES-eGFP vector. siRNAs targeting IGF1R-AS1 were purchased from Dharmacon. siRNAs targeting SMARCA1 (SR304469) or SMARCA4 (SR321835) were purchased from OriGene. shRNA oligos targeting IGF1R-AS1 were constructed in pLKO-vector (#10878) from Addgene. Sequences of siRNA and shRNA oligos are listed in the Supplementary Data 3.

Lentivirus packaging
Lentivirus was packaged by co-transfection of constructs with second-generation packaging plasmids pMD2.G and psPAX2 into HEK293T cells on 6-well plates. After the first 24 h of transfection (250 ng of pMD2.G, 750 ng of psPAX2, 1 μg of pLKO-shRNA), the medium was changed to DMEM, and 48 and 72 h after transfection, the supernatants were pooled, filtered through a 0.45-µm filter, and used for infection.

Cell proliferation
Cell proliferation was measured by CellTiter-Glo® 2.0 Cell viability assay (Promega, G9241). Briefly, cells were seeded onto 96-well plates at 3,000 cells per well with 100 µl of culture medium. 100 µl of CellTiter-Glo® 2.0 Reagent was added to each well and luminescence signal was recorded after mixing the contents for 15 minutes on an orbital shaker.

Migration and invasion assays
Cell migration assays were performed using Transwell chambers (8 μM pore size). Cells in 200 µl serum-free DMEM or RPMI-1640 medium were added to the upper chamber (for LNCaP and 22RV1 cells, 1×105 cells; for VCaP cells, 3×105 cells). In total, 800 µl DMEM medium with 10% FBS was added to the bottom wells of the chambers as a chemo-attractant, and the chambers were incubated at 37°C with 5% CO₂. After 24-48 hours, the invaded cells on the bottom surface of the membrane were gently washed with 1X PBS and fixed with 100% methanol for 5 min followed by 0.5% crystal violet staining at room temperature for 15 min. The non-invading cells from the upper surface of the chamber were removed by scrubbing. Randomly, 3-4 fields were captured under microscope and the invaded cell number was counted per field. Similar inserts coated with Matrigel were used to determine cell invasion. Cells in 200 µl serum-free DMEM or RPMI-1640 medium were added to the upper chamber (for LNCaP and 22RV1 cells, 1×105 cells; for VCaP cells, 3×105 cells). A total of 800 μl complete medium was added to the bottom wells of the chambers. Then invaded cell numbers were counted at 24-48 h.

RNA isolation and Quantitative real-time PCR (qRT-PCR)
Total RNA was extracted from cultured cells using the RNeasy plus Kit (Qiagen, 74134) according to the manufacturer’s instructions. cDNA was synthesized from 1 μg of total RNA using the High-Capacity cDNA Reverse Transcription Kit with RNase Inhibitor (ThermoFisher Scientific, 4374966). Samples were amplified for 40 cycles using an Applied biosystems Quantstudio TM 6 Flex real-time PCR system (ThermoFisher Scientific). The 2−ΔΔCT method was used to calculate the relative gene expression levels normalized against β-actin. Primer sequences are listed in the Supplementary Data 4.

RNA immunoprecipitation
Precipitation of RNA bound to target proteins was performed using Magna RIP™ RNA-Binding Protein Immunoprecipitation Kit (Millipore, 17-700). Cell extracts were prepared from subconfluent 15 cm plates, rinsed twice with cold 1x PBS. RIP was performed with whole-cell extracts obtained by shearing 1.0 × 107 cells in 1 mL of ice-cold RIP Lysis Buffer. All extracts were cleared of debris by centrifugation at 14,000 rpm for 10 minutes at 4 °C and divided into 100 μL aliquots for different conditions. A 10% volume aliquot was set aside as input. 2-5 µg of isotypic IgG and antibodies against SMARCA1, SMARCA4, CTCF, and Flag-tag were incubated with 50 μL of prewashed protein A + G magnetic beads with rotation for 30 minutes at room temperature. Then beads-antibody mixture was washed 3 times with 0.5 mL of RIP wash buffer and incubated with 100 μL aliquots of extracts overnight with gentle rotation at 4 °C. After that, beads were washed 6 times with 500 μL of ice-cold RIP wash buffer. Next, 150 μL of proteinase K buffer (1% SDS and 1.2 µg/µl proteinase K in RIP wash buffer) was added to the mixture beads and incubated for 30 minutes at 55°C. The supernatant was collected after placing the tubes on the magnetic separator. Then 250 μL of RIP wash buffer and 400 μL phenol:chloroform:isoamyl alcohol (125:24:1) (Sigma-Aldrich, P1944) were added to each tube containing the supernatant. Centrifuge the tubes at 14,000 rpm for 10 min at room temperature and remove 350 μL of the aqueous phase to a new tube. Add 400 μL of chloroform to the aqueous phase. Centrifuge them at 14,000 rpm for 10 min at room temperature to separate the phases. Take 300 μL of the aqueous phase and place it in a new tube. To each tube add 50 μL of ammonium acetate 5 M, 15 μL of LiCl 7.5 M, 5 μL glycogen (5 mg/ml), and 850 μL of absolute ethanol. Keep the samples at -80 °C overnight to allow the RNA precipitation, then centrifuge at 14,000 rpm for 30 min at 4 °C and discard the supernatant. Wash the pellet with 500 μL of cold 80 % ethanol and centrifuge at 14,000 rpm for 15 min at 4 °C. Discard the supernatant and air-dry the pellets. Resuspend the pellets in 20 μL of RNase-free water. RNA was reversely transcribed and the enrichment of IGF1R-AS1 and SChLAP1 was measured with specific primers by qPCR (Supplementary Data 4 ).

In vitro RNA transcription
The IGF1R-AS1 cDNA was cloned into pCDH-EF1-IRES-eGFP vector. Biotin-labeled RNAs were transcribed in vitro by T7 RNA polymerase (Promega, P2075) with biotin RNA labeling mix (Roche, 11685597910), using T7-DNA PCR product as templates. After treatment with DNase I, transcribed RNAs were purified by RNeasy MinElute Cleanup Kit (Qiagen, 74204). Then RNA size and purity were verified through running RNA gel.

LncRNA secondary structure prediction
The secondary structure of lncRNA IGF1R-AS1 was predicted via the RNAfold web server87 (available at http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi). The IGF1R-AS1 RNA sequence was processed using the default parameters to determine the structure with the minimum free energy (MFE).

RNA pulldown and protein mass spectrometry
Proteins bound to IGF1R-AS1 were identified by mass spectrometry. In summary, nuclear extracts were prepared from around 1 × 107 VCaP cells using NE-PER™ Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific, 78833). Protein concentration of nuclear extract was measured by Bradford assay, and the nuclear extract was diluted with RIP wash buffer to ∼2 mg/ml. The nuclear extract was precleared with streptavidin T1 magnetic beads (Invitrogen, 65601) in RIP wash buffer for 1 hour with rotation at room temperature. After that, a 10% volume aliquot was set aside as input. Then, 50 pmol of in vitro transcribed biotinylated RNA was incubated with 650 μL of 0.1 μg/μL yeast tRNA-supplemented RIP wash buffer and 650 μL of the precleared nuclear extract overnight at 4 °C with rotation. Next, 60 μL of prewashed streptavidin beads were added to the mix and incubated for one extra hour. Finally, the beads were then washed 5 times with 500 μL of RIP wash buffer. In the last wash, all the supernatant was carefully removed, and the beads were resuspended in protein sample buffer, boiled at 95°C for 10 minutes, loaded and ran in precast 4–12% Bis-Tris gel for several minutes. Protein gels were cut and sent to Northwestern University Proteomics Facility for protein mass spectrometry analysis.

In vitro RNA-protein binding assay
5 pmol of biotinylated RNA was incubated with different amounts of recombinant human SMARCA1 protein (OriGene, TP318308) or SMARCA4 protein (Abcam, ab82237) for 1 hr at RT in 200 μl of binding buffer (50 mM Tris-HCl pH 7.9, 10% glycerol, 100 mM KCl, 5 mM MgCl2, 10 mM β-ME, 0.1% NP-40, 1 mM PMSF, 1 x Superase-in, and 1 x protease inhibitor cocktail). Then, 30 μl of washed streptavidin-conjugated magnetic beads were added to each reaction, and the mixtures were incubated at RT for 30 min. Beads were washed five times and boiled in 1 x SDS loading buffer, and the retrieved protein was analyzed using western blotting.

Protein isolation and Western blotting
Cells were lysed in mammalian protein extraction reagent (Pierce, 89900). After quantification using a BCA protein assay kit (Pierce, 23225), total proteins were separated by SDS-PAGE under denaturing conditions and transferred to PVDF membranes (Bio-Rad, 162-0177). Membranes were blocked in 5% non-fat milk (Bio-Rad, 1706404) and then incubated with primary antibodies overnight at 4 °C, followed by incubation with goat anti-mouse/rabbit/rat IgG (H+L)-HRP secondary antibody (GenDEPOT, SA002-500, SA001-500, SA006-500, diluted to 1:4000). Finally, the protein bands were immunodetected by ECL blotting substrates (Bio-Rad, 1705060) and captured by the ChemiDoc Imaging System (Bio-Rad, Hercules, CA).

Single-Molecule RNA FISH and Confocal Microscopy
Single-molecule RNA fluorescence in situ hybridization (smFISH) was performed to detect IGF1R-AS1 transcripts using custom-designed Stellaris FISH probes (LGC Biosearch Technologies). VCaP cells (2×106) were seeded on 12mm coverslips 24h before hybridization. Cells were fixed with 4% paraformaldehyde (10 min), quenched with 0.1M glycine (10 min), and washed with PBS. After permeabilization with 0.5% Triton X-100 (30 min), cells were incubated in pre-hybridization buffer (10% formamide/2× SSC) for 30 min. RNasin (Promega, N2111) was included throughout to prevent RNA degradation.

Hybridization was performed for 3h at 37°C in buffer containing 2× SSC, 10% formamide, 10% dextran sulfate, 0.2mg/mL salmon sperm ssDNA, 2mg/mL BSA, 100nM FISH probes, and 40U RNasin. After two 20-min washes (37°C) with 10% formamide/2× SSC and a final PBS wash, coverslips were mounted using prolonged diamond anti-fade medium. Probe specificity was validated using siRNA-mediated knockdown of IGF1R-AS1.

Imaging was performed on a custom Nikon microscope equipped with a Yokogawa W1 spinning disk, Kinetix CMOS camera (Photometrics), 60× oil immersion objective (NA 1.45), and lasers (405, 488, 561, 640nm). DAPI and ATTO647N-labeled IGF1R-AS1 probes were sequentially excited using 405nm and 640nm lasers. Analysis was conducted using FIJI ImageJ.

Chromatin immunoprecipitation (ChIP)
Precipitation of DNA bound to target proteins was performed using Magna ChIP™ A/G Chromatin Immunoprecipitation Kit (Millipore, 17-10085). Cells were cross-linked in 1% formaldehyde at room temperature for 10 min. Crosslinking was terminated by the addition of 125 mM (final concentration) glycine. Cells were then incubated with glycine at room temperature for 5 min with gentle mixing, washed with ice-cold PBS, scraped and resuspended in PBS. After washing, cell pellets were lysed using cell lysis buffer (10 mM Tris-HCl [pH 7.5], 10 mM NaCl, 3 mM MgCl2, 0.5% IGEPAL, 1 mM PMSF), incubated on ice for 10 min, and then centrifuged at 800 g for 5 min at 4°C to pellet the nuclei. Pellets were lysed using nuclear lysis buffer (50 mM Tris-HCl [pH 8.0], 10 mM EDTA, 1% SDS, 1 mM PMSF, and proteinase inhibitor cocktail) and then diluted with IP dilution buffer (16.7 mM Tris-HCl [pH 8.0], 1.2 mM EDTA, 1.1% Triton X-100, 167 mM NaCl, 0.01% SDS). Nuclear lysates were sonicated, and the debris was removed by centrifugation. 2-5 µg of Anti-BRD4, anti-AR, anti-SMARCA1, anti-SMARCA4, anti-CTCF, anti-SMC1A, and IgG antibodies were mixed with clear nuclear lysates for immunoprecipitation. Co-precipitated DNA was purified, and the enrichment of target DNA fragments was measured with specific primers by qPCR. ChIP-qPCR primers used in this study are listed in Supplementary Data 4.

Quantitative analysis of chromosome conformation capture assays (3C-qPCR)
3C-qPCR was performed following a published protocol with minor modifications88. Briefly, cells under various treatments were crosslinked with 1% formaldehyde for 10 minutes at room temperature, and the reaction was quenched with 0.125 M glycine. Nuclei were isolated, lysed, and digested overnight at 37 °C with 100 U of MluCI (NEB, R0538) under rotation. Ligation was carried out at 16 °C for 4 hours, followed by a 30-minute incubation with 5 µl of T4 DNA ligase (NEB, M0202) at room temperature. Reverse crosslinking was performed overnight at 68 °C in the presence of proteinase K. Genomic DNA was extracted using phenol-chloroform. After treatment with 1 µl of RNase A (Invitrogen, AM2270) for 30 minutes at 37 °C, secondary digestion was performed with 100 U of HindIII (NEB, R0104S) for 2 hours at 37 °C under rotation, followed by a second phenol-chloroform extraction. For qPCR analysis, GAPDH served as an internal reference, and relative interaction frequencies were calculated using the ΔΔCt method, normalized to the E3 region. Primer sequences are provided in Supplementary Data 4.

Xenograft mouse model
CB-17 SCID male mice (strain 236, ages 6-7 weeks) and NOD-SCID NCG male mice (strain 572, ages 5-6 weeks) were purchased from Charles River. All animal care and experimental procedures were conducted in compliance with institutional and National Institutes of Health (NIH) guidelines, with approval from the University of Minnesota and Northwestern University Institutional Animal Care and Use Committee (IACUC). All mice were housed in a specific pathogen-free facility maintained at 21.5 ± 1 °C and 30-70% humidity, with a 12-hour dark-light cycle and access to food and water. Animal health and behavior were monitored daily throughout the study. Mice were weighed twice weekly to monitor for cachexia or rapid weight loss. For VCaP xenograft experiments, 2 × 106 VCaP cells expressing IGF1R-AS1 shRNA or control shRNA were suspended in 100 μl PBS containing 50% Matrigel and implanted subcutaneously into the dorsal flank of pre-castrated CB-17 SCID male mice. For 22RV1 xenograft experiments, 2 × 106 22RV1 cells expressing IGF1R-AS1 shRNAs or control shRNA were suspended in 100 μl PBS containing 50% Matrigel and implanted subcutaneously into the dorsal flank of pre-castrated NOD-SCID NCG male mice. Tumor volumes were measured by calipers for length (a), width (b), and calculated as tumor volume = MIN(a)^2 × MAX(b)/2. Once the average tumor volume in the control group reached 2000 mm3, mice were euthanized via CO2 inhalation. Mice's death was subsequently confirmed by cervical dislocation as a secondary physical method in accordance with institutional guidelines. Additional criteria for early euthanasia included a body weight loss exceeding 10%, tumor ulceration, or any tumor growth that impeded normal physiological function.

Statistical analysis
Statistical analysis was carried out using the GraphPad Prism 10 to assess differences between experimental groups. Statistical differences were analyzed by two-tailed Student’s t-test, Chi-square test, or one-way and two-way ANOVA where appropriate. p values lower than 0.05 were considered to be statistically significant. No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those reported in previous studies. The results were reproducible and conducted with established internal controls. When feasible, experiments were repeated two or more times and yielded similar results.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Yang et al identified 1,344 new lncRNAs in prostate cancer data sets, and performed further bioinformatics analyses and\nexperiments to show lncRNA IGF1R-AS1 interacts with SWI/SNF complex to regulate MYC expression and cell proliferation\nin VCaP cells. The data are well-presented, and the manuscript is clearly written and well-organized. However, I have\nseveral suggestions to improve the novelty, mechanistic insight, and robustness of this study.\n1. The authors have performed 3’ RACE and PacBio Iso-seq to validate IGF1R-AS1 isoforms. Since this is a novel\nlncRNA, additional Northern blotting and 5’ RACE would further confirm transcript integrity and size.\n2. The functional characterization of IGF1R-AS1 is currently limited to the VCaP cell line. Validation in more PCa cell lines is\nnecessary for both in vitro and in vivo assays. Additionally, for the in vivo tumor growth assay, they only used one IGF1R-\nAS1 shRNA (Fig 3h, shAS1-2#). shAS1-1# should also be used for in vivo tumor growth assay.\n3. This study reports an interaction between IGF1R-AS1 and the SWI/SNF complex. However, given the extensive\nliteratures on lncRNA-protein interactions in cancer, including with the SWI/SNF complex, the conceptual advances here\nappear incremental. The authors should provide a detailed analysis of this interaction to identify the specific protein domains\nand RNA sequences involved.\n4. The mechanism, by which the SWI/SNF-IGF1R-AS1 complex targets specific enhancers or promoters genome-wide,\nremains unclear. Since the key novelty of this study is the cooperation of SWI/SNF and IGF1R-AS1 to regulate epigenome,\nclarification of the mechanism is necessary.\n5. They claimed IGF1R-AS1 modulates CTCF-mediated chromatin loops, but the current data are insufficient to support a\nrobust conclusion. There is no CTCF HiChIP data to show reduced CTCF looping upon IGF1R-AS1 knockdown, and the\nmechanism explaining why knockdown decreases CTCF binding or looping is not addressed. The mechanism here is also\nrelated with the genome-wide specific targeting of SWI/SNF-IGF1R-AS1 complex.\n6. The development of new targeting drugs based on this lncRNA, with thorough in vivo assessments of identity, PK,\ntoxicity\nand efficacy, should improve the translational impact of this study.\n7. Since IGF1R-AS1 was identified in metastatic castration-resistant prostate cancer (mCRPC) datasets, its potential role in\nresistance to AR-targeting therapies warrants investigation. In vivo assays addressing this question could increase the\nclinical importance of this study.", "reviewer_2": "The authors discover the antisense lncRNA IGF1R-AS1, transcribed from a high-density super-enhancer within the IGF1R\nlocus, and delineate its oncogenic mechanism in metastatic castration-resistant prostate cancer (mCRPC). IGF1R-AS1\nrecruits SWI/SNF chromatin-remodelling and CTCF/cohesin architectural complexes to establish long-range loops that\njuxtapose distal MYC enhancers with the MYC promoter, thereby amplifying MYC expression and tumourigenicity. The work\nillustrates how a super-enhancer–driven lncRNA can remodel three-dimensional genome topology in trans to activate distal\noncogenic circuitry in a cancer-specific context.\n1. Threshold for lncRNA inclusion (Fig. 1a).\nThe manuscript states that lncRNAs retained for downstream analysis are those expressed in ≥5 of 101 mCRPC\nspecimens.\nPlease justify this 5-sample threshold. Does it represent a statistical cut-off (e.g. to control the false-discovery\nrate) or a\nbiological rationale (e.g. prevalence ≥5 %)?\n2. Please describe explicitly how many of the 101 tumors express IGF1R-AS1 above background and provide the\nexpression distribution (e.g. violin plot or histogram).\n3. Most functional assays are performed in VCaP cells. To strengthen the claim that IGF1R-AS1 is broadly oncogenic in\nmCRPC, please test at least one additional metastatic line with high endogenous expression (e.g., MDA-PCa-2b or 22Rv1)\nusing knockdown. Conversely, introduce IGF1R-AS1 into a line (e.g., LNCaP) to determine whether overexpression is\nsufficient to confer the oncogenic phenotypes.\n4. Selection of TCGA specimens (Fig. 2b). Only three “positive” TCGA prostate samples are shown. Clarify the selection\ncriteria (“positive” is ambiguous). Were these the top three expressers of IGF1R-AS1, or the only cases with matched\nnormal\ntissue?\n5. The author described that both SMARCA1 and SMARCA4 interact with the MYC promoter and enhancer regions and\nbind IGF1R-AS1. However, the subsequent analyses rely exclusively on SMARCA4 ChIP-seq data. Are the authors\nfocusing solely on SMARCA4 because SMARCA1 and SMARCA4 operate within the same SWI/SNF chromatin-remodeling\ncomplex? Please clarify this point in the manuscript.\nMinor comments\nTypographical error (Fig. 2h). “siNON” should read “siCon” (scramble control).\nLabelling ambiguity (Fig. 3h). The axis label “siAS-1 #1/#2 meta” is unclear. If both siRNAs were co-transfected, please\nindicate “mix” or “pool”.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "In this manuscript, Yang et al. identify IGF1R-AS1, a previously uncharacterized tumor-specific lncRNA, as a key regulator in\nmetastatic castration-resistant prostate cancer (mCRPC). Through integrative multi-omics analyses, the authors demonstrate\nthat IGF1R-AS1 interacts with the chromatin remodelers SMARCA1 and SMARCA4 to orchestrate long-range chromatin\nlooping between the distal MYC enhancer E3 and its promoter, thereby amplifying MYC expression and driving\ntumorigenesis. This work not only unveils a novel trans-acting mechanism by which lncRNAs modulate oncogenic signaling\nbut also highlights the broader potential of tumor-specific lncRNAs as therapeutic targets in cancer. This is a well-executed\nstudy with significant findings. However, some mechanistic details require further refinement before publication. Below is a\ndetailed review with suggestions for further improvement:\n1. The manuscript suggests IGF1R-AS1 recruits SWI/SNF to MYC enhancers, but the exact binding sites or motifs mediating\nthis interaction are unclear. Additional experiments (e.g., truncation mutants of IGF1R-AS1 to identify functional domains)\ncould strengthen this claim. Moreover, it is interesting to see whether IGF1R-AS1 directly binds SMARCA1 and SMARCA4.\nCan these two proteins directly bind to IGF1R-AS1 in vitro? If so, which functional domains are responsible for the direct\ninteraction?\n2. As CCAT1 is localized in a super-enhancer region, it is extensively transcribed in many types of cancers and has been\ndemonstrated to have a 5' additional extension in HeLa cells (see Cai et al., Nature, 2020), I am wondering whether IGF1R-\nAS1 RNA directly interact with the enhancer RNAs transcribed in E3 region? could them form RNA base pairings? This can\nexplain why IGF1R-AS1 specifically targets the chromatin remodeling complex to this enhancer and potentially to thousands\nof other sites.\n3. While the study shows IGF1R-AS1 does not regulate IGF1R, it would be helpful to explicitly test whether IGF1R-AS1\nknockdown affects other nearby genes (e.g., by RNA-seq or qPCR).\n4. For the IGF1R-AS1 knockdown studies, it is better to reintroduce IGF1R-AS1 in knockdown cells (e.g., via\noverexpression) to confirm that the observed phenotypes are due to IGF1R-AS1 loss and not off-target effects.\n5. The Tempus cohort analysis suggests IGF1R-AS1 activity predicts poor survival. Expanding this to other cohorts or\nvalidating in independent datasets would strengthen the biomarker claim.\n6. Several typos need to be fixed: Page 3: \"evolutionally\" → \"evolutionarily\"; Page 9: \"fueling cancer progression\" → \"driving\ncancer progression\""}, "author_response": "Point-by-Point response\nRE: NCOMMS-25-35735 - Tumor-specific lncRNA IGF1R-AS1 trans-regulates chromatin interactions\nassociated with oncogenic MYC signaling\nWe would like to thank the editor for the opportunity to allow us to revise our manuscript and thank all\nreviewers for your insightful and constructive comments, which have significantly improved the quality of our\nwork. We have carefully considered the comments and are committed to addressing each one of them. As\nrequested, we have added new data and rewritten some parts of the manuscript. We believe that these changes\nhave resulted in a more solid and cohesive manuscript. We will now provide a point-by-point response to the\nreviewers’ comments. The changes in the manuscript are highlighted in red. We hope the Reviewers and the\neditor will find this manuscript to be much improved and suitable for publication. We want to assure the\nreviewers and the editor that we take their comments seriously and are dedicated to addressing them to the best\nof our ability.\nREVIEWER COMMENTS\nReviewer #1, expertise in prostate cancer epigenetics, lncRNAs and MYC (Remarks to the Author):\nYang et al identified 1,344 new lncRNAs in prostate cancer data sets, and performed further bioinformatics analyses and\nexperiments to show lncRNA IGF1R-AS1 interacts with SWI/SNF complex to regulate MYC expression and cell\nproliferation in VCaP cells. The data are well-presented, and the manuscript is clearly written and well-organized.\nHowever, I have several suggestions to improve the novelty, mechanistic insight, and robustness of this study.\nResponse: We greatly appreciate the reviewer’s supportive evaluation of our manuscript.\n1. The authors have performed 3’ RACE and PacBio Iso-seq to validate IGF1R-AS1 isoforms. Since this is a novel\nlncRNA, additional Northern blotting and 5’ RACE would further confirm transcript integrity and size.\nResponse: We sincerely thank the reviewer for this crucial suggestion and agree that further confirmation of IGF1R-AS1\ntranscript integrity and size is essential.\nNorthern blot is a traditional method for determining the size and integrity of RNA transcripts; however, it has several\nsignificant disadvantages, such as low sensitivity and the potential for hazardous radioactive probes. Therefore, we\nadapted to use the third-generation long-read sequencing technology, Nanopore direct RNA sequencing, which preserves\nthe native state of RNA and sequences entire RNA molecules from end to end, providing more accurate RNA size\nmeasurements, and its real-time sequencing allows direct assessment of RNA integrity from the sample, and PacBio long-\nreads sequencing, which can also accurately sequence the full-length of transcripts.\nTo directly address the concern, we analyzed our whole-transcriptome Nanopore direct RNA sequencing (dRNA-seq) data\ngenerated from VCaP cells, which was used in a separate study1 and deposited into GEO under accession GSE277934. We\nfound that the major IGF1R-AS1 isoform size and sequence in VCaP cells is consistent with the analysis from PacBio Iso-\nseq data in VCaP cells. This compelling evidence confirms the integrity and size of the IGF1R-AS1 transcript, and the\nresult has been incorporated into new supplementary Fig. 2b (line 180-181) (and shown in Response Fig. 1 for reference).\n2. The functional characterization of IGF1R-AS1 is currently limited to the VCaP cell line. Validation in more PCa cell\nlines is necessary for both in vitro and in vivo assays. Additionally, for the in vivo tumor growth assay, they only used one\nIGF1R-AS1 shRNA (Fig 3h, shAS1-2#). shAS1-1# should also be used for in vivo tumor growth assay.\nResponse: We thank the reviewer for these crucial suggestions regarding the broader validation. We conducted extensive\nfunctional characterization of IGF1R-AS1 in a second PCa cell line, 22RV1, which expresses detectable IGF1R-AS1. We\nperformed IGF1R-AS1 knockdown using two independent siRNAs or shRNAs in 22RV1 cells and then evaluated cell\ngrowth, migration, and invasion. Consistent with the findings in VCaP cells, knocking down IGF1R-AS1 in 22RV1 cells\nsuppressed cell growth, migration, and invasion (new supplementary Fig. 3b-d). In addition, we performed in vivo murine\nxenograft assays using 22RV1 cells with IGF1R-AS1 stable knockdown by two different shRNAs (shAS1-1# and shAS1-\n2#), and observed that both IGF1R-AS1 shRNAs significantly inhibited xenograft tumor progression in vivo (new\nsupplementary Fig. 3k-n).\nThese new data have been incorporated into revised supplementary Fig. 3 (lines 243-246, and lines 252-254) (and shown\nhere in Response Fig. 2 for reference).\n3. This study reports an interaction between IGF1R-AS1 and the SWI/SNF complex. However, given the extensive\nliteratures on lncRNA-protein interactions in cancer, including with the SWI/SNF complex, the conceptual advances here\nappear incremental. The authors should provide a detailed analysis of this interaction to identify the specific protein\ndomains and RNA sequences involved.\nResponse: We thank the reviewer for this critical suggestion. We have performed the RNA-protein domain mapping to\nidentify the specific regions governing the interaction between IGF1R-AS1 with SMARCA1 and SMARCA4.\nFirst, to investigate whether IGF1R-AS1 directly interacts with SMARCA1 and SMARCA4, we performed in vitro RNA-\nprotein binding assay with in vitro-transcribed biotin-labeled IGF1R-AS1 RNA and purified recombinant His-tagged\nSMARCA4 protein, or Flag-tagged SMARCA1 protein, which demonstrated that IGF1R-AS1 binds directly to\nSMARCA1 protein and SMARCA4 protein (new Fig. 4f-g).\nTo map the IGF1R-AS1 functional motifs corresponding to SMARCA1 and SMARCA4 binding, we predicted the\nsecondary structure of IGF1R-AS1 using RNAfold webserver (new Fig. 4h), and conducted in vitro RNA pull-down\nassays using a series of truncated biotin-labeled IGF1R-AS1 fragments (new Fig. 4i). This analysis revealed that\nnucleotides 512-645 of IGF1R-AS1 are critical for the interaction with SMARCA4 protein, while nucleotides 177-407\nand 408-511 of IGF1R-AS1 are essential for the interaction with SMARCA1 protein.\nBoth SMARCA1 and SMARCA4 contain an ATPase domain in the middle of the protein sequence (new Fig. 4j-k). We\ncloned the full-length, N-terminal domain, ATPase domain, and C-terminal domain of both SMARCA1 and SMARCA4,\neach with a C-terminal Flag tag. We expressed them in VCaP cells and then performed RIP assay using anti-Flag antibody\n(new supplementary Fig. 4g-h), followed by qPCR to analyze the enrichment of IGF1R-AS1 by different protein domains\n(new Fig. 4j-k). The results showed that IGF1R-AS1 mainly binds to the ATPase domain of SMARCA4 and the C-\nterminal domain of SMARCA1 (new Fig. 4j-k).\nThese detailed mapping studies significantly strengthen our findings by identifying the specific RNA motifs and protein\ndomains responsible for the IGF1R-AS1-SMARCA4 and IGF1R-AS1-SMARCA1 interaction. These new data have been\nincorporated into revised Fig. 4 and supplementary Fig. 4 (lines 292-302) (see Response Fig. 3 for reference).\n4. The mechanism, by which the SWI/SNF-IGF1R-AS1 complex targets specific enhancers or promoters genome-wide,\nremains unclear. Since the key novelty of this study is the cooperation of SWI/SNF and IGF1R-AS1 to regulate epigenome,\nclarification of the mechanism is necessary.\nResponse: We thank the reviewer for this crucial comment.\nPrevious investigations demonstrated that CTCF and SMARCA4 physically interact and colocalize to enhancers and\nregulate gene expression2-4. In our analysis, we showed notably disrupted CTCF binding and reduced CTCF-mediated\nchromatin interactions around the E3 region in VCaP cells after the treatment with SWI/SNF degrader (Fig. 6b). Our\nIGF1R-AS1 pulldown protein MS data also suggest that CTCF binds IGF1R-AS1, and thus it is very likely that IGF1R-\nAS1 regulates the epigenome by affecting the interaction between CTCF and SMARCA4. We validated the interaction\nbetween IGF1R-AS1 and CTCF through CTCF RIP-qPCR (new Fig. 6g) and IGF1R-AS1 pulldown assay, which further\nshowed that IGF1R-AS1 interacts with CTCF through nucleotides 177-407 (new Fig. 6h), while it interacts with\nSMARCA4 through nucleotides 512-645 (new Fig. 4i, See Response Fig. 3). We further performed CTCF co-IP in VCaP\ncells and found that knocking down IGF1R-AS1 reduced the interaction between SMARCA4 and CTCF (new Fig. 6i),\nindicating that IGF1R-AS1 acts as a scaffold to enhance their interaction and further regulate the epigenome. We have\nadded these results into revised Fig. 4 and Fig. 6 (lines 446-453) (see Response Fig. 4 for reference).\n5. They claimed IGF1R-AS1 modulates CTCF-mediated chromatin loops, but the current data are insufficient to support a\nrobust conclusion. There is no CTCF HiChIP data to show reduced CTCF looping upon IGF1R-AS1 knockdown, and the\nmechanism explaining why knockdown decreases CTCF binding or looping is not addressed. The mechanism here is also\nrelated with the genome-wide specific targeting of SWI/SNF-IGF1R-AS1 complex.\nResponse: We thank the reviewer for this important comment.\nWe performed CTCF HiChIP in siRNA negative-control and IGF1R-AS1 knockdown VCaP cells to directly assess the\nimpact of IGF1R-AS1 on CTCF-mediated chromatin looping. This new data was incorporated into the revised Fig. 6.\nAnalysis of this CTCF HiChIP data confirmed down-regulation of the E3-MYC promoter loop upon IGF1R-AS1\ndepletion (new Fig. 6d, see Response Fig. 5). Importantly, aggregate peak analysis (APA) showed significant reduction in\nCTCF looping strength genome-wide (new Fig. 6e; APA score 10.16 vs 8.52 in siNC and siAS1 respectively, see\nResponse Fig. 5). Additionally, CTCF looping strength decreased across all chromosomes individually (new Fig. 6f, see\nResponse Fig. 5), consistent with ATAC-seq footprinting analysis in VCaP cells after knocking down IGF1R-AS1\n(revised supplementary Fig. 6f, see Response Fig. 5). These data confirmed that IGF1R-AS1 positively affects CTCF\nlooping genome-wide. Besides, in our analysis, we showed notably disrupted CTCF binding and reduced CTCF-mediated\nchromatin interactions around the E3 region in VCaP cells after the treatment with SWI/SNF degrader (Fig. 6b, see\nResponse Fig. 4). And we further showed that knocking down IGF1R-AS1 reduced the binding of SMARCA4,\nSMARCA1, CTCF, and the cohesin subunit SMC1A at the E3 enhancer (Fig. 6c, see Response Fig. 5). Moreover, we\nrevealed that IGF1R-AS1 binds SMARCA4 through nucleotides 512-645 (new Fig. 4i, see Response Fig. 3), binds CTCF\nthrough nucleotides 177-407 (new Fig. 6h, see Response Fig. 4), and knocking down IGF1R-AS1 decreased the\ninteraction between SMARCA4 and CTCF (new Fig. 6i, see Response Fig. 4). All together, these data indicate that\nIGF1R-AS1 acts as a scaffold to enhance interaction between SMARCA4 and CTCF to regulate chromatin looping and\nthe epigenome.\nWe have added these results into revised Fig. 6 and supplementary Fig. 6 (lines 441-446) (see Response Fig.5 for\nreference), and the proposed mechanism is summarized in a schematic model in Fig. 6j (see Response Fig.5).\n6. The development of new targeting drugs based on this lncRNA, with thorough in vivo assessments of identity, PK,\ntoxicity and efficacy, should improve the translational impact of this study.\nResponse: We sincerely appreciate the reviewer’s insightful suggestion to develop novel IGF1R-AS1-targeting drugs to\nenhance the translational impact of our findings.\nOur current study successfully establishes the vital role of IGF1R-AS1 in tumor progression and demonstrates its potential\nas a therapeutic target for PCa treatment. However, detailed drug development, including thorough PK, toxicity, and\nefficacy testing, is a complex process beyond the current scope of this foundational mechanistic manuscript. In our\nfollow-up study, we will collaborate with RNA therapy experts to design and synthesize next-generation nucleic acid\ntherapeutics (such as modified siRNAs or antisense oligonucleotides (ASOs)) targeting IGF1R-AS1. Furthermore, we will\ngenerate a cell-based luciferase reporter high-throughput platform to screen small molecule inhibitors against IGF1R-\nAS1. Once we have the lead compounds, siRNAs, or ASOs, we will evaluate them in mouse models in vivo to assess their\npharmacokinetics, safety profiles, and efficacy in inhibiting CRPC progression.\n7. Since IGF1R-AS1 was identified in metastatic castration-resistant prostate cancer (mCRPC) datasets, its potential role\nin resistance to AR-targeting therapies warrants investigation. In vivo assays addressing this question could increase the\nclinical importance of this study.\nResponse: We thank the reviewer for this excellent advice.\nEnzalutamide is a nonsteroidal antiandrogen for the treatment of mCRPC, both before and after chemotherapy. 22RV1 is a\nwell-characterized enzalutamide-resistant prostate cancer cell line that expresses both AR and AR splice variants,\nincluding ARv75. In 22RV1 cells, we depleted IGF1R-AS1 expression by two different siRNAs and/or shRNAs and\nperformed in vitro and in vivo assays, demonstrating that knocking down IGF1R-AS1 inhibited 22RV1 cell growth,\nmigration, invasion, and 22RV1 xenograft growth (revised supplementary Fig. 3, see Response Fig. 2). These results in\n22RV1 cells demonstrated the potential to overcome enzalutamide resistance by targeting IGF1R-AS1 in prostate cancer\ntreatment.\nReviewer #1 (Remarks on code availability):\nN/A\nReviewer #2, expertise in lncRNAs, prostate cancer genomics and metastasis (Remarks to the Author):\nThe authors discover the antisense lncRNA IGF1R-AS1, transcribed from a high-density super-enhancer within the\nIGF1R locus, and delineate its oncogenic mechanism in metastatic castration-resistant prostate cancer (mCRPC). IGF1R-\nAS1 recruits SWI/SNF chromatin-remodelling and CTCF/cohesin architectural complexes to establish long-range loops\nthat juxtapose distal MYC enhancers with the MYC promoter, thereby amplifying MYC expression and tumourigenicity.\nThe work illustrates how a super-enhancer–driven lncRNA can remodel three-dimensional genome topology in trans to\nactivate distal oncogenic circuitry in a cancer-specific context.\nResponse: We thank the reviewer for the excellent summary of the findings in our manuscript.\n1. Threshold for lncRNA inclusion (Fig. 1a).\nThe manuscript states that lncRNAs retained for downstream analysis are those expressed in ≥5 of 101 mCRPC\nspecimens. Please justify this 5-sample threshold. Does it represent a statistical cut-off (e.g. to control the false-discovery\nrate) or a biological rationale (e.g. prevalence ≥5 %)?\nResponse: We thank the reviewer for this important question regarding our threshold for lncRNA retention. The threshold\nof ≥5 samples (representing ~5% prevalence) was chosen based on biological rationale rather than as a statistical false-\ndiscovery rate control.\nOur reasoning was as follows:\n1) Biological relevance: We aimed to focus on lncRNAs that are recurrently expressed across multiple patients, as these\nare more likely to represent biologically relevant features of mCRPC biology rather than patient-specific events or\ntechnical artifacts. LncRNAs expressed in <5% of samples would be challenging to validate and are unlikely to represent\ngeneralizable disease mechanisms amenable to cohort-level analysis.\n2) Balance between discovery and robustness: This threshold balances the goal of discovering potentially rare but\nrecurrent lncRNAs while filtering sporadically detected transcripts that may reflect technical noise. Given the known low\nexpression levels and high tissue-specificity of many lncRNAs, a stringent threshold could eliminate biologically relevant\ntranscripts, while an overly permissive threshold would retain noise.\n3) Practical considerations: Prevalence-based filtering is a common approach in large-scale cancer transcriptome analyses\nto focus on consistently detectable features.\nTo address the reviewer's concern, we performed sample threshold analysis for lncRNA discovery in these 101 mCRPC\nspecimens, showing that when setting different thresholds, the number of known prostate cancer relevant lncRNAs\n(including PCA3, SCHLAP1, MALAT1, HOTAIR, PCAT1, PVT1, UCA1, SNHG20, HOTTIP, PCGEM1, CTBP1-AS,\nARLNC1, MIR503HG, DANCR, NEAT1, TUG1, LINC01128, PCAT1, PCAT5, PCAT7, PCAT14, PCAT18, PCAT19,\nSNHG3, SNHG12, SOCS2-AS1, TRPM2-AS, MEG3) retained changes accordingly. This analysis demonstrates that at a\nthreshold of ≥5 samples, ~90% of known lncRNAs are retained, supporting our choice as appropriately balanced between\nsensitivity and specificity (Response Fig. 6). We have revised the Methods section entitled ‘Novel lncRNA identification’\nto explicitly justify the prevalence threshold, clarifying that transcripts detected in fewer than five patient samples were\nexcluded to enrich for recurrently expressed lncRNAs and minimize sporadic, noise-driven detections (line 540-542).\n2. Please describe explicitly how many of the 101 tumors express IGF1R-AS1 above background and provide the\nexpression distribution (e.g. violin plot or histogram).\nResponse: We thank the reviewer for this important suggestion. We reanalyzed IGF1R-AS1 expression across primary\nPCa (n=500) and mCRPC (n=101) samples based on the IGF1R-AS1 feature junction (chr15: 98839896-98843675). We\nfound that 33 samples (32.7%) in 101 mCRPC, and 89 samples (17.8%) in 500 primary PCa express IGF1R-AS1 with its\nfeature junction.\nWe have now added a supplementary Fig. 3a (lines 239-240) (see Response Fig. 7) showing the expression distribution\namong PCa samples.\n3. Most functional assays are performed in VCaP cells. To strengthen the claim that IGF1R-AS1 is broadly oncogenic in\nmCRPC, please test at least one additional metastatic line with high endogenous expression (e.g., MDA-PCa-2b or\n22Rv1) using knockdown. Conversely, introduce IGF1R-AS1 into a line (e.g., LNCaP) to determine whether overexpression is\nsufficient to confer the oncogenic phenotypes.\nResponse: We thank the reviewer for providing these critical suggestions.\nWe performed functional characterization of IGF1R-AS1 in a second PCa cell line, 22RV1 which is a CRPC cell line\nexpressing AR wild-type and AR-v7 variant (see Response Fig. 8). We performed IGF1R-AS1 knockdown using two\nindependent siRNAs or shRNAs in 22RV1 cells, and then evaluated cell growth, migration, and invasion. Consistent with\nthe findings in VCaP cells, knocking down IGF1R-AS1 in 22RV1 cells suppressed cell growth, migration, and invasion\n(new Supplementary Fig. 3b-d, lines 242-246). Furthermore, we performed in vivo xenograft assays using 22RV1 cells\nwith IGF1R-AS1 stable knockdown by two different shRNAs (shAS1-1# and shAS1-2#), which also showed inhibited\ntumor progression in vivo (new Supplementary Fig. 3k-n, lines 252-253). In addition, we stably overexpressed IGF1R-\nAS1 in LNCaP cells using lentivirus, resulting in modest increases in cell growth, migration, and invasion (new\nSupplementary Fig. 3h-j, lines 248-249), further demonstrating the tumor progression role of IGF1R-AS1 in prostate\ncancer.\n4. Selection of TCGA specimens (Fig. 2b). Only three “positive” TCGA prostate samples are shown. Clarify the selection\ncriteria (“positive” is ambiguous). Were these the top three expressers of IGF1R-AS1, or the only cases with matched\nnormal tissue?\nResponse: We thank the reviewer for this valuable comment. In Fig. 3b, the three “positive” TCGA prostate cancer\nsamples correspond to tumors exhibiting the highest IGF1R-AS1 expression among cases with available matched normal\ntissue. These samples were chosen to illustrate representative expression patterns in tumors with elevated IGF1R-AS1\nlevels. We have revised the description in the manuscript as follows: “We examined the top three IGF1R-AS1-high TCGA\nprostate specimens for which tumor and matched adjacent normal tissue RNA-seq data were available” (line 234).\n5. The author described that both SMARCA1 and SMARCA4 interact with the MYC promoter and enhancer regions and\nbind IGF1R-AS1. However, the subsequent analyses rely exclusively on SMARCA4 ChIP-seq data. Are the authors\nfocusing solely on SMARCA4 because SMARCA1 and SMARCA4 operate within the same SWI/SNF chromatin-remodeling\ncomplex? Please clarify this point in the manuscript.\nResponse: We appreciate the reviewer's careful reading and agree that our focus on SMARCA4 in Figure 5 requires\nclarification.\nFirst, we wish to clarify that SMARCA1 and SMARCA4 operate in distinct chromatin remodeling complexes with\ndifferent functions. SMARCA4 is the catalytic ATPase subunit of the SWI/SNF complex, while SMARCA1 is the\ncatalytic ATPase of the ISWI complex. These complexes have complementary but distinct roles in chromatin regulation:\nSWI/SNF complexes typically promote chromatin accessibility and gene activation, whereas ISWI complexes primarily\nregulate nucleosome spacing and positioning, though their effects on gene expression can be context-dependent6.\nWe focused our chromatin accessibility and network analyses on SMARCA4 and the SWI/SNF complex for several\nreasons:\n1) Biological relevance: SWI/SNF plays a critical role in prostate cancer biology and has emerged as a therapeutic\ntarget in this disease7. Many drugs have been developed to pharmacologically suppress SWI/SNF function in\ncancer models, including testing in prostate cancer models, such as ADAADi, PB16, AU15330, I-78, 2-77, and\nsome of these drugs are now being evaluated in clinical trials for cancer treatment8.\n2) Functional alignment: SWI/SNF's role in promoting chromatin accessibility aligns with our observations of\nIGF1R-AS1-dependent accessibility changes.\n3) Data availability: High-quality SMARCA4 ChIP-seq data in VCaP cells were publicly available, enabling\ngenome-wide identification of direct SWI/SNF binding sites. This was essential for distinguishing IGF1R-AS1-\nregulated regions that are directly bound by chromatin remodelers from those regulated through secondary\nmechanisms.\nAdditionally, we performed complementary network analysis using ATAC-seq data from SMARCA1 knockdown cells\n(new Supplementary Figure 5g, see Response Fig. 9). While MYC was not the top dysregulated gene in SMARCA1\nnetwork as in SMARCA4 (Supplementary Figure 5f, see Response Fig. 9), it was identified among the top 40 hub genes\nin the SMARCA1 network (new Supplementary Figure 5g, see Response Fig. 9), consistent with our findings that both\nremodelers regulate MYC (Fig. 4n-q, Supplementary Figure 4 i-l, see Response Fig. 9, lines 350-352) and interact with\nIGF1R-AS1.\n6. Minor comments\nTypographical error (Fig. 2h). “siNON” should read “siCon” (scramble control).\nLabelling ambiguity (Fig. 3h). The axis label “siAS-1 #1/#2 meta” is unclear. If both siRNAs were co-transfected, please\nindicate “mix” or “pool”.\nResponse: We thank the reviewer for your valuable comment. We apologize for the error and ambiguity. We have\ncorrected “siNON” to “siCon” in Fig. 2h. In Fig. 3j, the label “siAS-1 #1/#2 meta” refers to a gene set enrichment analysis\n(GSEA) performed on the results of a meta-analysis of differentially expressed genes (DEGs) from independent siAS-1 #1\nand siAS-1 #2 knockdown experiments. The two siRNAs were not co-transfected; rather, DEG analyses were conducted\nseparately and then combined via meta-analysis prior to GSEA. We have revised the figure legend accordingly to clarify\nthis point.\nReviewer #3, ECR (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\nResponse: We sincerely appreciate the reviewer's evaluation of our manuscript.\nReviewer #4, expertise in non-coding RNA and transcriptional regulation, super-enhancers, epigenetics and cancer\n(Remarks to the Author):\nIn this manuscript, Yang et al. identify IGF1R-AS1, a previously uncharacterized tumor-specific lncRNA, as a key\nregulator in metastatic castration-resistant prostate cancer (mCRPC). Through integrative multi-omics analyses, the\nauthors demonstrate that IGF1R-AS1 interacts with the chromatin remodelers SMARCA1 and SMARCA4 to orchestrate\nlong-range chromatin looping between the distal MYC enhancer E3 and its promoter, thereby amplifying MYC expression\nand driving tumorigenesis. This work not only unveils a novel trans-acting mechanism by which lncRNAs modulate\noncogenic signaling but also highlights the broader potential of tumor-specific lncRNAs as therapeutic targets in cancer.\nThis is a well-executed study with significant findings. However, some mechanistic details require further refinement\nbefore publication. Below is a detailed review with suggestions for further improvement:\nResponse: We thank the reviewer for the supportive comments.\n1. The manuscript suggests IGF1R-AS1 recruits SWI/SNF to MYC enhancers, but the exact binding sites or motifs\nmediating this interaction are unclear. Additional experiments (e.g., truncation mutants of IGF1R-AS1 to identify\nfunctional domains) could strengthen this claim. Moreover, it is interesting to see whether IGF1R-AS1 directly binds\nSMARCA1 and SMARCA4. Can these two proteins directly bind to IGF1R-AS1 in vitro? If so, which functional domains\nare responsible for the direct interaction?\nResponse: We thank the reviewer for the excellent suggestions.\nFirst, to investigate whether IGF1R-AS1 directly interacts with SMARCA1 and SMARCA4, we performed in vitro RNA-\nprotein binding assay using in vitro-transcribed biotin-labeled IGF1R-AS1 RNA and recombinant His-tagged\nSMARCA4 protein, or Flag-tagged SMARCA1 protein, which demonstrated that IGF1R-AS1 binds directly to\nSMARCA1 protein and SMARCA4 protein (new Fig. 4f-g).\nTo map the IGF1R-AS1 functional motifs corresponding to SMARCA1 and SMARCA4 binding, we analyzed the\nsecondary structure of IGF1R-AS1 (new Fig. 4h), and conducted in vitro RNA pull-down assays using a series of\ntruncated biotin-labeled IGF1R-AS1 fragments (new Fig. 4i). This analysis revealed that nucleotides 512-645 of IGF1R-\nAS1 are critical for the interaction with SMARCA4 protein, while nucleotides 177-407 and 408-511 of IGF1R-AS1 are\nessential for the interaction with SMARCA1 protein.\nBoth SMARCA1 and SMARCA4 contain an ATPase domain in the middle of the protein sequence (new Fig. 4j-k). We\ncloned the full-length, N-terminal domain, ATPase domain, and C-terminal domain of both SMARCA1 and SMARCA4,\neach with a C-terminal Flag tag. We expressed them in VCaP cells and then performed RIP assay using anti-Flag antibody\n(new supplementary Fig. 4g-h), followed by qPCR to analyze the enrichment of IGF1R-AS1 by different protein domains\n(new Fig. 4j-k). The results showed that IGF1R-AS1 mainly binds to the ATPase domain of SMARCA4 and the C-\nterminal domain of SMARCA1 (new Fig. 4j-k).\nThese detailed mapping studies significantly strengthen our findings by identifying the specific RNA motifs and protein\ndomains responsible for the IGF1R-AS1-SMARCA4 and IGF1R-AS1-SMARCA1 interaction. These new data have been\nincorporated into revised Fig. 4 (lines 292-302) (and shown here in Response Fig. 3 for reference).\n2. As CCAT1 is localized in a super-enhancer region, it is extensively transcribed in many types of cancers and has been\ndemonstrated to have a 5' additional extension in HeLa cells (see Cai et al., Nature, 2020), I am wondering whether\nIGF1R-AS1 RNA directly interact with the enhancer RNAs transcribed in E3 region? could them form RNA base pairings?\nThis can explain why IGF1R-AS1 specifically targets the chromatin remodeling complex to this enhancer and potentially\nto thousands of other sites.\nResponse: We thank the reviewer for this insightful suggestion.\nFirst, we analyzed RNA-seq data from prostate cancer cell lines, including VCaP and 22Rv1, both of which express\nIGF1R-AS1. We found that neither the canonical CCAT1 nor its long isoforms are expressed in these prostate cancer cells\n(revised Supplementary Fig. 6a; see Response Fig. 10). In contrast, analysis of RNA-seq data from HCT116, a colon\ncancer cell line known to express high levels of the long CCAT1 isoform9, confirmed robust CCAT1 expression and the\npresence of a super-enhancer marked by strong H3K27ac ChIP-seq signals in this region (revised Supplementary Fig. 6a;\nsee Response Fig. 10). We further examined RNA-seq data from tumor tissue samples in the TCGA cohort, which also\nshowed low CCAT1 expression in prostate cancer (revised Supplementary Fig. 6b; see Response Fig. 10). Collectively,\nthese results indicate that CCAT1 or its long isoforms, is unlikely to participate in IGF1R-AS1-mediated regulation in the\ncontext of our study.\nIn our study, we found that IGF1R-AS1 interacts with SMARCA4 and CTCF through different nucleotide regions (new\nFig. 4h-i, see Response Fig. 3 and new Fig. 6h, see below Response Fig. 4), and knocking down IGF1R-AS1 reduced the\ninteraction between SMARCA4 and CTCF (new Fig. 6i, see below Response Fig. 4), which led to disrupted CTCF\nbinding and reduced CTCF-mediated chromatin interactions around the E3 region in VCaP cells, as well as reduction in\nCTCF looping strength genome-wide (new Fig. 6c-f, see below Response Fig. 5). These data showed that IGF1R-AS1\nacts as a scaffold to promote the interaction between SMARCA4 and CTCF at this enhancer and potentially thousands of\nother sites to regulate gene expression.\n3. While the study shows IGF1R-AS1 does not regulate IGF1R, it would be helpful to explicitly test whether IGF1R-AS1\nknockdown affects other nearby genes (e.g., by RNA-seq or qPCR).\nResponse: We thank the reviewer for this great advice.\nWe performed qPCR to assess the expression of two IGF1R-neighboring genes, SYNM and TTC23, which are expressed\nin VCaP cells. We found that their transcription was not significantly affected by knocking down IGF1R-AS1 in VCaP,\n22RV1, and H727 cells (revised Fig. 3l and Supplementary Fig. 3p-q, line 266, see below Response Fig. 11). These\nresults further support that IGF1R-AS1 functions in trans, rather than in cis, in regulating gene expression.\n4. For the IGF1R-AS1 knockdown studies, it is better to reintroduce IGF1R-AS1 in knockdown cells (e.g., via\noverexpression) to confirm that the observed phenotypes are due to IGF1R-AS1 loss and not off-target effects.\nResponse: We thank the reviewer for this important suggestion. We reintroduced IGF1R-AS1 by transfecting an IGF1R-\nAS1 overexpression construct into VCaP IGF1R-AS1 knockdown cells and observed rescued cell growth, migration, and\ninvasion (new Supplementary Fig. 3e-g, lines 247-248), and a rebound in Myc expression (new Supplementary Fig. 3t-u,\nlines 267-268), confirming that the observed phenotypes are due to IGF1R-AS1 loss but not off-target effects. See below\nResponse Fig. 12.\n5. The Tempus cohort analysis suggests IGF1R-AS1 activity predicts poor survival. Expanding this to other cohorts or\nvalidating in independent datasets would strengthen the biomarker claim.\nResponse: We thank the reviewer for this excellent suggestion.\nWe have now expanded our survival analysis to include two independent patient cohorts (new supplementary Fig. 5l-m,\nsee Response Fig. 13), thereby substantially strengthening the clinical relevance of IGF1R-AS1 as a prognostic\nbiomarker.\nWe first analyzed survival data from a microarray based study in primary prostate cancer (Taylor et al., 201010; n=140)\nand found that high IGF1R-AS1 ATAC-seq based network activity score predicts significantly worse disease-free survival\n(CoxPH p = 0.0135; new Supplementary Figure 5l). We further validated these findings in an independent mCRPC cohort\nfrom the SU2C study (Abida et al., 201911; n=71), where IGF1R-AS1-high patients showed significantly worse overall\nsurvival compared to IGF1R-AS1-low patients (CoxPH p = 0.0146; new Supplementary Figure 5m). This independently\nconfirms our findings from the Tempus mCRPC cohort in a separate patient population. Importantly, we found that\nIGF1R-AS1 stratifies patient outcomes even among MYC-high patients. In the SU2C cohort, patients with both high\nIGF1R-AS1 and high MYC expression had significantly worse survival than MYC-high patients with low IGF1R-AS1 (p\n= 0.0015; new Supplementary Figure 5m). This demonstrates that IGF1R-AS1 provides independent prognostic\ninformation beyond MYC expression alone, consistent with our mechanistic model in which IGF1R-AS1 amplifies MYC-\ndriven oncogenesis through regulation of chromatin architecture. To further assess IGF1R-AS1's clinical utility, we\ncompared its prognostic performance to the expression of Prolaris cell cycle progression markers, which are utilized in an\nFDA-cleared test for prostate cancer risk stratification. IGF1R-AS1 network score demonstrated comparable or superior\nprognostic performance to Prolaris markers z-score as measured by concordance index (new Supplementary Figures 5m)\nWe have revised the Results section (lines 375-379) to describe the multi-cohort validation and added new Supplementary\nFigures 5l-m showing the survival analyses in the Taylor and SU2C cohorts.\n6. Several typos need to be fixed: Page 3: \"evolutionally\" → \"evolutionarily\"; Page 9: \"fueling cancer progression\" →\n\"driving cancer progression\"\nResponse: We apologize for these errors (lines 130 and 516). We have corrected them and carefully checked the typos\nthroughout the manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have comprehensively addressed my concerns. The incorporation of Nanopore direct RNA sequencing, the\nvalidation in a second CRPC cell line (22RV1), the detailed domain mapping of the lncRNA-protein interactions, and the\nCTCF HiChIP analysis strengthened the novelty, mechanistic depth, and robustness of the study. I have no further\ncomments.", "reviewer_2": "Overall, the authors’ responses are clear, well-reasoned, and comprehensive, and the issues raised have been well\naddressed. I have minor comments for the paper.\n1. Introduction: ‘Opening new avenues for therapeutic intervention’ feels too ambitious given the current data. The study\ncompellingly demonstrates tumor-specific lncRNA-mediated distal regulation and 3D chromatin architecture, but the\nevidence does not yet support clinical or therapeutic claims.\n2. Response 7. The current distribution plot is not straightforward for evaluating prevalence. The authors’ point is that the\nIGF1R-AS1 junction is more prevalent in metastatic vs localized tumors, showing raw sample counts obscure the\ncomparison because the metastatic (Met) and localized (PCa) cohorts differ in size. A pie chart summarizing the\nproportions should be sufficient\n3. Following the authors’ clarification of the rationale for studying SMARCA4, I recommend inserting concise biological\nrelevance and functional alignment justifications to strengthen clarity and coherence.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "The revised manuscript has fully addressed all of my concerns, and I am pleased to support its publication."}, "author_response": "Point-by-Point response\nRE: NCOMMS-25-35735A- Tumor-specific lncRNA IGF1R-AS1 trans-regulates chromatin interactions\nassociated with oncogenic MYC signaling\nWe would like to thank the editor for the opportunity to allow us to further revise our manuscript and thank all\nreviewers for your insightful and supportive comments, which have significantly improved the quality of our\nwork. As requested, we have rewritten a few parts of the manuscript. We believe that these changes have\nresulted in a more solid and cohesive manuscript. We will now provide a point-by-point response to the\nreviewers’ comments. We hope the Reviewers and the editor will find this manuscript to be improved and\nsuitable for publication.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have comprehensively addressed my concerns. The incorporation of Nanopore direct RNA sequencing, the\nvalidation in a second CRPC cell line (22RV1), the detailed domain mapping of the lncRNA-protein interactions, and the\nCTCF HiChIP analysis strengthened the novelty, mechanistic depth, and robustness of the study. I have no further\ncomments.\nResponse: We are sincerely grateful for the reviewer’s positive feedback.\nReviewer #2 (Remarks to the Author):\nOverall, the authors’ responses are clear, well-reasoned, and comprehensive, and the issues raised have been well\naddressed. I have minor comments for the paper.\nResponse: We greatly appreciate the reviewer's supportive comment.\n1. Introduction: ‘Opening new avenues for therapeutic intervention’ feels too ambitious given the current data. The study\ncompellingly demonstrates tumor-specific lncRNA-mediated distal regulation and 3D chromatin architecture, but the\nevidence does not yet support clinical or therapeutic claims.\nResponse: We thank the reviewer for this comment. We removed the phrase “opening new avenues for therapeutic\nintervention” from the introduction.\n2. Response 7. The current distribution plot is not straightforward for evaluating prevalence. The authors’ point is that the\nIGF1R-AS1 junction is more prevalent in metastatic vs localized tumors, showing raw sample counts obscure the\ncomparison because the metastatic (Met) and localized (PCa) cohorts differ in size. A pie chart summarizing the\nproportions should be sufficient.\nResponse: We thank the reviewer for this suggestion. We retained only the pie chart and removed the bar plot from the\nfigure, making it straightforward to evaluate the distribution of the IGF1R-AS1 junction in PCa patient samples. See\nrevised Supplementary Figure 3a below.\n3. Following the authors’ clarification of the rationale for studying SMARCA4, I recommend inserting concise biological\nrelevance and functional alignment justifications to strengthen clarity and coherence.\nResponse: We thank the reviewer for this great suggestion. We added the justification in the discussion: “Due to the\nSWI/SNF complex’s critical role in PCa biology and SMARCA4’s alignment with IGF1R-AS1 in the regulation of\nchromatin accessibility, we focused on investigating SMARCA4’s participation in IGF1R-AS1-regulated MYC promoter-\nenhancer interaction.” (line 489-492)\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.\nResponse: We greatly appreciate the reviewer's effort in reviewing our manuscript.\nReviewer #4 (Remarks to the Author):\nThe revised manuscript has fully addressed all of my concerns, and I am pleased to support its publication.\nResponse: We are grateful that the reviewer supports the publication of our manuscript."}]

Decision letter