ESRP1 deficiency promotes doxorubicin resistance by modulating alternative splicing of SEPTIN9 and SPTBN1-mediated cytoskeleton organization in breast cancer

ESRP1 deficiency promotes doxorubicin resistance by modulating alternative splicing of SEPTIN9 and SPTBN1-mediated cytoskeleton organization in breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article ESRP1 deficiency promotes doxorubicin resistance by modulating alternative splicing of SEPTIN9 and SPTBN1-mediated cytoskeleton organization in breast cancer Jing Zhang, Wen Li, Qingling Song, Jie Wang, Libin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7485233/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background Breast cancer remains one of the most prevalent malignancies among women, with doxorubicin resistance posing a significant challenge that undermines treatment success and survival outcomes. Aberrant alternative splicing (AS), driven by dysregulation or mutations in splicing factors (SFs), is implicated in cancer initiation, progression, and drug resistance. This study aims to investigate the role of the epithelial cell-specific splicing factor ESRP1 in regulating doxorubicin resistance in breast cancer, focusing on how ESRP1 deficiency contributes to AS changes that promote chemoresistance. Methods We analyzed RNA-sequencing (RNA-seq) data from doxorubicin-resistant (MCF7-DR) and parental (MCF7) breast cancer cell lines to identify enhanced AS events (ASEs) and changes in ESRP1 expression. An integrative analysis combining crosslinking immunoprecipitation (CLIP-seq) data and The Cancer Genome Atlas (TCGA) database was performed to validate ESRP1 binding targets and assess the impact of ESRP1-mediated splicing on cytoskeleton organization and small GTPase-mediated signaling. Results We observed extensive AS changes and downregulated ESRP1 expression in doxorubicin-resistant cells. Integrative analysis revealed that ESRP1 directly regulates the splicing of SEPTIN9 and SPTBN1, two genes involved in cytoskeletal remodeling and small GTPase-mediated signaling. ESRP1 deficiency was associated with increased doxorubicin resistance, in part by driving critical ASEs linked to cytoskeletal organization. Conclusions Our findings suggest that ESRP1 plays a crucial role in modulating doxorubicin resistance through its regulation of ASEs in breast cancer cells. Targeting the ESRP1-dependent splicing network may offer new strategies to overcome chemoresistance and improve patient outcomes. ESRP1 alternative splicing doxorubicin resistance SEPTIN9 SPTBN1 breast cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Breast cancer is a disease characterized by abnormal growth of breast cells that are out of control and form tumors 1 . Breast cancer is one of the most common malignant tumors in women 2 . In 2022, 2.3 million women worldwide were diagnosed with breast cancer, and 670,000 died from it 3 . Chemotherapy resistance is a major obstacle in the treatment of breast cancer, severely affecting the long-term survival rate of patients 4 . Doxorubicin is a key treatment for breast cancer 5 , 6 , but its common resistance leads to recurrence and metastasis, poor prognosis and low survival 7 . The mechanisms of doxorubicin resistance include the overexpression of MDR transporters, DNA damage repair, epithelial-mesenchymal transition (EMT), and changes in the tumor microenvironment 8 – 10 . Thus, exploring the molecular mechanisms of doxorubicin resistance in breast cancer cells is of utmost importance for developing new therapeutic strategies to overcome or prevent drug resistance. Alternative splicing (AS) is critical for the regulation of human gene expression, significantly contributing to the diversity of functional proteins 11 , 12 . In chemotherapy-resistant tumors, extensive and specific RNA splicing abnormalities can alter tumor cell responses to treatment. For instance, in breast cancer, AS of the Bcl-x gene leads to the production of pro-apoptotic and anti-apoptotic isoforms, influencing the survival of cancer cells in response to chemotherapeutic agents. Additionally, splicing of the CD44 gene generates various isoforms that enhance tumor invasiveness and metastatic potential, further complicating treatment outcomes 13 . AS has been shown to play a pivotal role in the prognosis, survival, and drug resistance of breast cancer 14 – 16 , with specific mechanisms still requiring further exploration. Splicing factors (SFs) play pivotal roles in the recognition of splice sites and the assembly of spliceosomes during AS 17 . Studies have highlighted the regulatory role of SFs such as SRSF1 and hnRNPA1 in modulating ASEs associated with doxorubicin resistance, underscoring the importance of understanding these pathways to develop targeted therapies. Epithelial splicing regulatory protein 1 (ESRP1) is an RNA-binding protein (RBP) and SF that regulates AS of mRNA, which plays an important role in maintaining cell epithelial characteristics and regulating tumor progression 18 , 19 . ESRP1 mediates aberrant splicing in cancer cells, and plays a role in cell proliferation and chemoresistance through the regulation of apoptosis and autophagy 20 . In breast cancer, previous study has shown that ERα-ESRP 1/2 axis plays a role in its occurrence and development by controlling the splicing patterns of related genes 21 . Additional research has demonstrated that ESRP1 can target the splicing of α6 integrin mRNA to increase the sensitivity of triple-negative breast cancer cells to paclitaxel 22 . These results imply the significant role of ESRP1 in chemoresistance of breast cancer. However, the specific regulatory mechanisms are not clear. In this study, we aim to investigate the role of ESRP1 in doxorubicin resistance of breast cancer and to explore the AS network regulated by ESRP1. We explored SFs and ASEs in doxorubicin-resistant MCF7 (MCF7-DR) human breast cancer cells using bioinformatics and expression analysis methods based on previously reported RNA-seq dataset (GSE174152). We demonstrated that the expression of ESRP1 is reduced in doxorubicin-resistant breast cancer cells. The functions of ESRP1 as biomarker have also been verified in The Cancer Genome Atlas (TCGA) database, providing the potential for the prevention and intervention of doxorubicin resistance in breast cancer. Then, we explored the possible binding sites of ESRP1 using CLIP-seq dataset (GSE233931). Further analysis suggests that ESRP1 can regulate the AS of SEPTIN9 and SPTBN1, thereby affecting pathways such as the cytoskeleton organization and small GTPase-mediated signal transduction (Fig. 1 A). Our findings highlight the association between ESRP1 and doxorubicin resistance in breast cancer, potentially offering new molecular targets and a theoretical foundation for the treatment of breast cancer. 2 Materials and methods 2.1 Access to and processing of public data We used the transcriptome sequencing data (RNA-seq) of parental and doxorubicin-resistant MCF7 (MCF7-DR) human breast cancer cells. The accession numbers of Gene Expression Omnibus (GEO) database were GSE174152. Public sequencing data were obtained from the Sequence Read Archive (SRA). SRA Run data files were transformed into fastq format with NCBI SRA Tool fastq-dump. Raw RNA sequencing data were processed using fastp (v0.23.4) 23 to remove sequencing adapters and filter out low-quality bases. Cleaned reads were aligned to the human reference genome (GRCh38) using the STAR aligner (v2.7.10b) 24 . Uniquely mapped reads were retained for further analysis, including read count and expression level calculations, reported as fragments per kilobase of exon per million mapped reads (FPKM). 2.2 Splicing analysis using SUVA SUVA (splicing site usage variation analysis) focused on different usages of each splice site 25 . It redefined splicing formula as clustered simple junction pairs to overcome the complex splicing patterns. A pipeline was implemented to identify and quantify the five types of AS events (ASEs) defined by SUVA across whole dataset, and then to screen out statistically significant regulated AS (RAS) events by comparing two comparative samples or two groups of samples with replicates. Splice junction (SJ) reads and non-junction reads from mapping result of STAR was the input of SUVA. 2.3 Co-expression analysis of splicing ratio of RAS and expression of DE SFs The software DEseq2 26 , which is specifically used to analyze the differential expression of genes, was applied to screen the raw count data for differentially expressed genes (DEGs). The results were analyzed based on the fold change (FC ≥ 2 or ≤ 0.5) and false discovery rate (FDR ≤ 0.05) to determine whether a gene was differentially expressed. Then expression profile of differentially expressed SFs were filtered out from all DEGs according to a catalogue of 603 SFs of human was retrieved from Gene Ontology (GO) database and previous report 27 . We constructed a co-expression network to explore correlations between differentially expressed SFs and RAS events. Correlations with |Pearson’s r| ≥ 0.99 and p-value ≤ 0.01 were included in the network. RNA-binding interactions were verified using the RNAinter database ( http://www.rnainter.org/ ). 2.4 GO functional enrichment analysis To identify functional categories of genes, we employed the clusterProfiler package (v4.6.2) 28 , which enabled us to determine GO terms and KEGG pathways. 2.5 TCGA-breast cancer data analysis For the analysis of ESRP1-regulated RAS in breast cancer, we downloaded clinical and genomic data from TCGA database, specifically targeting the breast cancer cohort (BRCA). We selected samples based on ESRP1 expression levels. Samples were classified into two groups: those with high ESRP1 expression (top 15 samples) and those with low ESRP1 expression (bottom 15 samples). To assess the overlap between the RAS identified from TCGA and ESRP1-targeted RAS detected in the MCF7-DR cell line data, we performed a comparative analysis. 2.6 Analysis of CLIP-seq data of ESRP1 We used the Crosslinking immunoprecipitation-high-throughput-sequencing data (CLIP-seq) for ESRP1. The accession numbers of GEO database were GSE233931. After reads were aligned onto the genome, only uniquely mapped reads were used for the following analysis. Reads with at least 1 bp overlap were clustered as peaks. For each gene, computational simulation was used to randomly generated reads with the same number and lengths as reads in peaks. The outputting reads were further mapped to the same genes to generate random max peak height from overlapping reads. The whole process was repeated for 500 times. All the observed peaks with heights higher than those of random max peaks (p-value < 0.05) were selected. The IP and input samples were analyzed by the simulation independently, and the IP peaks that have overlap with Input peaks were removed. The target genes of IP were finally determined by the peaks and the binding motifs of IP protein were called by HOMER software (v5.1) 29 . To further explore the functional significance of the identified binding sites, we cross-referenced the ESRP1-targeted genes with the RAS genes identified in the doxorubicin-resistant MCF7 (MCF7-DR) cell line data and TCGA. 2.7 Statistical Analysis and Data Visualization If not specified, all bioinformatics analyses were performed with R software (v4.3.2) to compute statistics and generate plots throughout this manuscript. Principal component analysis (PCA) was performed using the R package factoextra ( https://cloud.r-project.org/package=factoextra ) to display the clustering pattern of samples based on the top two components. The sequencing data and genomic annotations were visualized with a script we developed in-house by normalizing the reads by the tags per million (TPM) of each gene. The pheatmap package in R was used for clustering based on Euclidean distance. For the analysis of overall survival among patients, Kaplan-Meier curves were generated using the “survival” R package. 3 Results 3.1 Transcriptome analysis identifies differential ASEs in control and doxorubicin-resistant breast cancer cells Breast cancer is one of the most common malignancies worldwide. AS produces complex and dynamic variations in protein isoforms that play a certain role in chemoresistance in breast cancer. For in-depth analysis of the changes in ASEs during the development of chemoresistance in breast cancer, we downloaded the transcriptome dataset GSE174152, which includes two doxorubicin-resistant breast cancer cell samples (MCF7-DR) and two parental breast cancer cell samples (MCF7). Five different types of ASE model defined by SUVA according to splicing site usage variation 25 . We first compared the differential ASEs between the two groups of cell samples as a whole. As shown in Fig. 1 B, RAS events identified by SUVA were mainly alt5p and alt3p. The splicing events were corresponding to classical splicing events, in which A5SS and cassetteExon events accounted for a large proportion (Fig. 1 C). Since a splicing event involves two transcripts, and these two transcripts may account for a very small proportion of the overall gene expression, we aim to find the more dominant spliced transcripts. We filtered the splicing events based on the proportion of all reads in the region (pSAR) that are different for the variable splicing events. Splicing events with pSAR less than 50% were filtered out, and we selected 1,021 events with pSAR ≥ 50% for further analysis (Fig. 1 D). The control and doxorubicin-resistant group can be clearly distinguished in the first principal component by using the splicing ratio of RAS events for PCA, indicating a significant increase in the heterogeneity of splicing patterns with the onset of chemotherapy resistance (Fig. 1 E). This change may reflect fundamental changes in gene expression regulation mechanisms during the development of chemotherapy resistance, particularly an increase in complexity at the post-transcriptional level. To analyze the RAS regulation patterns in the two groups, we used a heatmap to display the distribution of ratios for all differential splicing events (Fig. 1 F). GO analysis was applied to elucidate biological processes, which indicated that these differentially spliced genes (RASGs) were significantly enriched in cytoplasmic microtubule organization, regulation of actin filament-based processes, and regulation of actin cytoskeleton organization (Fig. 1 G). Further, we presented the top 5 enriched pathways and the network diagram of genes included in the RAS (Fig. 1 H). Overall, our findings indicate that a large number of ASEs are associated with doxorubicin resistance in breast cancer. These events can modulate key functional pathways. 3.2 Splicing factor ESRP1 regulates doxorubicin resistance-related ASEs in breast cancer SFs are a class of RBPs that play pivotal roles in the recognition of splice sites and the assembly of spliceosomes during AS 30 , 31 . Aberrant AS by either dysregulation or mutations of SFs contributes to cancer initiation and progression 32 . To thoroughly investigate the regulatory mechanisms of SFs in AS associated with doxorubicin resistance in breast cancer, we analyzed the DEGs and SFs in the MCF7-DR and MCF7 cell samples of the dataset GSE174152. We identified a total of 36 differentially expressed SFs (Fig. 2 A). Further, co-expression analysis was performed between SFs and ASEs, selecting SF-RAS pairs with a pearson correlation coefficient greater than 0.99 and a p-value less than 0.01. Subsequently, the RNAinter database was used to identify pairs of SF and RASGs with evidence of binding. Finally, functional enrichment analysis was conducted on these RASGs, and the top 10 pathways along with their corresponding RAS and upstream SF were selected to draw a network diagram. A total of 9 SFs were retained, including the splicing factors ESPR1, RBM23, MSI1, IGF2BP3, CELF2, ESRP2, KHDHBS3, RBM47, and NOVA1. These SF genes can affect the development of doxorubicin resistance in breast cancer by regulating RASEs such as small GTPase-mediated signal transduction and actin filament-related pathways (Fig. 2 B). Among them, the expression of RBM23, ESPR1, RBM47, ESRP2, and MSI1 were decreased in MCF7-DR cells, while the expression of CELF2, KHDRBS3, IGF2BP3, and NOVA1 were increased (Fig. 2 C). 3.3 TCGA data analysis validates ESRP1 cause a wide range of ASEs in breast cancer To further investigate the specific mechanism by which ESRP1 inhibits doxorubicin resistance in breast cancer, we downloaded breast cancer-related data from the TCGA database, and selected 15 samples with the highest and lowest expression of ESPR1 for RAS analysis (Fig. 3 A). Using the SUVA software, we found that the main types of differential RASEs were alt5p and alt3p (Fig. 3 B). Then, by screening splicing events with different proportions of ASEs in all reads in the region, events with pSAR less than 50% were screened out. We selected 1,904 splicing events with pSAR > = 50% for subsequent analysis (Fig. 3 C). The heat map shows the distribution of RASEs splicing ratio in two groups of samples (Fig. 3 D). Using the splicing ratio of these events, PCA results showed that ESRP1 high expression and low expression samples could be significantly distinguished (Fig. 3 E). Furthermore, we performed GO enrichment analysis on these RASGs, which were mainly enriched in biological pathways such as regulation of small GTPase mediated signal transduction and regulation of cell-matrix adhesion (Fig. 3 F). In addition, we conducted an overlap analysis between the differential RASEs identified from TCGA and the RASEs obtained in ESRP1 co-expression, and selected 45 overlapping RASEs (Fig. 3 G). The biological functions of overlapping ERSP1-modulated genes were analyzed using GO enrichment analysis (Fig. 3 H). The results showed that these ERSP1-regulated RAS targets were mainly involved in protein localization to cell periphery, regulation of small GTPase mediated signal transduction, actin filament organization, cell projection organization. 3.4 CLIP-seq identifies ESRP1 binding sites To identify ESRP1’s binding sites, we downloaded the CLIP-seq dataset (GSE233931), which established SGC7901 gastric cancer cell line with stable overexpression of ESRP1 and conducted CLIP seq for ESRP1. A total of 576 binding peaks were detected in both IP experiments (Fig. 4 A). ESRP1 mainly bound to introns and CDS (Fig. 4 B). Furthermore, GO analysis was conducted on these ESRP1 binding genes, which were primarily enriched in RNA splicing and protein-RNA complexes assembly (Fig. 4 C). The motif enrichment analysis of peak on ESRP1-binding genes showed the top five genetic sequence. The top motif which ESRP1 bound to was 5’-CGUUGCU-3’ (Fig. 4 D). By overlap analysis of ESRP1-binding genes obtained from IP and differentially AS genes detected in the previous section, we identified three genes, DDR1, SEPTIN9 and SPTBN1 (Fig. 4 E). These results suggest that ESRP1 can affect doxorubicin resistance in breast cancer by regulating the AS of DDR1, SEPTIN9, and SPTBN1. 3.5 ESRP1 regulates the AS of SEPTIN9 and SPTBN1 To study the role of SEPTIN9 and SPTBN1 in doxorubicin resistance in breast cancer, we analyzed the changes of AS in MCF7-DR cells. A splicing event on gene SEPTIN9 is shown in Fig. 5 A. The reads distribution map showed that SEPTIN9 preferentially selected the proximal 5' splice sites, tending to retain shorter transcripts in the DR group, and the proportion of this splicing event increases in the DR group and the low ESRP1 expression group. A splicing event on another gene SPTBN1 is shown in Fig. 5 B, SPTBN1 preferentially selected the distal 5'splicing sites in the DR group, the longer transcripts were more selected in the DR group, and the proportion of this splicing event decreases in the DR group and the low ESRP1 expression group. These suggested that the AS of SEPTIN9 and SPTBN1 are related to doxorubicin resistance in breast cancer. This is consistent with the previous study reported that splice isoform of SEPTIN9 and SPTBN1 is crucial for chemotherapeutic drug resistance in breast cancer. 4 Discussion In this study, we investigated the role of AS in doxorubicin resistance in breast cancer, focusing on the splicing factor ESRP1. Our analysis revealed a significant increase in RAS events in doxorubicin-resistant MCF7 (MCF7-DR) cells compared to parental MCF7 cells. We identified a total of 1,021 differentially spliced events that are potentially linked to chemoresistance. Notably, splicing alterations in key genes, such as SEPTIN9 and SPTBN1, were shown to influence critical pathways related to cytoskeletal organization and cellular signaling. These findings underscore the importance of AS as a mechanism that contributes to the development of drug resistance in breast cancer and highlight ESRP1 as a crucial regulator that may serve as a potential therapeutic target. Our study revealed that the ASEs identified in MCF7 and MCF7-DR cells were significantly enriched in pathways related to cytoplasmic microtubule organization, regulation of actin filament-based processes, and regulation of actin cytoskeleton organization. These pathways are crucial for maintaining cell shape, motility, and intracellular transport, which are essential for tumor progression and metastasis. The enrichment of these splicing events suggests that alterations in the splicing landscape may enhance the invasive potential of breast cancer cells, thereby contributing to doxorubicin resistance. Previous studies have documented similar associations between cytoskeletal dynamics and chemoresistance 33 – 36 , indicating that changes in cell morphology and motility can affect drug uptake and efficacy. Our findings expand upon this literature by demonstrating that specific splicing alterations in key regulatory genes can directly influence these critical pathways. The implications of our results suggest potential therapeutic applications, where targeting the splicing machinery, specifically ESRP1 and its downstream effects, may offer a novel strategy to counteract chemoresistance in breast cancer. Future research should focus on elucidating the precise molecular mechanisms by which these splicing alterations affect drug resistance and exploring the potential of splicing modulators as adjunct therapies in the treatment of resistant breast cancer. In our investigation of AS in doxorubicin-resistant breast cancer cells, we identified several SFs that may play critical roles in mediating chemoresistance. Among them, CELF2 has shown antitumor activity in a variety of cancer models, including breast cancer, lung cancer, gastric cancer, and ovarian cancer. Loss of CELF2 can promote drug resistance in squamous cell carcinoma (SCC) 37 . IGF2BP3 promotes the progression and cisplatin resistance of laryngeal squamous cell carcinoma (LSCC) through the UBA2-PI3K pathway 38 . NOVA1 exhibits splicing regulatory activity in natural breast tumors 39 . NOVA1 to mediate doxorubicin resistance of hepatocellular carcinoma (HCC) cells by as a sponge for miR-3129-5p 40 . KHDRBS3 can alter CD44 isoform expression, enhancing the stemness of basal-like breast cancer cells 41 . Circ_RBM23 promotes sorafenib resistance, malignant proliferation, migration, and invasion in HCC by regulating the miR-338-3p/RAB1B axis 42 . RBM47 inhibits the progression and metastasis of breast cancer by regulating the splicing and stabilization of its target mRNA 43 , 44 . Increasing evidence suggests that MSI1 is associated with resistance to cancer therapy, as its depletion leads to reduced expression of the catalytic subunit of DNA-PK, resulting in increased DNA damage 45 . Loss of ESRP2 leads to the mesenchymal subtype expression of genes associated with EMT, participating in tumor progression 46 , 47 . Apart from the above SFs, ESRP1 stands out as a particularly significant candidate. ESRP1 is known for its involvement in maintaining epithelial characteristics and regulating splicing patterns of genes implicated in tumor progression 19 , 48 . Existing studies have indicated that ESRP1 is closely related to tumor chemoresistance, and its role in breast cancer chemoresistance remains unclear 21 , 49 , 50 . Previous research has highlighted the importance of SFs like SRSF1 and hnRNPA1 in promoting drug resistance by influencing the splicing of key oncogenes and tumor suppressors. However, our study emphasizes ESRP1's unique contribution, as its downregulation in resistant cells correlates with a shift towards splicing patterns that favor invasive phenotypes and enhance cell motility. The choice to focus on ESRP1 was driven by its dual role in regulating AS and maintaining epithelial integrity, both of which are crucial in the context of breast cancer. By demonstrating that ESRP1 modulation can influence splicing events associated with cytoskeletal organization and cellular dynamics, we provide new insights into how SFs can affect drug sensitivity. These findings align with existing literature that links aberrant splicing to cancer progression but extend the discussion by identifying ESRP1 as a key player in the context of chemotherapy resistance. Looking ahead, further research should explore the specific mechanisms by which ESRP1 regulates AS and its impact on the efficacy of chemotherapeutic agents. Additionally, targeting ESRP1 or its downstream splicing events may represent a promising therapeutic strategy to overcome drug resistance in breast cancer. Investigating the potential of splicing modulators in combination with conventional therapies could pave the way for more effective treatment regimens for patients facing resistant breast cancer. Our integrated analysis utilizing TCGA, cell line data, and CLIP-seq results identified several ASEs that are potentially regulated by ESRP1 and associated with chemotherapy resistance. DDR1 can promote the formation of dense structures around the tumor by organizing collagen fibers, preventing immune cells from entering the tumor, thereby facilitating the tumor's immune evasion and chemotherapeutic resistance 51 , 52 . Septins are a family member of polymeric GTP-binding proteins that constitute a major component of the cytoskeleton 53 , 54 . SEPTIN9 has multiple isoforms and exhibits different expression patterns and functions in breast cancer cells 55 . The overexpression of SEPTIN9 isoforms occurs in about 30% of human breast cancer cases and is associated with poor prognosis and resistance to microtubule-targeting anticancer agents 55 , 56 . SPTBN1 encodes the β II subunit of spectrin (a cytoskeletal protein) to maintain cell morphology and is involved in the regulation of DNA damage repair, angiogenesis, and stemness maintenance 57 . It has also been reported that SPTBN1 plays a significant role in the pathogenesis, progression, and chemoresistance of many types of cancer 58 – 62 . NUMA1 is a cell cycle protein that is upregulated in breast cancer and is important for maintaining the characteristics of breast cancer stem cells (BCSCs) 63 . SPTAN1 is an important cytoskeletal protein and signaling molecule that can positively and negatively impact cancer progression depending on its localization and regulation 64 . LRP8 has been identified as a novel positive regulator in triple negative breast cancer (TNBC) and can facilitate the formation of dense structures around the tumor by organizing collagen fibers, preventing immune cells from entering the tumor, thereby promoting tumor immune evasion and chemotherapeutic resistance 65 . Notably, genes such as SEPTIN9 and SPTBN1 emerged as key candidates, demonstrating significant changes in splicing patterns across doxorubicin-resistant cell lines and patient samples. SEPTIN9 is involved in cytoskeletal dynamics, which plays a critical role in cell migration and invasion—processes known to contribute to drug resistance in breast cancer. Similarly, SPTBN1 is essential for maintaining cell shape and facilitating intracellular signaling, further linking its splicing variants to cellular responses to chemotherapeutic agents. These findings underscore the pivotal role of ESRP1 in modulating splicing events that influence key biological pathways related to drug resistance. By highlighting the interplay between splicing regulation and chemotherapy response, our study contributes to the growing body of literature connecting aberrant splicing with cancer progression and treatment failure. Future research should aim to elucidate the precise molecular mechanisms through which ESRP1 influences these splicing events and how they, in turn, affect drug sensitivity. Additionally, exploring the therapeutic potential of targeting ESRP1 or its downstream splicing targets could lead to novel strategies for overcoming chemoresistance in breast cancer. Investigating the efficacy of splicing modulators in conjunction with traditional chemotherapy may enhance treatment outcomes for patients facing drug-resistant tumors, ultimately advancing the field of personalized cancer therapy. In conclusion, our study highlights the critical role of AS in mediating doxorubicin resistance in breast cancer, with a specific focus on the splicing factor ESRP1. We identified key ASEs regulated by ESRP1 that are associated with pathways relevant to tumor progression and drug resistance, such as cytoskeletal organization. These findings not only enhance our understanding of the molecular mechanisms underlying chemoresistance but also suggest potential therapeutic targets for improving treatment outcomes in breast cancer. However, this study is not without limitations. The reliance on cell line models, while valuable for initial insights, may not fully capture the complexity of tumor biology present in actual patient samples. Furthermore, the functional significance of the identified splicing events and their direct contributions to drug resistance need to be validated through in vivo studies. Future research should aim to explore these mechanisms in more diverse and clinically relevant models, as well as investigate the potential of splicing modulators as therapeutic strategies to overcome chemoresistance in breast cancer. By addressing these limitations, we can further elucidate the therapeutic implications of our findings and advance the field of precision oncology. Declarations Author contributions statement Conception: JW and LC. Interpretation or analysis of data: WL and JZ. Preparation of the manuscript: WL, JZ and LC. Revision for important intellectual content: WL, JZ and JW. Supervision: LC. Ethics Statement Not applicable. Consent for participate Not applicable. Consent for publication Not applicable. Conflict of Interests Statement The Authors declare that there is no conflict of interest. Funding This research received no specific grant from any funding agency in the public, private or not-for-profit commercial sectors. Data availability statement The data that support the findings of this study are available in Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/. These data were derived from the following resources available in the public domain: GSE174152, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174152. GSE233931, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE233931. References Katsura C, Ogunmwonyi I, Kankam HKN, Saha S. Breast cancer: presentation, investigation and management. Br J Hosp Med. 2022;83(2):1–7. 10.12968/hmed.2021.0459 . Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. 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02:32:23","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146181,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7485233/v1/08c359abe45f901d4a0681bd.html"},{"id":91931855,"identity":"8224e396-b7d0-496e-aa5e-9b16570e8b1d","added_by":"auto","created_at":"2025-09-23 02:32:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":364099,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal profiles of AS in parental MCF7 and doxorubicin-resistant MCF7 (MCF7-DR) cells. \u003cstrong\u003e(A)\u003c/strong\u003eAnalysis workflow. RNA-seq data from MCF7 and MCF7-DR were used to identify RAS events via SUVA. DEGs and SFs were then integrated to construct an SF–RAS co-disturbed network, focusing on ESRP1. Additional validation was performed using TCGA BRCA dataset and ESRP1’s CLIP-seq binding profile (GSE233931).\u003cstrong\u003e (B)\u003c/strong\u003eDistribution of the five SUVA-defined ASE types (alt3p, alt5p, contain, olp, ir) detected in MCF7 vs MCF7-DR cells. The y-axis shows the number of RAS events in each category. \u003cstrong\u003e(C)\u003c/strong\u003eAnnotation of the SUVA-detected RAS events to classical AS event types, including A5SS, A3SS, cassetteExon, mutually exclusive exons (MXE), and intron retention, among others. The bar plot indicates how many RAS events correspond to each classical AS category. \u003cstrong\u003e(D)\u003c/strong\u003eBar plot showing the number of RAS events at different thresholds of pSAR (proportion of Splicing event-specific Reads among all reads in that region). Events with higher pSAR values have a greater relative abundance in the respective splicing site. \u003cstrong\u003e(E)\u003c/strong\u003ePCA of RAS events for which pSAR is greater than 50%. The x-axis (Dim1) explains 99.2% of the variance in splicing ratios, clearly separating doxorubicin-resistant (DR) samples from controls, while the y-axis (Dim2) explains 0.4%. \u003cstrong\u003e(F)\u003c/strong\u003eHeatmap of splicing ratios for RAS events (pSAR \u0026gt; 50%), illustrating hierarchical clustering of the four samples (two control and two DR). Red and blue represent relative differences (z-scores) in splicing ratios. \u003cstrong\u003e(G)\u003c/strong\u003eGO enrichment analysis of RASGs. The bubble color denotes the statistical significance (p-value), and the bubble size reflects the number of RASGs enriched in each biological process (BP) term. \u003cstrong\u003e(H)\u003c/strong\u003e Network diagram connecting the top five enriched GO terms (center) with the associated RASGs (peripheral nodes). Node size indicates the gene count within each GO term, and edges signify membership of a gene in a particular biological process.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7485233/v1/21de2e2571d292d834cb0205.png"},{"id":91934478,"identity":"5241ab4c-4b46-4ae2-aa4c-7bfb9134af1a","added_by":"auto","created_at":"2025-09-23 02:40:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2156016,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of key SFs in parental MCF7 and doxorubicin-resistant MCF7 (MCF7-DR) cells.\u003cstrong\u003e (A)\u003c/strong\u003e Venn diagram showing the overlap between 5,184 DEGs and 603 curated SF genes in MCF7 vs. MCF7-DR cells. A total of 36 SFs are identified as DEGs (in the overlapping region). \u003cstrong\u003e(B)\u003c/strong\u003eSF–RAS interaction network. Nine differentially expressed SFs (orange triangles) are correlated with RAS events enriched in biological processes relevant to cytoskeletal organization and small GTPase-mediated signal transduction (outer ring). Edges represent either SF–RAS or RAS–GO associations, indicating the functional linkage between each SF and its downstream splicing targets. \u003cstrong\u003e(C)\u003c/strong\u003e Heatmap of normalized expression levels for the nine differentially expressed SFs in two control (MCF7) and two doxorubicin-resistant (MCF7-DR) samples. The color scale (z-scores) ranges from blue (lower expression) to red (higher expression).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7485233/v1/c589b713d5011609e3fc27db.png"},{"id":91931859,"identity":"3c075bcc-c8d7-4b8c-ad85-03d4586aff6e","added_by":"auto","created_at":"2025-09-23 02:32:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1933759,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eESRP1-RAS events in the TCGA BRCA dataset. (A)\u003c/strong\u003e Box plot comparing ESRP1 expression in two groups of breast cancer patients from the TCGA cohort (ESRP1-low vs. ESRP1-high). The y-axis shows normalized ESRP1 expression, and each point represents an individual sample. \u003cstrong\u003e(B)\u003c/strong\u003e Distribution of five SUVA-defined AS event categories (alt5p, alt3p, contain, olp, ir) in TCGA samples. The x-axis indicates the number of RAS events in each category. \u003cstrong\u003e(C)\u003c/strong\u003e Bar plot illustrating the number of RAS events across varying thresholds of pSAR in TCGA samples. Higher pSAR implies greater abundance of the corresponding AS event. \u003cstrong\u003e(D)\u003c/strong\u003eHeatmap depicting splicing ratios (z-scores) for RAS events with pSAR \u0026gt; 50% in individual TCGA samples. Samples are hierarchically clustered into high-ESRP1 (orange) and low-ESRP1 (blue) groups. \u003cstrong\u003e(E)\u003c/strong\u003e PCA of the same RAS events (pSAR \u0026gt; 50%), showing clear separation between high-ESRP1 (red) and low-ESRP1 (blue) samples. Dim1 and Dim2 represent the two principal components capturing the most variance in splicing patterns. \u003cstrong\u003e(F)\u003c/strong\u003e GO enrichment analysis of RASGs between the high- and low-ESRP1 expression groups. The bubble size reflects the number of genes, whereas bubble color indicates the statistical significance (adjusted p-value). \u003cstrong\u003e(G)\u003c/strong\u003e Venn diagram comparing the ASEs identified in TCGA samples with those found in ESRP1 co-expression analysis from MCF7/MCF7-DR. The overlapping region (n=45) represents common ASEs potentially regulated by ESRP1 in both datasets. \u003cstrong\u003e(H)\u003c/strong\u003e GO enrichment analysis of the 45 overlapping ASE genes. The x-axis shows the –log10(p-value), and each bubble’s size corresponds to the number of genes associated with that biological process. The color scale indicates the p-value significance level.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7485233/v1/ce8a081744f75a806e4b3002.png"},{"id":91931860,"identity":"9dc2e8ea-f4f4-4535-88ab-ab85078ff0ba","added_by":"auto","created_at":"2025-09-23 02:32:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":999613,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of ESRP1 binding sites and potential target genes using CLIP-seq.\u003cstrong\u003e (A)\u003c/strong\u003e Venn diagram illustrating the overlap of ESRP1-binding peaks identified in two independent immunoprecipitation (IP) replicates (IP_1 and IP_2). A total of 1,169 peaks are specific to IP_1, 604 are specific to IP_2, and 576 peaks are shared by both. \u003cstrong\u003e(B)\u003c/strong\u003e Genomic distribution of ESRP1-binding peaks. The majority of peaks (49.74%) are located in intronic regions, followed by CDS (19.2%), 3′ UTR (13.44%), noncoding exons (10.3%), and other genomic features, indicating that ESRP1 predominantly associates with introns and coding sequences. \u003cstrong\u003e(C)\u003c/strong\u003e GO enrichment analysis of ESRP1-bound target genes. The enriched GO terms primarily include RNA splicing, protein–RNA complex assembly, and mRNA processing events, suggesting that ESRP1 plays a significant role in post-transcriptional gene regulation. \u003cstrong\u003e(D)\u003c/strong\u003eTop four RNA-binding motifs identified in ESRP1-bound peaks. Motif logos and associated statistics (p-value, log p-value, and target coverage) are shown, highlighting consensus sequences that may mediate ESRP1’s splicing regulation. \u003cstrong\u003e(E)\u003c/strong\u003eVenn diagram comparing RASGs (blue circle) with ESRP1-binding targets from IP_1 (green circle) and IP_2 (orange circle). Three genes (DDR1, SEPTIN9, and SPTBN1) overlap among all three sets, indicating direct ESRP1 binding to these RASGs and potential roles in ESRP1-mediated splicing regulation.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7485233/v1/e8344f647c215065e5b85bd4.png"},{"id":91934480,"identity":"c6ea6bb0-c107-49c2-8a5d-9ef4d0622bec","added_by":"auto","created_at":"2025-09-23 02:40:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1523660,"visible":true,"origin":"","legend":"\u003cp\u003eAS analysis of SEPTIN9 and SPTBN1 in different groups.\u003cstrong\u003e (A)\u003c/strong\u003eLeft: Genome browser-style visualization of read coverage and splice junctions for the SEPTIN9 locus in control (control_1, control_2) and DR (DR_1, DR_2) cells. Blue regions indicate normalized read coverage, arcs show junction-spanning reads, and the red boxes highlight the critical splice site where usage differs between control and DR cells. Transcript annotations (ENSEMBL IDs) are shown below. Right: Box plots of the splicing ratio for the same SEPTIN9 event in MCF7 vs. MCF7-DR (left) and in TCGA BRCA samples with high vs. low ESRP1 expression (right). Schematic diagrams above the plots illustrate the differing exon usage resulting from altered 5′ splice site selection. \u003cstrong\u003e(B)\u003c/strong\u003e Similarly, for SPTBN1.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7485233/v1/31b1a1279d73956034d2811b.png"},{"id":91937944,"identity":"aa4c8b63-b302-4971-b68b-1cb52bd3eb76","added_by":"auto","created_at":"2025-09-23 03:04:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8312025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7485233/v1/5af95881-6aa1-4480-825d-18b3660cc2da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ESRP1 deficiency promotes doxorubicin resistance by modulating alternative splicing of SEPTIN9 and SPTBN1-mediated cytoskeleton organization in breast cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBreast cancer is a disease characterized by abnormal growth of breast cells that are out of control and form tumors \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Breast cancer is one of the most common malignant tumors in women \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In 2022, 2.3\u0026nbsp;million women worldwide were diagnosed with breast cancer, and 670,000 died from it \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Chemotherapy resistance is a major obstacle in the treatment of breast cancer, severely affecting the long-term survival rate of patients \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Doxorubicin is a key treatment for breast cancer \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, but its common resistance leads to recurrence and metastasis, poor prognosis and low survival \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The mechanisms of doxorubicin resistance include the overexpression of MDR transporters, DNA damage repair, epithelial-mesenchymal transition (EMT), and changes in the tumor microenvironment \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Thus, exploring the molecular mechanisms of doxorubicin resistance in breast cancer cells is of utmost importance for developing new therapeutic strategies to overcome or prevent drug resistance.\u003c/p\u003e\u003cp\u003eAlternative splicing (AS) is critical for the regulation of human gene expression, significantly contributing to the diversity of functional proteins \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In chemotherapy-resistant tumors, extensive and specific RNA splicing abnormalities can alter tumor cell responses to treatment. For instance, in breast cancer, AS of the Bcl-x gene leads to the production of pro-apoptotic and anti-apoptotic isoforms, influencing the survival of cancer cells in response to chemotherapeutic agents. Additionally, splicing of the CD44 gene generates various isoforms that enhance tumor invasiveness and metastatic potential, further complicating treatment outcomes \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. AS has been shown to play a pivotal role in the prognosis, survival, and drug resistance of breast cancer \u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, with specific mechanisms still requiring further exploration. Splicing factors (SFs) play pivotal roles in the recognition of splice sites and the assembly of spliceosomes during AS \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Studies have highlighted the regulatory role of SFs such as SRSF1 and hnRNPA1 in modulating ASEs associated with doxorubicin resistance, underscoring the importance of understanding these pathways to develop targeted therapies.\u003c/p\u003e\u003cp\u003eEpithelial splicing regulatory protein 1 (ESRP1) is an RNA-binding protein (RBP) and SF that regulates AS of mRNA, which plays an important role in maintaining cell epithelial characteristics and regulating tumor progression \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. ESRP1 mediates aberrant splicing in cancer cells, and plays a role in cell proliferation and chemoresistance through the regulation of apoptosis and autophagy \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In breast cancer, previous study has shown that ERα-ESRP 1/2 axis plays a role in its occurrence and development by controlling the splicing patterns of related genes \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Additional research has demonstrated that ESRP1 can target the splicing of α6 integrin mRNA to increase the sensitivity of triple-negative breast cancer cells to paclitaxel \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These results imply the significant role of ESRP1 in chemoresistance of breast cancer. However, the specific regulatory mechanisms are not clear.\u003c/p\u003e\u003cp\u003eIn this study, we aim to investigate the role of ESRP1 in doxorubicin resistance of breast cancer and to explore the AS network regulated by ESRP1. We explored SFs and ASEs in doxorubicin-resistant MCF7 (MCF7-DR) human breast cancer cells using bioinformatics and expression analysis methods based on previously reported RNA-seq dataset (GSE174152). We demonstrated that the expression of ESRP1 is reduced in doxorubicin-resistant breast cancer cells. The functions of ESRP1 as biomarker have also been verified in The Cancer Genome Atlas (TCGA) database, providing the potential for the prevention and intervention of doxorubicin resistance in breast cancer. Then, we explored the possible binding sites of ESRP1 using CLIP-seq dataset (GSE233931). Further analysis suggests that ESRP1 can regulate the AS of SEPTIN9 and SPTBN1, thereby affecting pathways such as the cytoskeleton organization and small GTPase-mediated signal transduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Our findings highlight the association between ESRP1 and doxorubicin resistance in breast cancer, potentially offering new molecular targets and a theoretical foundation for the treatment of breast cancer.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Access to and processing of public data\u003c/h2\u003e\u003cp\u003eWe used the transcriptome sequencing data (RNA-seq) of parental and doxorubicin-resistant MCF7 (MCF7-DR) human breast cancer cells. The accession numbers of Gene Expression Omnibus (GEO) database were GSE174152. Public sequencing data were obtained from the Sequence Read Archive (SRA). SRA Run data files were transformed into fastq format with NCBI SRA Tool fastq-dump. Raw RNA sequencing data were processed using fastp (v0.23.4) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e to remove sequencing adapters and filter out low-quality bases. Cleaned reads were aligned to the human reference genome (GRCh38) using the STAR aligner (v2.7.10b) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Uniquely mapped reads were retained for further analysis, including read count and expression level calculations, reported as fragments per kilobase of exon per million mapped reads (FPKM).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Splicing analysis using SUVA\u003c/h2\u003e\u003cp\u003eSUVA (splicing site usage variation analysis) focused on different usages of each splice site \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It redefined splicing formula as clustered simple junction pairs to overcome the complex splicing patterns. A pipeline was implemented to identify and quantify the five types of AS events (ASEs) defined by SUVA across whole dataset, and then to screen out statistically significant regulated AS (RAS) events by comparing two comparative samples or two groups of samples with replicates. Splice junction (SJ) reads and non-junction reads from mapping result of STAR was the input of SUVA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Co-expression analysis of splicing ratio of RAS and expression of DE SFs\u003c/h2\u003e\u003cp\u003eThe software DEseq2 \u003csup\u003e26\u003c/sup\u003e, which is specifically used to analyze the differential expression of genes, was applied to screen the raw count data for differentially expressed genes (DEGs). The results were analyzed based on the fold change (FC\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5) and false discovery rate (FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05) to determine whether a gene was differentially expressed. Then expression profile of differentially expressed SFs were filtered out from all DEGs according to a catalogue of 603 SFs of human was retrieved from Gene Ontology (GO) database and previous report \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We constructed a co-expression network to explore correlations between differentially expressed SFs and RAS events. Correlations with |Pearson\u0026rsquo;s r| \u0026ge; 0.99 and p-value\u0026thinsp;\u0026le;\u0026thinsp;0.01 were included in the network. RNA-binding interactions were verified using the RNAinter database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rnainter.org/\u003c/span\u003e\u003cspan address=\"http://www.rnainter.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 GO functional enrichment analysis\u003c/h2\u003e\u003cp\u003eTo identify functional categories of genes, we employed the clusterProfiler package (v4.6.2) \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which enabled us to determine GO terms and KEGG pathways.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 TCGA-breast cancer data analysis\u003c/h2\u003e\u003cp\u003eFor the analysis of ESRP1-regulated RAS in breast cancer, we downloaded clinical and genomic data from TCGA database, specifically targeting the breast cancer cohort (BRCA). We selected samples based on ESRP1 expression levels. Samples were classified into two groups: those with high ESRP1 expression (top 15 samples) and those with low ESRP1 expression (bottom 15 samples). To assess the overlap between the RAS identified from TCGA and ESRP1-targeted RAS detected in the MCF7-DR cell line data, we performed a comparative analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Analysis of CLIP-seq data of ESRP1\u003c/h2\u003e\u003cp\u003eWe used the Crosslinking immunoprecipitation-high-throughput-sequencing data (CLIP-seq) for ESRP1. The accession numbers of GEO database were GSE233931. After reads were aligned onto the genome, only uniquely mapped reads were used for the following analysis. Reads with at least 1 bp overlap were clustered as peaks. For each gene, computational simulation was used to randomly generated reads with the same number and lengths as reads in peaks. The outputting reads were further mapped to the same genes to generate random max peak height from overlapping reads. The whole process was repeated for 500 times. All the observed peaks with heights higher than those of random max peaks (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected. The IP and input samples were analyzed by the simulation independently, and the IP peaks that have overlap with Input peaks were removed. The target genes of IP were finally determined by the peaks and the binding motifs of IP protein were called by HOMER software (v5.1) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. To further explore the functional significance of the identified binding sites, we cross-referenced the ESRP1-targeted genes with the RAS genes identified in the doxorubicin-resistant MCF7 (MCF7-DR) cell line data and TCGA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical Analysis and Data Visualization\u003c/h2\u003e\u003cp\u003eIf not specified, all bioinformatics analyses were performed with R software (v4.3.2) to compute statistics and generate plots throughout this manuscript. Principal component analysis (PCA) was performed using the R package factoextra (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.r-project.org/package=factoextra\u003c/span\u003e\u003cspan address=\"https://cloud.r-project.org/package=factoextra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to display the clustering pattern of samples based on the top two components. The sequencing data and genomic annotations were visualized with a script we developed in-house by normalizing the reads by the tags per million (TPM) of each gene. The pheatmap package in R was used for clustering based on Euclidean distance. For the analysis of overall survival among patients, Kaplan-Meier curves were generated using the \u0026ldquo;survival\u0026rdquo; R package.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Transcriptome analysis identifies differential ASEs in control and doxorubicin-resistant breast cancer cells\u003c/h2\u003e\u003cp\u003eBreast cancer is one of the most common malignancies worldwide. AS produces complex and dynamic variations in protein isoforms that play a certain role in chemoresistance in breast cancer. For in-depth analysis of the changes in ASEs during the development of chemoresistance in breast cancer, we downloaded the transcriptome dataset GSE174152, which includes two doxorubicin-resistant breast cancer cell samples (MCF7-DR) and two parental breast cancer cell samples (MCF7). Five different types of ASE model defined by SUVA according to splicing site usage variation \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. We first compared the differential ASEs between the two groups of cell samples as a whole. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, RAS events identified by SUVA were mainly alt5p and alt3p. The splicing events were corresponding to classical splicing events, in which A5SS and cassetteExon events accounted for a large proportion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eSince a splicing event involves two transcripts, and these two transcripts may account for a very small proportion of the overall gene expression, we aim to find the more dominant spliced transcripts. We filtered the splicing events based on the proportion of all reads in the region (pSAR) that are different for the variable splicing events. Splicing events with pSAR less than 50% were filtered out, and we selected 1,021 events with pSAR\u0026thinsp;\u0026ge;\u0026thinsp;50% for further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The control and doxorubicin-resistant group can be clearly distinguished in the first principal component by using the splicing ratio of RAS events for PCA, indicating a significant increase in the heterogeneity of splicing patterns with the onset of chemotherapy resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). This change may reflect fundamental changes in gene expression regulation mechanisms during the development of chemotherapy resistance, particularly an increase in complexity at the post-transcriptional level.\u003c/p\u003e\u003cp\u003eTo analyze the RAS regulation patterns in the two groups, we used a heatmap to display the distribution of ratios for all differential splicing events (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). GO analysis was applied to elucidate biological processes, which indicated that these differentially spliced genes (RASGs) were significantly enriched in cytoplasmic microtubule organization, regulation of actin filament-based processes, and regulation of actin cytoskeleton organization (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Further, we presented the top 5 enriched pathways and the network diagram of genes included in the RAS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Overall, our findings indicate that a large number of ASEs are associated with doxorubicin resistance in breast cancer. These events can modulate key functional pathways.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Splicing factor ESRP1 regulates doxorubicin resistance-related ASEs in breast cancer\u003c/h2\u003e\u003cp\u003eSFs are a class of RBPs that play pivotal roles in the recognition of splice sites and the assembly of spliceosomes during AS\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Aberrant AS by either dysregulation or mutations of SFs contributes to cancer initiation and progression\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. To thoroughly investigate the regulatory mechanisms of SFs in AS associated with doxorubicin resistance in breast cancer, we analyzed the DEGs and SFs in the MCF7-DR and MCF7 cell samples of the dataset GSE174152. We identified a total of 36 differentially expressed SFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Further, co-expression analysis was performed between SFs and ASEs, selecting SF-RAS pairs with a pearson correlation coefficient greater than 0.99 and a p-value less than 0.01. Subsequently, the RNAinter database was used to identify pairs of SF and RASGs with evidence of binding. Finally, functional enrichment analysis was conducted on these RASGs, and the top 10 pathways along with their corresponding RAS and upstream SF were selected to draw a network diagram. A total of 9 SFs were retained, including the splicing factors ESPR1, RBM23, MSI1, IGF2BP3, CELF2, ESRP2, KHDHBS3, RBM47, and NOVA1. These SF genes can affect the development of doxorubicin resistance in breast cancer by regulating RASEs such as small GTPase-mediated signal transduction and actin filament-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Among them, the expression of RBM23, ESPR1, RBM47, ESRP2, and MSI1 were decreased in MCF7-DR cells, while the expression of CELF2, KHDRBS3, IGF2BP3, and NOVA1 were increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 TCGA data analysis validates ESRP1 cause a wide range of ASEs in breast cancer\u003c/h2\u003e\u003cp\u003eTo further investigate the specific mechanism by which ESRP1 inhibits doxorubicin resistance in breast cancer, we downloaded breast cancer-related data from the TCGA database, and selected 15 samples with the highest and lowest expression of ESPR1 for RAS analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Using the SUVA software, we found that the main types of differential RASEs were alt5p and alt3p (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Then, by screening splicing events with different proportions of ASEs in all reads in the region, events with pSAR less than 50% were screened out. We selected 1,904 splicing events with pSAR\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;50% for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The heat map shows the distribution of RASEs splicing ratio in two groups of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Using the splicing ratio of these events, PCA results showed that ESRP1 high expression and low expression samples could be significantly distinguished (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Furthermore, we performed GO enrichment analysis on these RASGs, which were mainly enriched in biological pathways such as regulation of small GTPase mediated signal transduction and regulation of cell-matrix adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). In addition, we conducted an overlap analysis between the differential RASEs identified from TCGA and the RASEs obtained in ESRP1 co-expression, and selected 45 overlapping RASEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). The biological functions of overlapping ERSP1-modulated genes were analyzed using GO enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). The results showed that these ERSP1-regulated RAS targets were mainly involved in protein localization to cell periphery, regulation of small GTPase mediated signal transduction, actin filament organization, cell projection organization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 CLIP-seq identifies ESRP1 binding sites\u003c/h2\u003e\u003cp\u003eTo identify ESRP1\u0026rsquo;s binding sites, we downloaded the CLIP-seq dataset (GSE233931), which established SGC7901 gastric cancer cell line with stable overexpression of ESRP1 and conducted CLIP seq for ESRP1. A total of 576 binding peaks were detected in both IP experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). ESRP1 mainly bound to introns and CDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, GO analysis was conducted on these ESRP1 binding genes, which were primarily enriched in RNA splicing and protein-RNA complexes assembly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The motif enrichment analysis of peak on ESRP1-binding genes showed the top five genetic sequence. The top motif which ESRP1 bound to was 5\u0026rsquo;-CGUUGCU-3\u0026rsquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). By overlap analysis of ESRP1-binding genes obtained from IP and differentially AS genes detected in the previous section, we identified three genes, DDR1, SEPTIN9 and SPTBN1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). These results suggest that ESRP1 can affect doxorubicin resistance in breast cancer by regulating the AS of DDR1, SEPTIN9, and SPTBN1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.5 ESRP1 regulates the AS of SEPTIN9 and SPTBN1\u003c/h2\u003e\u003cp\u003eTo study the role of SEPTIN9 and SPTBN1 in doxorubicin resistance in breast cancer, we analyzed the changes of AS in MCF7-DR cells. A splicing event on gene SEPTIN9 is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. The reads distribution map showed that SEPTIN9 preferentially selected the proximal 5' splice sites, tending to retain shorter transcripts in the DR group, and the proportion of this splicing event increases in the DR group and the low ESRP1 expression group. A splicing event on another gene SPTBN1 is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, SPTBN1 preferentially selected the distal 5'splicing sites in the DR group, the longer transcripts were more selected in the DR group, and the proportion of this splicing event decreases in the DR group and the low ESRP1 expression group. These suggested that the AS of SEPTIN9 and SPTBN1 are related to doxorubicin resistance in breast cancer. This is consistent with the previous study reported that splice isoform of SEPTIN9 and SPTBN1 is crucial for chemotherapeutic drug resistance in breast cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, we investigated the role of AS in doxorubicin resistance in breast cancer, focusing on the splicing factor ESRP1. Our analysis revealed a significant increase in RAS events in doxorubicin-resistant MCF7 (MCF7-DR) cells compared to parental MCF7 cells. We identified a total of 1,021 differentially spliced events that are potentially linked to chemoresistance. Notably, splicing alterations in key genes, such as SEPTIN9 and SPTBN1, were shown to influence critical pathways related to cytoskeletal organization and cellular signaling. These findings underscore the importance of AS as a mechanism that contributes to the development of drug resistance in breast cancer and highlight ESRP1 as a crucial regulator that may serve as a potential therapeutic target.\u003c/p\u003e\u003cp\u003eOur study revealed that the ASEs identified in MCF7 and MCF7-DR cells were significantly enriched in pathways related to cytoplasmic microtubule organization, regulation of actin filament-based processes, and regulation of actin cytoskeleton organization. These pathways are crucial for maintaining cell shape, motility, and intracellular transport, which are essential for tumor progression and metastasis. The enrichment of these splicing events suggests that alterations in the splicing landscape may enhance the invasive potential of breast cancer cells, thereby contributing to doxorubicin resistance. Previous studies have documented similar associations between cytoskeletal dynamics and chemoresistance \u003csup\u003e\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, indicating that changes in cell morphology and motility can affect drug uptake and efficacy. Our findings expand upon this literature by demonstrating that specific splicing alterations in key regulatory genes can directly influence these critical pathways. The implications of our results suggest potential therapeutic applications, where targeting the splicing machinery, specifically ESRP1 and its downstream effects, may offer a novel strategy to counteract chemoresistance in breast cancer. Future research should focus on elucidating the precise molecular mechanisms by which these splicing alterations affect drug resistance and exploring the potential of splicing modulators as adjunct therapies in the treatment of resistant breast cancer.\u003c/p\u003e\u003cp\u003eIn our investigation of AS in doxorubicin-resistant breast cancer cells, we identified several SFs that may play critical roles in mediating chemoresistance. Among them, CELF2 has shown antitumor activity in a variety of cancer models, including breast cancer, lung cancer, gastric cancer, and ovarian cancer. Loss of CELF2 can promote drug resistance in squamous cell carcinoma (SCC) \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. IGF2BP3 promotes the progression and cisplatin resistance of laryngeal squamous cell carcinoma (LSCC) through the UBA2-PI3K pathway \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. NOVA1 exhibits splicing regulatory activity in natural breast tumors \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. NOVA1 to mediate doxorubicin resistance of hepatocellular carcinoma (HCC) cells by as a sponge for miR-3129-5p \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. KHDRBS3 can alter CD44 isoform expression, enhancing the stemness of basal-like breast cancer cells\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Circ_RBM23 promotes sorafenib resistance, malignant proliferation, migration, and invasion in HCC by regulating the miR-338-3p/RAB1B axis \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. RBM47 inhibits the progression and metastasis of breast cancer by regulating the splicing and stabilization of its target mRNA \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Increasing evidence suggests that MSI1 is associated with resistance to cancer therapy, as its depletion leads to reduced expression of the catalytic subunit of DNA-PK, resulting in increased DNA damage \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Loss of ESRP2 leads to the mesenchymal subtype expression of genes associated with EMT, participating in tumor progression \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Apart from the above SFs, ESRP1 stands out as a particularly significant candidate. ESRP1 is known for its involvement in maintaining epithelial characteristics and regulating splicing patterns of genes implicated in tumor progression \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Existing studies have indicated that ESRP1 is closely related to tumor chemoresistance, and its role in breast cancer chemoresistance remains unclear \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Previous research has highlighted the importance of SFs like SRSF1 and hnRNPA1 in promoting drug resistance by influencing the splicing of key oncogenes and tumor suppressors. However, our study emphasizes ESRP1's unique contribution, as its downregulation in resistant cells correlates with a shift towards splicing patterns that favor invasive phenotypes and enhance cell motility. The choice to focus on ESRP1 was driven by its dual role in regulating AS and maintaining epithelial integrity, both of which are crucial in the context of breast cancer. By demonstrating that ESRP1 modulation can influence splicing events associated with cytoskeletal organization and cellular dynamics, we provide new insights into how SFs can affect drug sensitivity. These findings align with existing literature that links aberrant splicing to cancer progression but extend the discussion by identifying ESRP1 as a key player in the context of chemotherapy resistance. Looking ahead, further research should explore the specific mechanisms by which ESRP1 regulates AS and its impact on the efficacy of chemotherapeutic agents. Additionally, targeting ESRP1 or its downstream splicing events may represent a promising therapeutic strategy to overcome drug resistance in breast cancer. Investigating the potential of splicing modulators in combination with conventional therapies could pave the way for more effective treatment regimens for patients facing resistant breast cancer.\u003c/p\u003e\u003cp\u003eOur integrated analysis utilizing TCGA, cell line data, and CLIP-seq results identified several ASEs that are potentially regulated by ESRP1 and associated with chemotherapy resistance. DDR1 can promote the formation of dense structures around the tumor by organizing collagen fibers, preventing immune cells from entering the tumor, thereby facilitating the tumor's immune evasion and chemotherapeutic resistance \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Septins are a family member of polymeric GTP-binding proteins that constitute a major component of the cytoskeleton \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. SEPTIN9 has multiple isoforms and exhibits different expression patterns and functions in breast cancer cells \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The overexpression of SEPTIN9 isoforms occurs in about 30% of human breast cancer cases and is associated with poor prognosis and resistance to microtubule-targeting anticancer agents \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. SPTBN1 encodes the β II subunit of spectrin (a cytoskeletal protein) to maintain cell morphology and is involved in the regulation of DNA damage repair, angiogenesis, and stemness maintenance\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. It has also been reported that SPTBN1 plays a significant role in the pathogenesis, progression, and chemoresistance of many types of cancer \u003csup\u003e\u003cspan additionalcitationids=\"CR59 CR60 CR61\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. NUMA1 is a cell cycle protein that is upregulated in breast cancer and is important for maintaining the characteristics of breast cancer stem cells (BCSCs) \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. SPTAN1 is an important cytoskeletal protein and signaling molecule that can positively and negatively impact cancer progression depending on its localization and regulation \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. LRP8 has been identified as a novel positive regulator in triple negative breast cancer (TNBC) and can facilitate the formation of dense structures around the tumor by organizing collagen fibers, preventing immune cells from entering the tumor, thereby promoting tumor immune evasion and chemotherapeutic resistance \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNotably, genes such as SEPTIN9 and SPTBN1 emerged as key candidates, demonstrating significant changes in splicing patterns across doxorubicin-resistant cell lines and patient samples. SEPTIN9 is involved in cytoskeletal dynamics, which plays a critical role in cell migration and invasion\u0026mdash;processes known to contribute to drug resistance in breast cancer. Similarly, SPTBN1 is essential for maintaining cell shape and facilitating intracellular signaling, further linking its splicing variants to cellular responses to chemotherapeutic agents. These findings underscore the pivotal role of ESRP1 in modulating splicing events that influence key biological pathways related to drug resistance. By highlighting the interplay between splicing regulation and chemotherapy response, our study contributes to the growing body of literature connecting aberrant splicing with cancer progression and treatment failure. Future research should aim to elucidate the precise molecular mechanisms through which ESRP1 influences these splicing events and how they, in turn, affect drug sensitivity. Additionally, exploring the therapeutic potential of targeting ESRP1 or its downstream splicing targets could lead to novel strategies for overcoming chemoresistance in breast cancer. Investigating the efficacy of splicing modulators in conjunction with traditional chemotherapy may enhance treatment outcomes for patients facing drug-resistant tumors, ultimately advancing the field of personalized cancer therapy.\u003c/p\u003e\u003cp\u003eIn conclusion, our study highlights the critical role of AS in mediating doxorubicin resistance in breast cancer, with a specific focus on the splicing factor ESRP1. We identified key ASEs regulated by ESRP1 that are associated with pathways relevant to tumor progression and drug resistance, such as cytoskeletal organization. These findings not only enhance our understanding of the molecular mechanisms underlying chemoresistance but also suggest potential therapeutic targets for improving treatment outcomes in breast cancer. However, this study is not without limitations. The reliance on cell line models, while valuable for initial insights, may not fully capture the complexity of tumor biology present in actual patient samples. Furthermore, the functional significance of the identified splicing events and their direct contributions to drug resistance need to be validated through in vivo studies. Future research should aim to explore these mechanisms in more diverse and clinically relevant models, as well as investigate the potential of splicing modulators as therapeutic strategies to overcome chemoresistance in breast cancer. By addressing these limitations, we can further elucidate the therapeutic implications of our findings and advance the field of precision oncology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception:\u0026nbsp;JW and LC.\u003c/p\u003e\n\u003cp\u003eInterpretation or analysis of data: WL and JZ.\u003c/p\u003e\n\u003cp\u003ePreparation of the manuscript: WL, JZ and LC.\u003c/p\u003e\n\u003cp\u003eRevision for important intellectual content: WL, JZ and JW.\u003c/p\u003e\n\u003cp\u003eSupervision: LC.\u003c/p\u003e\n\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConflict of Interests Statement\u003c/h2\u003e\n\u003cp\u003eThe Authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, private or not-for-profit commercial sectors.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available in Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/. These data were derived from the following resources available in the public domain: GSE174152, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174152. GSE233931, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE233931.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKatsura C, Ogunmwonyi I, Kankam HKN, Saha S. Breast cancer: presentation, investigation and management. Br J Hosp Med. 2022;83(2):1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12968/hmed.2021.0459\u003c/span\u003e\u003cspan address=\"10.12968/hmed.2021.0459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. 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Cancer Lett. 2018;438:165\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ESRP1, alternative splicing, doxorubicin resistance, SEPTIN9, SPTBN1, breast cancer","lastPublishedDoi":"10.21203/rs.3.rs-7485233/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7485233/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBreast cancer remains one of the most prevalent malignancies among women, with doxorubicin resistance posing a significant challenge that undermines treatment success and survival outcomes. Aberrant alternative splicing (AS), driven by dysregulation or mutations in splicing factors (SFs), is implicated in cancer initiation, progression, and drug resistance. This study aims to investigate the role of the epithelial cell-specific splicing factor ESRP1 in regulating doxorubicin resistance in breast cancer, focusing on how ESRP1 deficiency contributes to AS changes that promote chemoresistance.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed RNA-sequencing (RNA-seq) data from doxorubicin-resistant (MCF7-DR) and parental (MCF7) breast cancer cell lines to identify enhanced AS events (ASEs) and changes in ESRP1 expression. An integrative analysis combining crosslinking immunoprecipitation (CLIP-seq) data and The Cancer Genome Atlas (TCGA) database was performed to validate ESRP1 binding targets and assess the impact of ESRP1-mediated splicing on cytoskeleton organization and small GTPase-mediated signaling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe observed extensive AS changes and downregulated ESRP1 expression in doxorubicin-resistant cells. Integrative analysis revealed that ESRP1 directly regulates the splicing of SEPTIN9 and SPTBN1, two genes involved in cytoskeletal remodeling and small GTPase-mediated signaling. ESRP1 deficiency was associated with increased doxorubicin resistance, in part by driving critical ASEs linked to cytoskeletal organization.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur findings suggest that ESRP1 plays a crucial role in modulating doxorubicin resistance through its regulation of ASEs in breast cancer cells. Targeting the ESRP1-dependent splicing network may offer new strategies to overcome chemoresistance and improve patient outcomes.\u003c/p\u003e","manuscriptTitle":"ESRP1 deficiency promotes doxorubicin resistance by modulating alternative splicing of SEPTIN9 and SPTBN1-mediated cytoskeleton organization in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:32:18","doi":"10.21203/rs.3.rs-7485233/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-09T18:51:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T20:47:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77098097849145396843293172213823419437","date":"2025-10-05T12:15:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T12:49:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29326431531330619793555604602429532862","date":"2025-09-28T06:55:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319864666755426147988210707823544908212","date":"2025-09-27T17:13:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47502594589739231922604855456829554351","date":"2025-09-24T15:18:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194228858267098232821266408186820971038","date":"2025-09-24T13:52:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T14:15:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169610837515544809110441441934566619414","date":"2025-09-12T13:34:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T13:18:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-11T10:14:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-29T11:53:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-29T11:52:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-08-29T05:49:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53c5afd1-7ac8-44ff-b1bc-0b09e165b56f","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T09:54:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 02:32:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7485233","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7485233","identity":"rs-7485233","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}