Comprehensive analysis reveals immunosuppressive part of SSRP1 in pan-cancer and its potential funtion in pancreatic cancer

Comprehensive analysis reveals immunosuppressive part of SSRP1 in pan-cancer and its potential funtion in pancreatic 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 Comprehensive analysis reveals immunosuppressive part of SSRP1 in pan-cancer and its potential funtion in pancreatic cancer Chuanbao Li, Hailiang Wang, Junlu Wang, Haipeng Hou, Qi Gong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6927777/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Research increasingly showed a correlation between Structure Specific Recognition Protein 1 (SSRP1) and the progression of diverse cancers. Nonetheless, the influences of SSRP1 on pan-cancer remains inadequately understood. Methods Differential expression of SSRP1 at mRNA levels was systematically assessed across 33 cancer types utilizing TCGA, GTEx and GEO datasets. Analysis of SSRP1 protein expression levels was conducted through the UALCAN tool. Extensive bioinformatics analyses on 33 cancer types, encompassing tumor mutational burden, prognostic, methylation, and immune microenvironment analyses, we employed Sangerbox 3.0, PanCanSurvPlot ,TISIDB, TIDE, TISCH2 and CancerSEA platform for comprehensive analysis. To elucidate the spatial distribution and functional relevance of SSRP1, we first performed spatial transcriptomic analysis by querying CROST and SpatialDB. Subsequently, siRNA-mediated knockdown of SSRP1 was conducted in pancreatic cancer cell lines followed by a panel of functional assays to assess its role in tumorigenesis. Results It was observed that SSRP1 expression increased in the majority of tumors acting an essential role in prognosis and diagnosis. There was a strong correlation between terrible clinical outcomes and SSRP1 expression in ACC, LAML, MESO, PAAD and SARC. The association between SSRP1 and tumor heterogeneity, stemness, DNA methyltransferases and Homologous Recombination Repair (HRR) genes were examined. We investigated the relationship between SSRP1 and immune infiltration as well as immunotherapy in pan-cancer. In the analysis of immune microenvironment, SSRP1 was positively correlated with immune supression and Patients exhibiting elevated expression levels of SSRP1 had poor response to immunotherapy. In vitro, the knockdown of SSRP1 inhibited the proliferation, migration and invasion of pancreatic cancer was firstly detected in this study. Conclusion In summary, our research offers a comprehensive analysis of SSRP1's functional mechanisms across various cancers and confirms its role in pancreatic cancer, underscoring its Prognostic and therapeutic potential. Pan-cancer TCGA SSRP1 Tumor microenvironment Immunoinhibition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Pancreatic cancer is one of the deadliest tumors with a new incidence and mortality rate of 3% and 8% in the United States in 2024[ 1 ]. Multi-omics research has been regarded as an important research tool to study tumor progression as well as to find effective therapeutic targets[ 2 , 3 ]. SSRP1, also known as FACT80, is a subunit of the Facilitates Chromatin Transcription (FACT) complex that aids in chromatin elongation and transcription of target genes[ 4 , 5 ]. Some researchers have claimed that SSRP1 removes H2Bub and represses transcription of MERVL and MERVL fusion genes by recruiting Usp7[ 6 ]. SSRP1 can facilitate the replication initiation assembly on the nucleus of somatic cells in African Xenopus laevis egg extracts by promoting the expulsion of histone H1 from somatic chromatin, and embryos at higher SSRP1 protein levels develop significantly faster[ 7 ].0020An analysis of single-cell transcriptomes was conducted on tumor and paired distal liver tissue samples from five patients with hepatoblastoma. As a potential epigenetically targeted therapeutic strategy, FACT inhibits the oncogenic feedback loop between MYC and SSRP1 in probable hepatoblastomas[ 8 ]. Ying et al.found that SSRP1 recruits to DNA single-strand breaks (SSB) in a PARP-dependent manner and promotes DNA damage repair by interacting with the N-terminus of the DNA repair protein XRCC1 retained at the DNA damage site[ 9 ]. As a result of mutual transcriptional regulation and activation, SSRP1 and EWSR1-FLI1 promote cell cycle/DNA replication and the IGF1R-PI3K-AKT-mTOR pathway in Ewing sarcoma, thereby driving tumorigenesis[ 10 ]. SSRP1 has been identified as an oncogene that promotes the progression of hepatocellular carcinoma, nodal carcinoma, and nasopharyngeal carcinoma[ 11 – 13 ]. However, to date, there has not been a comprehensive pan-cancer study for SSRP1.We sought to investigate the diagnostic and immunological functions of SSRP1 across various cancers, with a particular focus on its role in pancreatic cancer. A thorough pan-cancer investigation of SSRP1 was carried out utilizing a variety of databases and tools to identify differences in expression, clinical characteristics, genomic heterogeneity, and response to immunotherapy in different tumors and normal tissues. Notably, our study revealed that SSRP1 has important roles in DNA repair, T cell depletion, and immunotherapy.We investigated SSRP1 expression differences between normal pancreatic tissue and pancreatic cancer utilizing the GEO database.Our experiments with pancreatic cancer cell lines revealed that SSRP1 enhances the malignant phenotype progression, indicating its potential as a novel diagnostic marker for pancreatic cancer. Materials and Methods SSRP1 expression data acquisition UCSC XENA ( https://xenabrowser.net/datapages/ ) was employed to attain TPM-formatted RNA sequencing data for Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) processed by Toil Process Harmonization. Normal tissue data of GTEx and TCGA were extracted and uniformly processed as log2(TPM + 1)[ 14 ]. SSRP1 protein data for various cancers and normal tissues, The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) was used to determine differential expression[ 15 ] ( https://ualcan.path.uab.edu/analysis-prot.html ). The Cancer Cell Line Encyclopedia (CCLE) provides information on SSRP1 expression in cancer cell lines[ 16 ] ( https://sites.broadinstitute.org/ccle/ ). SSRP1 expression in pancreatic cancer and normal tissues was obtained from Gene Expression Omnibus (GEO) data (GSE16515, GSE15471, GSE62452, GSE28735), which were normalized and homogenized, and visualized with the ggplot2 package. Differential expression of SSRP1 in pan-cancer in different clinical subgroups and ROC diagnostic curves Among the extracted TCGA data, data without corresponding clinical information were discarded and TNM staging differential expression box plots were visualized using The R 'ggplot2' package. TISIDB ( http://cis.hku.hk/TISIDB/ )[ 17 ] was used to analyze SSRP1 expression across various stages and grades. We generated receiver operating characteristic curves (ROC) from a combined cohort derived from TCGAand GTEx datasets in order to assess the diagnostic utility of SSRP1 across various cancer types. ROC analysis was conducted with the pROC package, and the results were visualized via ggplot2. Survival curves and prognostic value Gene Expression protiling lnteractive Anaysis 2 (GEPIA2) ( http://gepia2.cancer-pku.cn/ )[ 18 ] was utilized to obtain pan-cancer survival heatmaps as well as survival curves. Mean, the K-M survival curves of SSRP1 in different clinical subgroups of pancreatic cancer were also obtained[ 19 ]. In addition, the survival prognosis data for the GEO dataset was retrieved from the PanCanSurvPlot online platform ( http://zjyy-oncology.asuscomm.cn:20008/ ). Genomic Alterations, Mutation Load and Microsatellite Instability and Gene Correlation The cBioPortal database was used to SSRP1 mutations and prognosis in pan-cancer genomes ( https://www.cbioportal.org/ )[ 20 ]. Information about Microsatellite instability(MSI) as well as Tumor mutation burden(TMB) were obtained from TCGA and visualized using the ( https://www.home-for-researchers.com/ ). Meanwhile, UALCAN was utilized to explore the correlation between pan-cancer somatic mutations in key pathways and promoter methylation and SSRP1 expression in pan-cancer[ 15 ]. TIMER2.0 ( http://timer.cistrome.org/ ) was analyzed differential expression of SSRP1 in TP53 mutant tumors and its correlation with DNA methylation, Homologous Recombination Repair (HRR), and RNA splicing-related genes[ 21 ]. SSRP1 specific structure and DNA methylation We explored the relationship between SSRP1 expression and DNA methylation levels in probe cg01250938 in Epigenome-Wide Association Study (EWAS) ( https://ngdc.cncb.ac.cn/ewas/atlas ) database. In various cancers and tissues, we investigated the relationship between SSRP1 DNA methylation levels and clinical prognosis[ 22 ]. The SSRP1 gene sequences and DNA methylation of normal and cancer patients were analyzed using Shiny Methylation Analysis Resource Tool (SMART) ( http://www.bioinfo-zs.com/artapp/ )[ 23 ]. Genomic Heterogeneity, Associated Pathways, and Pharmacological Enrichment Sangerbox 3.0 ( http://vip.sangerbox.com/ ) was tasked with investigating the correlation among SSRP1 expression and various factors including HRD, Purity, MEO, Ploidy, LOH, MATH, and RNA modifications in pan-cancer[ 24 ]. We also investigated the impact of RNA splicing event occurrence in SSRP1 in OncoSplicing ( http://www.oncosplicing.com/ ) on clinical prognosis and also explored the correlation between SSRP1 and RNA splicing genes in pan-cancer[ 25 ]. Gene Set Cancer Analysis (GSCA)( https://guolab.wchscu.cn/GSCA/ ) was engaged in relating SSRP1 to drugability[ 26 ]. We utilized the GSVA package in R to extract genes from RNAseq data obtained from TCGA, employing the 'ssgsea' method. Pathway scores were subsequently correlated using Spearman correlation[ 27 ]. The relationship between SSRP1 and cancer-related pathways such as stemness, cycling, and DNA repair in pan-cancer was explored using the Cancer Single-cell State Atlas (CancerSEA) database ( http://biocc.hrbmu.edu.cn/CancerSEA/ )[ 28 ]. Immunological role about SSRP1 in pan-cancer as well as single-cell Initially, the TISDIB tool was employed to analyze SSRP1 expression variations across different molecular and immune subtypes, as well as its association with immune-related molecules, including C1:Wound healing; C2:IFN-γ dominant; C3:Inflammatory; C4: Lymphocyte-depleted; C5:Immunologically quiet; C6:TGF-βdominant. Subsequently. Relationship between immune cells and SSRP1 expression in pan-cancer was explored using TIMER 2.0.Correlation between SSRP1 and immune matrix data of pan-cancer data was performed, and matrix and immune scores for the corresponding data were calculated by R package-estimate[ 29 ]. Utilizing the correlation module in GEPIA2 in order to investigate the connection between SSRP1 expression and the Exhausted T-Cell signature. Tumor Immune Single-cell Hub 2 (TISCH2) ( http://tisch.comp-genomics.org/home/ ) was utilized toward investigate expression about SSRP1 during various immune cells in pan-carcinoma[ 30 ]. Immunotherapy Prediction and Spatial Transcriptome RNAseq data and clinical information from TCGA were analyzed using the TIDE algorithm to predict potential immunotherapy response[ 31 ]. The Tumor Immune Dysfunction and Exclusion(TIDE)( http://tide.dfci.harvard.edu/ ) database, utilizing published transcriptomic biomarkers, predicts patient responses to therapy and overall survival. This study employed TIDE to compare custom biomarkers with existing ones, investigating the correlation between SSRP1 and immunotherapy responsiveness. A database named Tumor Immune Syngeneic Mouse (TISMO) ( http://tio.cistrome.org/ ) which is for visualizing and accessing bulk data of syngeneic mouse models. SSRP1 expression levels were visualized across different ICB treatments, including anti-PDL1,anti-PD1, anti-CTLA4, anti-PDL2, and anti-PDL2, comparing pre- and post-treatment stages as well as responders and non-responders[ 32 ]. The comprehensive repository of spatial transcriptomics (CROST) ( https://ngdc.cncb.ac.cn/crost/home ) is a powerful research tool. It can be used for single-sample, interactive visualization, and exploration of cancer svg for spatial transcriptomics with multi-omics integration. CROST was used to obtain spatial transcriptomics of SSRP1 expression in different cancers[ 33 ]. In addition, we utilized the SpatialDB ( http://spatialomics.org/SpatialDB/ ) database to investigate the co-expression localization of SSRP1 and T-cell depletion-related molecules as well as DMSC biomarkers[ 34 ]. Cell culture HPDE cell line and Human pancreatic cancer cell lines (Capan-2, SW1990, BxPC-3, MIAPaCa-2, PANC-1, AsPC-1 and HS766T) were acquired from Suzhou Starfish Biotechnology Co.Ltd (Suzhou,China). The cells were cultured in Dulbecco's Modified Eagle Medium (DMEM, Zeta) or Roswell Park Memorial Institute (RPMI 1640, Zeta) which are supplemented with1% penicillin/streptomycin (Invitrogen, USA) and 10% fetal bovine serum within a humidified incubator maintained at 5% CO2 and 37°C. All cell lines were tested for mycoplasma and identified by short tandem repeat (STR) analysis. Cell transfection Cells were seeded one day prior to transfection to attain a cell density ranging from 30–50%. Transfection was performed using Lipofectamine 3000 (Invitrogen, USA) on cells cultured in 6-well plates. The small interfering RNA (siRNA) targeting SSRP1 and the negative control siRNA were procured from Ruibo (Guangzhou, China). Specific sequences were derived from[ 35 ]. All siRNA sequences used will be found in Supplementary Table 1. Subsequent assays were conducted 48 hours post-transfection. Western blotting Proteins were extracted from cells using phosphatase and protease inhibitors (Keygen, Nanjing, China) in RIPA buffer. In the aftermath of protein concentration by the BCA assay, proteins were separated by SDS-PAGE and transferred to nitrocellulose. Primary SSRP1 antibody (1:1000)(ER1901-13, HUABIO, China) and anti-beta Actin antibody (1:10000)(R1207-1, HUABIO, China) were incubated overnight. The next day, the bands were washed with PBST (3 times, 5 minutes each), followed by another wash after 1 hour of incubation with secondary antibody at room temperature. This bands were observed by chemiluminescence. Control groud was beta Actin Cell proliferation assay Assays of CCK-8 were conducted by seeding 1000 cells into 96-well plates and maintaining them for durations of 1, 2, 3, or 4 days. Subsequently, the medium was refreshed with 100 µL, and 10 µL of CCK-8 reagent (Sigma) was added. The cells were then incubated for 2 hours to facilitate OD 450 measurements. Following this, EdU was introduced into the medium at a concentration of 10 µM and incubated for an additional 2 hours. The cells were subsequently fixed and permeabilized. The subsequent reactions were performed according to the manufacturer's protocol (APExBIO, Cat.No.K1076). Fluorescence microscopy was employed to capture the images (ThermoFishr Scientific). For the colony formation assays, A total of 1,000 cells were inoculated and maintained in 6-well plates for a duration of 14 days. Subsequently, the cells were rinsed with PBS and fixed in 4% paraformaldehyde for 30 minutes. Ensuringly, the cells were treated with 0.5% crystal violet for 1 hour at ambient temperature and the colonies were quantified using ImageJ software. Wound healing experiment Following trypsin digestion, 2 x 10⁶ cells per well were seeded into 6-well plates. After an overnight incubation, a sterile pipette tip was employed to generate a scratch. The initial gap width was photographed immediately after the scratch and the residual gap width was recorded at 0 and 24 hours post-scratching. Cell migration and invasion assay Cell migration was assessed by seeding 200 µL of pancreatic cancer cell suspension in serum-free DMEM into the upper chamber, while the lower chamber was filled with 10% fetal bovine serum (CORNING, China). After a 24-hour incubation period, the cells in the chamber were fixed in 4% paraformaldehyde for 30 minutes and subsequently stained with 0.1% crystal violet. We then used an inverted light microscope to observe cells that had migrated or invaded the lower chamber, selecting three random fields. For the invasion assay, Matrigel (BD Biosciences, Franklin Lakes, NJ, USA) was evenly applied to Transwell chambers (Cat#: 3422, Corning Inc., Corning, NY, USA) and the procedure was carried out as described above. Statistical analysis Statistical analyses were performed using R Studio and GraphPad 9.0 software. The Wilcoxon test was applied for comparisons between two groups while one-way ANOVA or Kruskal-Wallis was used for analyzing three or more groups. P value of less than 0.05 was established as the threshold for statistical significance. Results The expression levels of SSRP1 across multiple cancer types Utilizing GTEx and TCGA data, We performed a comprehensive pan-cancer analysis of SSRP1 mRNA expression, identifying significant differences across 26 cancer types and a prevalent upregulation of SSRP1 in the majority of these cancers (Fig. 1 A). In our investigation of the TCGA pan-cancer dataset, we focused on evaluating the mRNA expression levels of SSRP1 in cancer tissues compared to matched normal samples, discovered that SSRP1 expression was only downregulated in KICH while upregulated in all other cancer types( Figure S1 A ). Using the UALCAN tool, we assessed SSRP1 protein expression across various cancer types, revealing significant upregulation in pan-cancer (Fig. 1 B). However, no statistically significant difference was observed for UCEC. We further probed the expression of SSRP1 in cell lines derived from pancreatic cancer and other pan-cancer cell lines using CCLE data (Fig. 1 C,D). By employing TISDIB, we analyzed the expression differences in SSRP1 across various tumor stages and grades, noting a negative correlation with tumor stage in SKCM( Figure S1 B, Figure S2 A ). In contrast, positive correlations were present in other cancers in relation to both tumor stage and grade( Figure S1 C ). Additionally, data confirmed a strong positive correlation of SSRP1 protein expression with tumor stage, alongside variations in expression linked to TNM staging and molecular subtypes in different tumor types( Figure S2 B,C ). Diagnostic value and K-M survival curves of SSRP1 in pan-cancer The GEPIA2 online tool analyzed the SSRP1 survival heatmap across pan-cancer.Univariate Cox regression revealed that SSRP1 levels were significantly elevated in ACC, LAML, LIHC, LUAD, MESO, PAAD, and SARC.High SSRP1 expression correlated with poor Overall Survival (OS), except in KIRC and UCS where it was linked to better OS (Fig. 1 E, Figure S3 A ). In ACC, BLCA, MESO, PAAD, and LIHC, the high SSRP1 expression group was positively correlated with poor disease-free Survival(DFS) (Fig. 1 F). The ROC curve analysis across various cancers suggests that SSRP1 is a potential biomarker for predicting cancer progression ( Figure S3 B ). Using the PanCanSurvPlot tool to identify SSRP1 expression groups in the GEO dataset revealed that high SSRP1 expression correlated with poor clinical prognosis in pan-cancer ( Figure S4 ). Genomic Alterations and Genomic Heterogeneity From a genomic perspective, investigating the impact of gene mutations on pan-cancer is critically important[ 36 ]. We utilized cBioPortal to examine SSRP1 mutation frequency, focusing on CNV and SNV alterations across various cancer types. Notably, SSRP1 amplification was primarily detected in UCS and MESO, while deep deletions were frequently observed in MESO.Single nucleotide variations predominantly occurred in UCEC, BLCA, UCS, SKCM, and CHOL (Fig. 2 A, C). Kaplan-Meier survival curves indicate that SSRP1 genomic alterations are linked to better patient prognoses (Fig. 2 b). Additionally, we observed a positive correlation between SSRP1 expression and chromatin state changes in BRCA, LUAD, PAAD, and HNSC(Fig. 2 D). Homologous recombination deficiency (HRD) can induce quantifiable, specific and stable genomic alterations, positioning HRD status as a pivotal factor in cancer treatment strategies and prognosis. A close association exists between HRD and the resistance to PARP inhibitors in advanced breast cancer [ 37 ]. Our analysis showed that SSRP1 expression negatively correlates with HRD in THYM while positively correlates with ACC ( Figure S5 A ). Furthermore, polyploidy represents a hallmark of cancer, where estimating tumor purity and ploidy enhances our understanding of cancer genomic evolution and intratumoral heterogeneity. Total mRNA expression from tumor cells across 15 cancer types has been shown to predict tumor progression[ 38 ]. SSRP1 showed a positive correlation with tumor purity in READ and a unfavorable correlation with ploidy in UVM ( Figure S5 B,D ). We evaluated the correlation between SSRP1 expression and LOH/mutational burden (MATH) in our study of SNV deletions ( Figure S5 E,F ). Based on prior research on neoantigen data across various tumor types [ 39 ], we found a favorable correlation between SSRP1 and CHOL, and an opposing relation with COAD ( Figure S5 C ). The TP53 gene, a critical tumor suppressor, plays an essential role in regulating other genes, and its mutations may have significant implications for gene expression [ 40 ]. In our study, we employed TIMER2.0 to analyze the expression of SSRP1 within the TP53 mutation cohort. The violin plot indicated significantly higher SSRP1 expression in this group compared to the control group across multiple cancer types, including BRCA, LUAD, ACC, KICH, LIHC and PAAD (Fig. 2 E). Correlation scatter plots from the EWAS database show that SSRP1 expression is typically inversely related to DNA methylation levels in most tumors.However, a positive correlation is notable in testicular cancer (Fig. 2 F, Figure S6 A ). The expression levels of SSRP1 also vary across different tissue types ( Figure S6 B ). We further utilized this analytical approach to investigate specific segments of the SSRP1 gene, incorporating both normal and cancer patient data regarding DNA methylation levels ( Figure S6 D,E ). The UALCAN tool was used to examine SSRP1 promoter methylation across various cancers, revealing elevated levels in KIRC, KIRP, and THCA ( Figure S6 C ). Notably, only in the case of LAML did patients with low SSRP1 expression exhibit improved prognoses, whereas, in other cancers, higher levels of SSRP1 methylation correlated with better clinical outcomes (Fig. 2 G). Collectively, these analyses underscore a significant link between SSRP1, genomic instability, DNA methylation, and clinical prognostic outcomes. SSRP1 is associated with RNA splicing, RNA modification, and multiple oncogenic pathways We carried out a Spearman relationship analysis to evaluate the association between SSRP1 and critical pathways in tumor proliferation, DNA replication, and repair. Our analysis indicated a strong positive correlation between SSRP1 and these cancer progression pathways, highlighting the potential role of SSRP1 in facilitating tumor advancement through these mechanisms (Fig. 4 B). The CancerSEA single-cell database corroborated a strong link between SSRP1 and pathways related to the cell cycle, DNA repair, and tumor stemness (Fig. 4 F). We also identified a positive relationship between SSRP1 expression across various cancer types and genes related to the Homologous Recombination Repair (HRR) pathways (Fig. 4 C). Furthermore, research has underscored the immunological relevance of RNA splicing in Treg cells, noting that RNA splicing contributes to immune regulation in tumor progression [ 41 ]. Utilizing the Oncosplicing tool, we found that SSRP1 RNA splicing events are consistently linked with more favorable clinical outcomes in LGG and LUSC, whereas an inverse association was found in BLCA, LIHC, SARC, and OV (Fig. 3 A). Additional findings revealed that SSRP1 is positively correlated with genes involved in RNA splicing across multiple cancer types (Fig. 3 B). It is acknowledged that epigenetic factors can influence tumor progression through immune pathways [ 42 ], resultantly, we analyzed the link between SSRP1 along with genes associated with RNA modification processes, uncovering a significant positive association across pan-cancer data (Fig. 3 C). DNA methylation is crucial, and in CHOL, SSRP1 and DNMT3L expression are negatively correlated, unlike the positive correlations observed in other cancers (Fig. 3 D). Alterations in signaling pathways like mTOR, WNT, NRF2, and P53/Rb may cause variations in SSRP1 protein expression, indicating SSRP1's potential role in these pathways (Fig. 4 D). Interaction networks involving SSRP1 and other molecules were further analyzed using the GeneMANIA tool (Fig. 3 G). In our investigation of SSRP1 and potential drug sensitivities, we discovered a negative correlation between SSRP1 expression and sensitivities to GSK1070916 and PIK-93 (Fig. 4 E). Collectively, these results suggest that SSRP1 may exert a significant influence on tumor progression through various cancer pathways. SSRP1 is bound to Immune suppression The Estimate algorithm was applied to conduct a relation analysis amid single-gene expression and immune infiltration matrix data within muti-cancer studies. The expression of SSRP1 is adversely correlated with Immune Score, Stromal Score, and Estimate Score. In KIRC, SSRP1 showed a positive correlation with the Stromal Score (Fig. 4 A). We then evaluated the connection between SSRP1 and both TMB and MSI. Significant positive correlations were identified between SSRP1 and TMB in both ACC and STAD and an optimistic interrelation with MSI in TGCT (Fig. 3 E,F). The GEPIA2 tool was also used to analyze SSRP1 expression about the T cell exhaustion signature across different cancer types.Correlation scatter plots revealed a positive association in ACC and KICH, contrasting with a negative association noted in OV and PAAD (Fig. 5 C). The relationship between SSRP1 and CD8 + T cell infiltration as well as myeloid-derived suppressor cells (MDSC) was investigated using the TIMER2.0 database. Although no significant correspondence was observed between SSRP1 and CD8 + T cell infiltration across pan-cancer, a notable positive correlation was found between SSRP1 and MDSC infiltration. This suggests that SSRP1 may inhibit the immune microenvironment via MDSC infiltration (Fig. 5 D,E). Furthermore, the relationships between SSRP1 and various other immune cell infiltrations were examined ( Figure S9 ). The TISDIB database confirmed that SSRP1 expression differs among immune subtypes, with significant downregulation in the C3 (Inflammatory) subtype, suggesting a potential functional association with inflammatory signaling pathways ( Figure S8 A ). We also examined the link among SSRP1 and various immune molecules, such as chemokines, receptors, immunostimulators, and MHC molecules( Figure S10 ). High expression of SSRP1 suppresses immunotherapy response As an increasing number of patients receive combined immune checkpoint blockade (ICB) therapies, the importance of comprehensive biomarker research has become evident, revealing potential new avenues for investigation [ 43 ].The complex immune microenvironment leads to effective immunotherapy responses in only a subset of patients [ 44 ]. Therefore, it is vital to search for novel biomarkers that can enhance immunotherapy in current clinical settings. We exploited the TIDE database to examine the relationship between SSRP1 expression and responses to ICB therapy. In cohorts from Zhao2019_PD1, Gide2019_PD1, Lauss2017_ACT, and Braun2020_PD1, patients with low SSRP1 expression exhibited enhanced OS and RFS following ICB treatment (Fig. 5 A). This finding indicated SSRP1 may play a regulatory role within the immune microenvironment, as the extent of cytotoxic T lymphocyte (CTL) infiltration is critical for the response toward immunotherapy [ 45 ]. We scrutinized the interconnection between SSRP1 expression and CTL levels in ICB cohorts, discovering a positive correlation in the GSE17536 cohort, whereas other cohorts showed negative correlations (Fig. 5 B, Figure S7 B ). In the ICB cohorts, high SSRP1 expression was linked to decreased CTL infiltration levels across AML, BRCA, COADREAD, HNSC, LUAD, and OV, potentially compromising patient survival and adversely affecting immune treatment responses. Conversely, this pattern did not hold in patients with low SSRP1 expression (Fig. 5 F). Using the TIDE algorithm to predict potential immune treatment responses, we found that only BRCA patients coupled with low SSRP1 expression possessed higher TIDE scores in comparison with those with high expression. In contrast, other tumor types consistently showed higher scores in the high expression group ( Figure S8 B ). This suggests that targeting SSRP1 may improve responses to ICB therapy. SSRP1 demonstrated superior predictive ability for ICB treatment outcomes compared to other standardized biomarkers, consistently achieving an AUC greater than 0.5 across nine cohorts, with notable performance in the HNSC (Uppaluri2020_PD1) and Melanoma (Riaz2017_PD1) cohorts ( Figure S7 A ). We utilized the TISMO tool to evaluate SSRP1 expression in cytokine-treated cancer cell lines ( Figure S11 A ) and with samples before and after anti-PD-1 coupled with anti-CTLA4 treatments ( Figure S11 B ). The results indicated significantly lower SSRP1 expression levels in responding samples following cytokine, anti-CTLA4, and anti-PD-1 therapy. Thus, we conclude that high SSRP1 expression may lead to resistance in patients undergoing immunotherapy. Single cell and spatial transcriptome analysis of SSRP1 localization To determine whether SSRP1 expression is associated with cancer-associated immune responses, we began by evaluating the spatial positioning of SSRP1 in a pan-cancer context using the CROST database (Fig. 6 A). We obtained spatial transcriptomic images from SpatialDB, revealing a substantial spatial connection between SSRP1 expression and the T cell exhaustion marker CD44 in addition to the MDSC marker CD14 (Fig. 6 B). To further substantiate the involvement of SSRP1 in the immune microenvironment, we employed TISCH2 to evaluate the enrichment of SSRP1 in particular immune cell populations. Our analysis revealed that SSRP1 was primarily enriched in CD8 + T cells, exhausted CD8 + T cells, CD4 + T cells, and T proliferating cells (Fig. 6 C, Figure S12 A ).While this does not provide a comprehensive understanding of SSRP1's mechanisms, this implied that SSRP1 may affect the immune microenvironment by affecting the functioning of immune cells. Moreover, to provide further evidence for the immunosuppressive role of SSRP1, a correlation heatmap analysis was conducted involving SSRP1 and various immunosuppressive molecules in TCGA-PAAD. The findings demonstrated a significant positive correlation between SSRP1 and these immunosuppressive factors (Fig. 6 D, Figure S12 B ). Given the proposed mechanisms of SSRP1, it can be postulated that it may facilitate the expression of these immunosuppressive genes, thereby contributing to immune suppression and, ultimately, the progression of pancreatic cancer. The role relating to SSRP1 in pancreatic cancer GEO datasets analysis uncovered that SSRP1 expression is markedly elevated in pancreatic cancer (Fig. 7 A). Consistent with this, the results of the Western blotting analysis indicated that SSRP1 protein levels were heightened within pancreatic cancer cells contrasted with normal pancreatic epithelial cells (HPDE) (Fig. 8 A). Correlational analysis about SSRP1 expression in the TCGA database indicates that higher SSRP1 levels correlate with poorer outcomes, including OS, DSS, and PFI events. Higher expression concerning SSRP1 was also noted in patients with more advanced pathological grades of pancreatic cancer. A drop in expression was observed at grade G4, likely attributable toward the limited number of patients in TCGA cohort. Conversely, no significant correlations were identified between SSRP1 expression and factors including gender, pathological stage, N stage, or M stage in gastric cancer patients (Fig. 7 B). Subsequent Kaplan-Meier curve analyses indicated a persistent link between high SSRP1 expression and poorer clinical outcomes across all clinical subgroups analyzed (Fig. 7 C). To Supplementallly elucidate the functional part about SSRP1 amid pancreatic cancer, we conducted a transfection experiment in which we introduced SSRP1-targeting siRNAs into MIAPaCa-2 and PANC-1 cells. The silencing efficiency of these siRNAs is depicted in a bar graph (Fig. 8 B,C). The CCK-8 and colony formation assays revealed that knockdown of SSRP1 consequentially restrained pancreatic cancer cell proliferation (Fig. 8 D,F). Similarly, the Edu incorporation assay demonstrated a notable decline in proliferation in cells transfected with siSSRP1 (Fig. 8 E). Targeted disruption of SSRP1 significantly reduced the migratory and invasive abilities of these cells evaluated in wound healing and Transwell assays (Fig. 8 G,H). In conclusion, these findings provide robust support for the premise that targeting SSRP1 effectively impairs the proliferation, migration, and invasion of pancreatic cancer. Discussion Numerous previous studies have extensively described the role of SSRP1 in promoting target gene transcription, DNA damage repair, and tumor drug resistance [ 5 , 11 , 46 ]. Recent studies have shown that CBL0137, a small molecule inhibitor of SSRP1, can not only increase the sensitivity of high-grade serous ovarian cancer (HGSCs) to PARP inhibitors, but also the combination of CBL0137 and PARP inhibition represents a new therapeutic strategy [ 47 ]. Moreover, the combination effect of SSRP1 and immunosuppressors is also being studied.When CBL0137 is used in combination with dual immune checkpoint inhibitors, the tumor growth of diffuse pleural mesothelioma (DPM) is significantly inhibited[ 48 ]. This indicates that the relationship between SSRP1 and tumor microenvironment is very complex and needs to be further explored. Building on prior research, our observation systematically analyzed the prognostic relevance, expression and function about SSRP1 across various cancer types. Alterations in gene expression levels in tumor tissues are essential for regulatory functions, and it is notable that SSRP1 protein expression and mRNA in pan-cancer and adjacent tissues exhibited strong concordance. Interestingly, SSRP1 mRNA levels were reduced, whereas protein levels were elevated in ACC, LAML and OV compared to normal tissues which possibly attributed to modifications of SSRP1 occurring after transcription and translation. In acute myeloid leukemia cells lacking METTL3, in spite of a 2–5 log 2-fold boost in mRNA expression, the protein levels of c-MYC, PTEN and Bcl-2 existed lowered [ 49 ]. This study identified an association between SSRP1 and the expression of various RNA modification-related genes, indicating that discrepancies between SSRP1 mRNA and protein expression could stem from post-transcriptional modifications.While low SSRP1 expression correlates with a favorable prognosis in ACC, LAML, MESO, PAAD, SARC, and LIHC, it appears to act as a protective factor in KIRC and UCS.Polo-like kinase 1 (Plk1) is frequently overexpressed in various human tumors and is considered an oncogene and a promising cancer target. However, some researchers argue that Plk1 overexpression can induce chromosome instability and inhibit tumor development [ 50 ]. The dual effect of SSRP1 likely stems from the triggering of particular molecular pathways across various cancers emphasizing the intrication and heterogeneity about cancer biology. Given role of SSRP1 in mediating DNA damage repair, its correlation with DNA repair pathways was analyzed across various cancer types.Unsurprisingly, SSRP1 expression was positively correlated with DNA repair, and surprisingly, SSRP1 expression was almost significantly correlated with HR related genes. Lysin-specific demethylase 1 (LSD1) is a key regulator of OC, inhibiting the transcription of BRCA1/2 and RAD51 to impact HR [ 51 ]. Therefore, we can infer that the potential mechanism of SSRP1 is to promote DNA damage repair by promoting the transcription of HR-related genes.The correlation heatmap of the CancerSEA single-cell database also revealed the correlation of SSRP1 and DNA damage. DNA replication and tumor proliferation pathway analysis also implied the potential of SSRP1 as an oncogene. TMB and MSI are predictive parameters for a tumor's sensitivity to immune checkpoint inhibitors (ICIs), which have revolutionized cancer therapy by reactivating T-cells. While tumors with high MSI or TMB are more predisposed to respond toward ICIs, the complexity of the immune response necessitates considering TMB alongside various other factors to optimize ICI outcome predictions [ 52 ]. CTL are crucial in anti-tumor activity due to their diverse molecular characteristics, which allow them to directly kill tumor cells upon recognizing target cells [ 53 ]. Our study identified an inverse correlation between SSRP1 expression and CTL levels.It is reasonable to assume that SSRP1 suppresses the immune microenvironment by affecting CTL. While SSRP1 positively correlates with T cell exhaustion signatures in ACC, KICH, PRAD, and SARC, it shows a negative correlation in CESC, GBM, OV, and SKCM. This discrepancy may be due to the balanced regulatory nature of T cell exhaustion.Regulated by complex processes such as transcriptome, epigenome and metabolic alterations, the emergence of ICB therapy enables exhausted cells to restore some function against various cancer types [ 54 ]. Single-cell RNA sequencing technology have led to the development of innovative approaches for analyzing genetic, epigenetic, spatial, proteomic and lineage information[ 55 ]. In hepatocellular carcinoma, SSRP1 has been shown to be associated with depleted CD8 + T cell infiltration[ 56 ]. Interestingly, our findings demonstrated that SSRP1 was predominantly enriched in CD8 + and CD4 + T cells in pancreatic cancer. Additionally, spatial transcriptomics revealed co-localization of SSRP1 with CD14 and CD44. The role of CD14 in innate immunity has been increasingly recognized in recent studie[ 57 ]. In gastric cancer, CD14 is associated with increased tumor invasiveness [ 58 ]。An accumulation of CD14 + monocytes has been observed in renal cell carcinoma with their increased presence correlating with poorer survival outcomes in patients[ 59 ]. CD44 as a tumor marker for cancer stem cells is primarily associated with iron endocytosis-mediated cellular plasticity. Researchers have defined CD44-CXCR2 + neutrophils as tumor-specific neutrophils in both mouse and human gastric cancer through single-cell analysis, characterizing the tumor-specific neutrophil population in gastric cancer[ 60 ]. In conclusion, our findings highlight the crucial role of SSRP1 in tumor microenvironment suggesting its potential as a target for enhancing immune therapy. Our study still has limitations. Firstly, we have only investigated the differential expression and prognosis of SSRP1 across various cancer types highlighting SSRP1 as an oncogene in pancreatic cancer. However, we do not conduct a deeper exploration of the underlying molecular mechanisms. Therefore, our future research will focus on exploring the specific mechanisms of SSRP1 in pancreatic cancer. Additionally, our findings require further validation with clinical data. Finally, the use of bioinformatics methods introduce some inevitable sample bias which may lead to inherent biases in the resulting datas. Conclusion This study utilized bioinformatics to investigate the role of SSRP1 in tumorigenesis with highlighting its regulatory function in TME. Subsequently, the potential capacity belonging to SSRP1 was validated in pancreatic cancer. These findings hold remarkable translational implications for the diagnosis and treatment of tumors. Abbreviations Full name Abbreviations Specific Recognition Protein 1 Facilitates chromatin transcription complex Genotype-Tissue Expression Cancer Cell Line Encyclopedia Single-Nucleotide Variation Transcripts Per Million Receiver Operating Characteristic Overall Survival Disease-Specific Survival Progression-Free Interval Disease-Free Interval Tumor Immune Dysfunction and Exclusion Copy Number Variation Tumor Mutational Burden Homologous Recombination Deficiency Microsatellite Instability Mismatch Repair DNA methyltransferases Homologous Recombination Repair Differentially Methylated Probes-based stemness index N1‐methyladenosine 5-methylcytosine N6‐methyladenosine Alternative Splicing Percent Spliced-In Gene Ontology Gene set enrichment analysis Tumor Immune Syngeneic MOuse Tumor Immune Single-cell Hub 2 Mechanisms of Action Cytotoxic T Lymphocytes Immune Checkpoint Inhibitor Adrenocortical carcinoma Bladder Urothelial Carcinoma Breast invasive carcinoma; Cervical squamous cell carcinoma and endocervical adenocarcinoma Cholangiocarcinoma Colon adenocarcinoma Adenocarcinoma of the colon and rectum Diffuse Large B-cell Lymphoma, a type of Lymphoid Neoplasm Esophageal carcinoma Glioblastoma multiforme Glioma Squamous cell carcinoma of the head and neck Kidney Chromophobe Renal clear cell carcinoma of the kidney Renal papillary cell carcinoma of the kidney Acute Myeloid Leukemia Brain Lower Grade Glioma Liver hepatocellular carcinoma Lung adenocarcinoma Lung squamous cell carcinoma Mesothelioma Non-Small Cell Lung Carcinoma Ovarian serous cystadenocarcinoma Pancreatic adenocarcinoma Pheochromocytoma and Paraganglioma Prostate adenocarcinoma Rectum adenocarcinoma Sarcoma Stomach adenocarcinoma Skin Cutaneous Melanoma Testicular Germ Cell Tumors Thyroid carcinoma Thymoma Uterine Corpus Endometrial Carcinoma Uterine Carcinosarcoma Uveal Melanoma SSRP1 FACT GTEx CCLE SNV TPM ROC OS DSS PFI DFI TIDE CNV TMB HRD MSI MMR DNMTs HRR DMPsi m1A m1C m6A AS PSI GO GSEA TISMO TISCH2 MoA CTLs ICI ACC BLCA BRCA CESC CHOL COAD COADREAD DLBC ESCA GBM GBMLGG HNSC KICH KIRC KIRP LAML LGG LIHC LUAD LUSC MESO NSCLC OV PAAD PCPG PRAD READ SARC STAD SKCM TGCT THCA THYM UCEC UCS UVM Declarations Ethics approval and consent to participate Given that the omics datasets employed in this study were retrieved from publicly accessible repositories that have undergone prior ethical approval and consent procedures, the requirement for additional ethical approval and consent is deemed inapplicable in the context of this research. Consent for publication All authors agree for publication. Availability of data and materials The data can be obtained from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding Not applicable. Author contributions CL: Conceptualization, Methodology, Formal analysis, Writing-original draft preparation; HW: Investigation, Writing - review and editing; JW: Conceptualization, Investigation, Formal analysis; Writing-original draft preparation; HH: Investigation, Writing-review and editing; QG: Methodology; Writing-review and editing; YL: Formal analysis; Writing-review and editing; PW: Writing - review and editing, Resources, Supervision, Conceptualization. Acknowledgments We extend our sincere gratitude to the personnel responsible for the curation and maintenance of these public databases. Supplementary material The online version contains supplementary material available at Author details 1 Department of Hepatobiliary Surgery, Weihai Central Hospital Affiliated to Qingdao University, Weihai, China 2 Department of Endocrinology, Weihai Central Hospital Affiliated to Qingdao University, Weihai, China. 3 Shandong Provincial Key Medical and Health Laboratory of Iron Metabolism Clinical Research, Weihai, China. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12–49. Hu J, Wang SG, Hou Y, Chen Z, Liu L, Li R, Li N, Zhou L, Yang Y, Wang L, et al. Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression. Nat Genet. 2024;56(3):442–57. Hedou J, Maric I, Bellan G, Einhaus J, Gaudilliere DK, Ladant FX, Verdonk F, Stelzer IA, Feyaerts D, Tsai AS et al. Discovery of sparse, reliable omic biomarkers with Stabl. Nat Biotechnol 2024. 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Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx FigureS1.jpg FigureS2.jpg FigureS3.jpg FigureS4.jpg FigureS5.jpg FigureS6.jpg FigureS7.jpg FigureS8.jpg FigureS9.jpg FigureS10.jpg FigureS11.jpg FigureS12.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6927777","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489384414,"identity":"350f9949-4d52-4665-ac91-42b62af00bd3","order_by":0,"name":"Chuanbao Li","email":"","orcid":"","institution":"Department of Hepatobiliary Surgery, Weihai Central Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Chuanbao","middleName":"","lastName":"Li","suffix":""},{"id":489384415,"identity":"944f6f3b-d206-4623-b259-effbd4627265","order_by":1,"name":"Hailiang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACA3YEm5nhA5DkAzHwakGSZmacwcAgwUaSFmYeYrSYMzOwSfzcUctgcLz3sbFNxeE6NvbmwwYMNTbRuLRYNjOwSfaeOc5gcOa4cXLOmcMSbDzHkhMYjqXlNuBy2GEGthu8bccYzG6kMR/ObQNqkcgxPsDYcBivlpt/YVosidVym7etBqwlmRGqJQG/Fsb237JtB3jszxxjNuw5ky7ZBvSLQQI+vxxvPmz4tq1OTrK9jVniR4U1Pz8wxCQ+1Njg1MLAwAiSOsyDKpiAUzkc1BFWMgpGwSgYBSMXAACz+lEXbSBoAwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Hepatobiliary Surgery, Weihai Central Hospital Affiliated to Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Hailiang","middleName":"","lastName":"Wang","suffix":""},{"id":489384416,"identity":"0a0d574f-8ece-46fb-ac94-0f94a4aa34f4","order_by":2,"name":"Junlu Wang","email":"","orcid":"","institution":"Department of Hepatobiliary Surgery, Weihai Central Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Junlu","middleName":"","lastName":"Wang","suffix":""},{"id":489384417,"identity":"671c1a6a-9098-4a18-9448-45a5ac616c1f","order_by":3,"name":"Haipeng Hou","email":"","orcid":"","institution":"Department of Hepatobiliary Surgery, Weihai Central Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Haipeng","middleName":"","lastName":"Hou","suffix":""},{"id":489384418,"identity":"53366fe0-d395-4bb6-a9c7-e31d3965bd34","order_by":4,"name":"Qi Gong","email":"","orcid":"","institution":"Shandong Provincial Key Medical and Health Laboratory of Iron Metabolism Clinical Research","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Gong","suffix":""},{"id":489384419,"identity":"e27cc438-77f1-450b-bc02-4ee87edd55e1","order_by":5,"name":"Yanlin Lin","email":"","orcid":"","institution":"Department of Endocrinology, Weihai Central Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yanlin","middleName":"","lastName":"Lin","suffix":""},{"id":489384420,"identity":"7b6e1281-e1b7-4423-8120-a48569c07e0b","order_by":6,"name":"Pengbo Wang","email":"","orcid":"","institution":"Department of Endocrinology, Weihai Central Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Pengbo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-19 05:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6927777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6927777/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87700310,"identity":"c2ac8871-683d-4483-aaad-4d174519fe94","added_by":"auto","created_at":"2025-07-28 07:17:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":697234,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of SSRP1 in pan-cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Differential expression of SSRP1 mRNA in the TCGA and GTEx databases, visualized using a box plot. (\u003cstrong\u003eB)\u003c/strong\u003eAnalysis of SSRP1 protein expression in various cancers using UALCAN. (\u003cstrong\u003eC) \u003c/strong\u003eExamination of SSRP1 expression in diverse cell lines utilizing the CCLE database. (\u003cstrong\u003eD) \u003c/strong\u003eAnalysis of SSRP1 expression in pancreatic cancer cell lines. (\u003cstrong\u003eE-F)\u003c/strong\u003e Utilization of the GEPIA2 tool to generate a survival heatmap and Kaplan-Meier survival curve for SSRP1 across pan-cancer datasets. Statistical significance is denoted as follows: *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001; ns: not significant.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/c4809d0aa4cee4529e40670c.jpg"},{"id":87700320,"identity":"4c727605-dd1e-4714-a410-51a9f275969a","added_by":"auto","created_at":"2025-07-28 07:17:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":764302,"visible":true,"origin":"","legend":"\u003cp\u003eGene mutation and DNA methylation analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Analysis of SSRP1 mutations utilizing the cBioPortal database. (\u003cstrong\u003eB)\u003c/strong\u003e Kaplan-Meier survival curves comparing pan-cancer outcomes between SSRP1 mutant and non-mutant groups. (\u003cstrong\u003eC)\u003c/strong\u003eSpecific single nucleotide variant (SNV) site mapping of SSRP1. (\u003cstrong\u003eD)\u003c/strong\u003eDifferential SSRP1 protein expression between chromatin-altered and non-altered groups. (\u003cstrong\u003eE)\u003c/strong\u003e SSRP1 expression in the context of pan-cancer TP53 mutations. (\u003cstrong\u003eF)\u003c/strong\u003e Analysis of the correlation between SSRP1 expression and DNA methylation. (\u003cstrong\u003eG)\u003c/strong\u003e Methylation expression and its association with clinical prognosis as depicted by Kaplan-Meier survival curves.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/81d97043b709115c2e4ded6c.jpg"},{"id":87700315,"identity":"5fcfa602-8147-49ed-9e0a-14173545711a","added_by":"auto","created_at":"2025-07-28 07:17:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1140800,"visible":true,"origin":"","legend":"\u003cp\u003eSSRP1 is associated with RNA splicing, RNA modification and genomic instability\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The oncosplicing tool was employed to examine the splicing of SSRP1 mRNA and its prognostic implications. (\u003cstrong\u003eB)\u003c/strong\u003e a correlation analysis was conducted between SSRP1 and RNA splice-related molecules across various cancer types. (\u003cstrong\u003eC)\u003c/strong\u003e a heatmap analysis was performed to identify genes associated with SSRP1 and RNA modifications. (\u003cstrong\u003eD)\u003c/strong\u003e a heatmap depicting the correlation between SSRP1 and DNA methyltransferase was generated. (\u003cstrong\u003eE-F)\u003c/strong\u003e the correlation of SSRP1 with TMB and MSI across different cancers was analyzed. (\u003cstrong\u003eG)\u003c/strong\u003e the GeneMANIA tool was utilized to analyze molecules interacting with SSRP1.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/bb1fd4a2098482a2e851a0fe.jpg"},{"id":87702363,"identity":"ae27f671-c3e0-4d1a-a57e-a06bcd69967b","added_by":"auto","created_at":"2025-07-28 07:33:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":855759,"visible":true,"origin":"","legend":"\u003cp\u003eImmunoscore and pathway enrichment and drug sensitivity analysis of SSRP1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e SSRP levels were associated with StromalScore, The correlation analysis of ImmuneScore and ESTIMATEScore was performed, and the results were presented as heat maps Display. (\u003cstrong\u003eB)\u003c/strong\u003e analysis of SSRP1 expression and DNA replication, tumor proliferation, and DNA repair signals The correlation of pathways. (\u003cstrong\u003eC)\u003c/strong\u003e Correlation analysis of SSRP1 and DNA damage repair related genes in pan-cancer. (\u003cstrong\u003eD) \u003c/strong\u003e\u0026nbsp;Using the UALCAN tool to analyze somatic alterations in specific pathways vs Differences in the expression of SSRP1. (\u003cstrong\u003eE)\u003c/strong\u003e GSCA database was utilized to assess the correlation between SSRP1 expression and various pharmacological agents. (\u003cstrong\u003eF) \u003c/strong\u003eBubble plots generated in CancerSEA were employed to investigate the association between SSRP1 and tumor stemness, as well as the relationship between DNA damage and pathways involved in repair and metastasis.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/b4b2f9ca7e61db33bb9e1a28.jpg"},{"id":87700324,"identity":"43507d5b-c0e9-49ae-bb6f-d69993fd9e0a","added_by":"auto","created_at":"2025-07-28 07:17:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":800822,"visible":true,"origin":"","legend":"\u003cp\u003eSSRP1 is associated with immunity\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Comparative analysis of SSRP1 expression levels (high vs. low) in patients undergoing immunotherapy, examining the relationship between SSRP1 expression and patient survival and prognosis. (\u003cstrong\u003eB)\u003c/strong\u003e investigation of the correlation between SSRP1 expression and cytotoxic T lymphocyte (CTL) levels. (\u003cstrong\u003eC) \u003c/strong\u003e\u0026nbsp;evaluation of SSRP1 expression and T cell exhaustion across various cancer types using GEPIA2 signature correlation. (\u003cstrong\u003eD-E)\u003c/strong\u003eheat map illustrating the correlation between SSRP1 expression and infiltration of CD8+ T cells and myeloid-derived suppressor cells (MDSCs). (\u003cstrong\u003eF) \u003c/strong\u003elinkage analysis between SSRP1 expression and CTL levels.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/019d72731a47443a19c52184.jpg"},{"id":87700330,"identity":"2fbed342-d42d-4240-b179-8bd2d95b094f","added_by":"auto","created_at":"2025-07-28 07:17:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":876053,"visible":true,"origin":"","legend":"\u003cp\u003eSSRP1 is connected to immunosuppression\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The CORST database was utilized to analyze the expression levels of SSRP1 across various cancer types. (\u003cstrong\u003eB)\u003c/strong\u003e An evaluation was conducted comparing SSRP1 expression with the coexpression patterns of molecules implicated in T-cell exhaustion, where circles in the figure denote SSRP1 expression. (\u003cstrong\u003eC)\u003c/strong\u003e A single-cell analysis was performed to assess the enrichment and abundance of SSRP1 in immune cells. (\u003cstrong\u003eD)\u003c/strong\u003e The relationship between SSRP1 and immunosuppression was investigated in the TCGA-PAAD dataset, with a scatter plot illustrating molecular correlations.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/bccf074cc195436193a92a2a.jpg"},{"id":87700322,"identity":"1247683d-1628-4a38-bad4-407a06a22c2c","added_by":"auto","created_at":"2025-07-28 07:17:07","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":660614,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in SSRP1 expression and subgroup survival analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Analysis of SSRP1 expression in pancreatic cancer and normal tissues using GEO datasets, highlighting differential expression in pancreatic tissue. (\u003cstrong\u003eB)\u003c/strong\u003e Examination of SSRP1 expression across various clinical subgroups within the TCGA-PAAD dataset, focusing on differential expression. (\u003cstrong\u003eC)\u003c/strong\u003e Kaplan-Meier survival curves comparing high and low SSRP1 expression groups within different clinical subgroups. Statistical significance is indicated as follows: *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/13a9a2e78d600c51be2fe888.jpg"},{"id":87702361,"identity":"3cf70ac4-d2a1-48ed-bd61-606c52d040c7","added_by":"auto","created_at":"2025-07-28 07:33:07","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":658560,"visible":true,"origin":"","legend":"\u003cp\u003eDown-regulation of SSRP1 inhibits the malignant phenotype of pancreatic cancer cell\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eExpression levels of SSRP1 in normal pancreatic epithelial cells and pancreatic cancer cells. (\u003cstrong\u003eB-C) \u003c/strong\u003eProtein levels of SSRP1 in cells transfected with siRNA.\u003cstrong\u003e(D-F)\u003c/strong\u003e The impact of SSRP1 on tumor cell proliferation was assessed using CCK-8, colony formation and EdU uptake assays (scale bar: 20 μm). (\u003cstrong\u003eG)\u003c/strong\u003e Tumor cell migration following SSRP1 knockdown was evaluated using a wound healing assay. (\u003cstrong\u003eH) \u003c/strong\u003eTumor cell migration and invasion were assessed using a Transwell assay. (Statistical significance is indicated as follows: *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.)\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/ccccf1d6ff5b45223a345227.jpg"},{"id":95763296,"identity":"50497e0c-9331-43fc-8e48-95e9a132b08b","added_by":"auto","created_at":"2025-11-12 18:38:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7815685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/46c79d18-dd34-4a56-9c76-b7e087f9d031.pdf"},{"id":87701724,"identity":"513e676a-c1c0-4e46-bd76-e369df0251ef","added_by":"auto","created_at":"2025-07-28 07:25:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16595,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/41de7ce87b0113eff6f0f0a3.docx"},{"id":87700313,"identity":"84706783-f530-44c5-8c71-24dcbe73b557","added_by":"auto","created_at":"2025-07-28 07:17:07","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":582305,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/52e1786a4e40ad9972de6a1e.jpg"},{"id":87701726,"identity":"a8d82791-939f-45ab-85d5-e9e052e7ad3f","added_by":"auto","created_at":"2025-07-28 07:25:07","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":555279,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/1747f3f27240502bd4d9a50e.jpg"},{"id":87700329,"identity":"d65a92ce-5ba4-4d42-bcec-af85f170ac11","added_by":"auto","created_at":"2025-07-28 07:17:08","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":638946,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/1c968f46141efd778359f3e7.jpg"},{"id":87700312,"identity":"398670d8-ca15-495f-bb5e-df0279f912e2","added_by":"auto","created_at":"2025-07-28 07:17:07","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":364903,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/adeaf2db2cd5d910fdec0314.jpg"},{"id":87700335,"identity":"210a5d74-86c2-4a21-94a0-499d78a011e3","added_by":"auto","created_at":"2025-07-28 07:17:08","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":520500,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/d6e421c02590b0b6dd02a18e.jpg"},{"id":87702362,"identity":"55333d63-956a-40ff-bc8e-cd42897da82f","added_by":"auto","created_at":"2025-07-28 07:33:07","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":711542,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/a0d205484e120a823fb1469b.jpg"},{"id":87701735,"identity":"1ac610ec-9817-45e0-9d36-84f85415b4fe","added_by":"auto","created_at":"2025-07-28 07:25:08","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":743809,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/bed984c80639dbec1d03af86.jpg"},{"id":87700333,"identity":"89a2cb3d-2af6-4e2e-8f12-0945baf180c1","added_by":"auto","created_at":"2025-07-28 07:17:08","extension":"jpg","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":335363,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/d594b137d3fbf02d670fe89d.jpg"},{"id":87702364,"identity":"5183596f-cb18-4878-a0b3-57fc7915db70","added_by":"auto","created_at":"2025-07-28 07:33:08","extension":"jpg","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":878850,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/f40992aea9297dd55ec51485.jpg"},{"id":87701741,"identity":"095573a3-1498-4be0-a7c3-600aa625bd7b","added_by":"auto","created_at":"2025-07-28 07:25:08","extension":"jpg","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":391492,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/997d292f480d794895f31209.jpg"},{"id":87701748,"identity":"adfb0b34-df37-4214-9a1b-95862674c1b2","added_by":"auto","created_at":"2025-07-28 07:25:09","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":529837,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/e57748e1a92771315c56e664.jpg"},{"id":87700338,"identity":"39d0c006-36a8-460e-ac57-2bc053fb5bdb","added_by":"auto","created_at":"2025-07-28 07:17:08","extension":"jpg","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":689326,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6927777/v1/da29674c355a80db2560b71b.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive analysis reveals immunosuppressive part of SSRP1 in pan-cancer and its potential funtion in pancreatic cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer is one of the deadliest tumors with a new incidence and mortality rate of 3% and 8% in the United States in 2024[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Multi-omics research has been regarded as an important research tool to study tumor progression as well as to find effective therapeutic targets[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. SSRP1, also known as FACT80, is a subunit of the Facilitates Chromatin Transcription (FACT) complex that aids in chromatin elongation and transcription of target genes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Some researchers have claimed that SSRP1 removes H2Bub and represses transcription of MERVL and MERVL fusion genes by recruiting Usp7[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. SSRP1 can facilitate the replication initiation assembly on the nucleus of somatic cells in African Xenopus laevis egg extracts by promoting the expulsion of histone H1 from somatic chromatin, and embryos at higher SSRP1 protein levels develop significantly faster[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].0020An analysis of single-cell transcriptomes was conducted on tumor and paired distal liver tissue samples from five patients with hepatoblastoma. As a potential epigenetically targeted therapeutic strategy, FACT inhibits the oncogenic feedback loop between MYC and SSRP1 in probable hepatoblastomas[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ying et al.found that SSRP1 recruits to DNA single-strand breaks (SSB) in a PARP-dependent manner and promotes DNA damage repair by interacting with the N-terminus of the DNA repair protein XRCC1 retained at the DNA damage site[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As a result of mutual transcriptional regulation and activation, SSRP1 and EWSR1-FLI1 promote cell cycle/DNA replication and the IGF1R-PI3K-AKT-mTOR pathway in Ewing sarcoma, thereby driving tumorigenesis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. SSRP1 has been identified as an oncogene that promotes the progression of hepatocellular carcinoma, nodal carcinoma, and nasopharyngeal carcinoma[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, to date, there has not been a comprehensive pan-cancer study for SSRP1.We sought to investigate the diagnostic and immunological functions of SSRP1 across various cancers, with a particular focus on its role in pancreatic cancer. A thorough pan-cancer investigation of SSRP1 was carried out utilizing a variety of databases and tools to identify differences in expression, clinical characteristics, genomic heterogeneity, and response to immunotherapy in different tumors and normal tissues. Notably, our study revealed that SSRP1 has important roles in DNA repair, T cell depletion, and immunotherapy.We investigated SSRP1 expression differences between normal pancreatic tissue and pancreatic cancer utilizing the GEO database.Our experiments with pancreatic cancer cell lines revealed that SSRP1 enhances the malignant phenotype progression, indicating its potential as a novel diagnostic marker for pancreatic cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSSRP1 expression data acquisition\u003c/h2\u003e\u003cp\u003eUCSC XENA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to attain TPM-formatted RNA sequencing data for Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) processed by Toil Process Harmonization. Normal tissue data of GTEx and TCGA were extracted and uniformly processed as log2(TPM\u0026thinsp;+\u0026thinsp;1)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. SSRP1 protein data for various cancers and normal tissues, The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) was used to determine differential expression[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/analysis-prot.html\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/analysis-prot.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Cancer Cell Line Encyclopedia (CCLE) provides information on SSRP1 expression in cancer cell lines[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sites.broadinstitute.org/ccle/\u003c/span\u003e\u003cspan address=\"https://sites.broadinstitute.org/ccle/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SSRP1 expression in pancreatic cancer and normal tissues was obtained from Gene Expression Omnibus (GEO) data (GSE16515, GSE15471, GSE62452, GSE28735), which were normalized and homogenized, and visualized with the ggplot2 package.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferential expression of SSRP1 in pan-cancer in different clinical subgroups and ROC diagnostic curves\u003c/h3\u003e\n\u003cp\u003eAmong the extracted TCGA data, data without corresponding clinical information were discarded and TNM staging differential expression box plots were visualized using The R 'ggplot2' package. TISIDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was used to analyze SSRP1 expression across various stages and grades. We generated receiver operating characteristic curves (ROC) from a combined cohort derived from TCGAand GTEx datasets in order to assess the diagnostic utility of SSRP1 across various cancer types. ROC analysis was conducted with the pROC package, and the results were visualized via ggplot2.\u003c/p\u003e\n\u003ch3\u003eSurvival curves and prognostic value\u003c/h3\u003e\n\u003cp\u003eGene Expression protiling lnteractive Anaysis 2 (GEPIA2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] was utilized to obtain pan-cancer survival heatmaps as well as survival curves. Mean, the K-M survival curves of SSRP1 in different clinical subgroups of pancreatic cancer were also obtained[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, the survival prognosis data for the GEO dataset was retrieved from the PanCanSurvPlot online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://zjyy-oncology.asuscomm.cn:20008/\u003c/span\u003e\u003cspan address=\"http://zjyy-oncology.asuscomm.cn:20008/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGenomic Alterations, Mutation Load and Microsatellite Instability and Gene Correlation\u003c/h3\u003e\n\u003cp\u003eThe cBioPortal database was used to SSRP1 mutations and prognosis in pan-cancer genomes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Information about Microsatellite instability(MSI) as well as Tumor mutation burden(TMB) were obtained from TCGA and visualized using the (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.home-for-researchers.com/\u003c/span\u003e\u003cspan address=\"https://www.home-for-researchers.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Meanwhile, UALCAN was utilized to explore the correlation between pan-cancer somatic mutations in key pathways and promoter methylation and SSRP1 expression in pan-cancer[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was analyzed differential expression of SSRP1 in TP53 mutant tumors and its correlation with DNA methylation, Homologous Recombination Repair (HRR), and RNA splicing-related genes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSSRP1 specific structure and DNA methylation\u003c/h3\u003e\n\u003cp\u003eWe explored the relationship between SSRP1 expression and DNA methylation levels in probe cg01250938 in Epigenome-Wide Association Study (EWAS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/ewas/atlas\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/ewas/atlas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. In various cancers and tissues, we investigated the relationship between SSRP1 DNA methylation levels and clinical prognosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The SSRP1 gene sequences and DNA methylation of normal and cancer patients were analyzed using Shiny Methylation Analysis Resource Tool (SMART) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinfo-zs.com/artapp/\u003c/span\u003e\u003cspan address=\"http://www.bioinfo-zs.com/artapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGenomic Heterogeneity, Associated Pathways, and Pharmacological Enrichment\u003c/h2\u003e\u003cp\u003eSangerbox 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vip.sangerbox.com/\u003c/span\u003e\u003cspan address=\"http://vip.sangerbox.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was tasked with investigating the correlation among SSRP1 expression and various factors including HRD, Purity, MEO, Ploidy, LOH, MATH, and RNA modifications in pan-cancer[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We also investigated the impact of RNA splicing event occurrence in SSRP1 in OncoSplicing (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.oncosplicing.com/\u003c/span\u003e\u003cspan address=\"http://www.oncosplicing.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on clinical prognosis and also explored the correlation between SSRP1 and RNA splicing genes in pan-cancer[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Gene Set Cancer Analysis (GSCA)(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA/\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was engaged in relating SSRP1 to drugability[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We utilized the GSVA package in R to extract genes from RNAseq data obtained from TCGA, employing the 'ssgsea' method. Pathway scores were subsequently correlated using Spearman correlation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The relationship between SSRP1 and cancer-related pathways such as stemness, cycling, and DNA repair in pan-cancer was explored using the Cancer Single-cell State Atlas (CancerSEA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biocc.hrbmu.edu.cn/CancerSEA/\u003c/span\u003e\u003cspan address=\"http://biocc.hrbmu.edu.cn/CancerSEA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImmunological role about SSRP1 in pan-cancer as well as single-cell\u003c/h3\u003e\n\u003cp\u003eInitially, the TISDIB tool was employed to analyze SSRP1 expression variations across different molecular and immune subtypes, as well as its association with immune-related molecules, including C1:Wound healing; C2:IFN-γ dominant; C3:Inflammatory; C4: Lymphocyte-depleted; C5:Immunologically quiet; C6:TGF-βdominant. Subsequently. Relationship between immune cells and SSRP1 expression in pan-cancer was explored using TIMER 2.0.Correlation between SSRP1 and immune matrix data of pan-cancer data was performed, and matrix and immune scores for the corresponding data were calculated by R package-estimate[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Utilizing the correlation module in GEPIA2 in order to investigate the connection between SSRP1 expression and the Exhausted T-Cell signature. Tumor Immune Single-cell Hub 2 (TISCH2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/home/\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized toward investigate expression about SSRP1 during various immune cells in pan-carcinoma[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eImmunotherapy Prediction and Spatial Transcriptome\u003c/h3\u003e\n\u003cp\u003eRNAseq data and clinical information from TCGA were analyzed using the TIDE algorithm to predict potential immunotherapy response[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The Tumor Immune Dysfunction and Exclusion(TIDE)(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database, utilizing published transcriptomic biomarkers, predicts patient responses to therapy and overall survival. This study employed TIDE to compare custom biomarkers with existing ones, investigating the correlation between SSRP1 and immunotherapy responsiveness. A database named Tumor Immune Syngeneic Mouse (TISMO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tio.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://tio.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) which is for visualizing and accessing bulk data of syngeneic mouse models. SSRP1 expression levels were visualized across different ICB treatments, including anti-PDL1,anti-PD1, anti-CTLA4, anti-PDL2, and anti-PDL2, comparing pre- and post-treatment stages as well as responders and non-responders[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The comprehensive repository of spatial transcriptomics (CROST) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/crost/home\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/crost/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a powerful research tool. It can be used for single-sample, interactive visualization, and exploration of cancer svg for spatial transcriptomics with multi-omics integration. CROST was used to obtain spatial transcriptomics of SSRP1 expression in different cancers[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, we utilized the SpatialDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://spatialomics.org/SpatialDB/\u003c/span\u003e\u003cspan address=\"http://spatialomics.org/SpatialDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database to investigate the co-expression localization of SSRP1 and T-cell depletion-related molecules as well as DMSC biomarkers[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCell culture\u003c/h2\u003e\u003cp\u003eHPDE cell line and Human pancreatic cancer cell lines (Capan-2, SW1990, BxPC-3, MIAPaCa-2, PANC-1, AsPC-1 and HS766T) were acquired from Suzhou Starfish Biotechnology Co.Ltd (Suzhou,China). The cells were cultured in Dulbecco's Modified Eagle Medium (DMEM, Zeta) or Roswell Park Memorial Institute (RPMI 1640, Zeta) which are supplemented with1% penicillin/streptomycin (Invitrogen, USA) and 10% fetal bovine serum within a humidified incubator maintained at 5% CO2 and 37\u0026deg;C. All cell lines were tested for mycoplasma and identified by short tandem repeat (STR) analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCell transfection\u003c/h2\u003e\u003cp\u003eCells were seeded one day prior to transfection to attain a cell density ranging from 30\u0026ndash;50%. Transfection was performed using Lipofectamine 3000 (Invitrogen, USA) on cells cultured in 6-well plates. The small interfering RNA (siRNA) targeting SSRP1 and the negative control siRNA were procured from Ruibo (Guangzhou, China). Specific sequences were derived from[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. All siRNA sequences used will be found in Supplementary Table\u0026nbsp;1. Subsequent assays were conducted 48 hours post-transfection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eWestern blotting\u003c/h2\u003e\u003cp\u003eProteins were extracted from cells using phosphatase and protease inhibitors (Keygen, Nanjing, China) in RIPA buffer. In the aftermath of protein concentration by the BCA assay, proteins were separated by SDS-PAGE and transferred to nitrocellulose. Primary SSRP1 antibody (1:1000)(ER1901-13, HUABIO, China) and anti-beta Actin antibody (1:10000)(R1207-1, HUABIO, China) were incubated overnight. The next day, the bands were washed with PBST (3 times, 5 minutes each), followed by another wash after 1 hour of incubation with secondary antibody at room temperature. This bands were observed by chemiluminescence. Control groud was beta Actin\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eCell proliferation assay\u003c/h2\u003e\u003cp\u003eAssays of CCK-8 were conducted by seeding 1000 cells into 96-well plates and maintaining them for durations of 1, 2, 3, or 4 days. Subsequently, the medium was refreshed with 100 \u0026micro;L, and 10 \u0026micro;L of CCK-8 reagent (Sigma) was added. The cells were then incubated for 2 hours to facilitate OD 450 measurements. Following this, EdU was introduced into the medium at a concentration of 10 \u0026micro;M and incubated for an additional 2 hours. The cells were subsequently fixed and permeabilized. The subsequent reactions were performed according to the manufacturer's protocol (APExBIO, Cat.No.K1076). Fluorescence microscopy was employed to capture the images (ThermoFishr Scientific).\u003c/p\u003e\u003cp\u003eFor the colony formation assays, A total of 1,000 cells were inoculated and maintained in 6-well plates for a duration of 14 days. Subsequently, the cells were rinsed with PBS and fixed in 4% paraformaldehyde for 30 minutes. Ensuringly, the cells were treated with 0.5% crystal violet for 1 hour at ambient temperature and the colonies were quantified using ImageJ software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eWound healing experiment\u003c/h2\u003e\u003cp\u003eFollowing trypsin digestion, 2 x 10⁶ cells per well were seeded into 6-well plates. After an overnight incubation, a sterile pipette tip was employed to generate a scratch. The initial gap width was photographed immediately after the scratch and the residual gap width was recorded at 0 and 24 hours post-scratching.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCell migration and invasion assay\u003c/h2\u003e\u003cp\u003eCell migration was assessed by seeding 200 \u0026micro;L of pancreatic cancer cell suspension in serum-free DMEM into the upper chamber, while the lower chamber was filled with 10% fetal bovine serum (CORNING, China). After a 24-hour incubation period, the cells in the chamber were fixed in 4% paraformaldehyde for 30 minutes and subsequently stained with 0.1% crystal violet. We then used an inverted light microscope to observe cells that had migrated or invaded the lower chamber, selecting three random fields. For the invasion assay, Matrigel (BD Biosciences, Franklin Lakes, NJ, USA) was evenly applied to Transwell chambers (Cat#: 3422, Corning Inc., Corning, NY, USA) and the procedure was carried out as described above.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R Studio and GraphPad 9.0 software. The Wilcoxon test was applied for comparisons between two groups while one-way ANOVA or Kruskal-Wallis was used for analyzing three or more groups. P value of less than 0.05 was established as the threshold for statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eThe expression levels of SSRP1 across multiple cancer types\u003c/h2\u003e\u003cp\u003eUtilizing GTEx and TCGA data, We performed a comprehensive pan-cancer analysis of SSRP1 mRNA expression, identifying significant differences across 26 cancer types and a prevalent upregulation of SSRP1 in the majority of these cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In our investigation of the TCGA pan-cancer dataset, we focused on evaluating the mRNA expression levels of SSRP1 in cancer tissues compared to matched normal samples, discovered that SSRP1 expression was only downregulated in KICH while upregulated in all other cancer types(\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e). Using the UALCAN tool, we assessed SSRP1 protein expression across various cancer types, revealing significant upregulation in pan-cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). However, no statistically significant difference was observed for UCEC. We further probed the expression of SSRP1 in cell lines derived from pancreatic cancer and other pan-cancer cell lines using CCLE data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC,D). By employing TISDIB, we analyzed the expression differences in SSRP1 across various tumor stages and grades, noting a negative correlation with tumor stage in SKCM(\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e). In contrast, positive correlations were present in other cancers in relation to both tumor stage and grade(\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e). Additionally, data confirmed a strong positive correlation of SSRP1 protein expression with tumor stage, alongside variations in expression linked to TNM staging and molecular subtypes in different tumor types(\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB,C\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eDiagnostic value and K-M survival curves of SSRP1 in pan-cancer\u003c/h2\u003e\u003cp\u003eThe GEPIA2 online tool analyzed the SSRP1 survival heatmap across pan-cancer.Univariate Cox regression revealed that SSRP1 levels were significantly elevated in ACC, LAML, LIHC, LUAD, MESO, PAAD, and SARC.High SSRP1 expression correlated with poor Overall Survival (OS), except in KIRC and UCS where it was linked to better OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, \u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA\u003c/b\u003e). In ACC, BLCA, MESO, PAAD, and LIHC, the high SSRP1 expression group was positively correlated with poor disease-free Survival(DFS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The ROC curve analysis across various cancers suggests that SSRP1 is a potential biomarker for predicting cancer progression (\u003cb\u003eFigure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB\u003c/b\u003e). Using the PanCanSurvPlot tool to identify SSRP1 expression groups in the GEO dataset revealed that high SSRP1 expression correlated with poor clinical prognosis in pan-cancer (\u003cb\u003eFigure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eGenomic Alterations and Genomic Heterogeneity\u003c/h2\u003e\u003cp\u003eFrom a genomic perspective, investigating the impact of gene mutations on pan-cancer is critically important[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We utilized cBioPortal to examine SSRP1 mutation frequency, focusing on CNV and SNV alterations across various cancer types. Notably, SSRP1 amplification was primarily detected in UCS and MESO, while deep deletions were frequently observed in MESO.Single nucleotide variations predominantly occurred in UCEC, BLCA, UCS, SKCM, and CHOL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, C). Kaplan-Meier survival curves indicate that SSRP1 genomic alterations are linked to better patient prognoses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Additionally, we observed a positive correlation between SSRP1 expression and chromatin state changes in BRCA, LUAD, PAAD, and HNSC(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Homologous recombination deficiency (HRD) can induce quantifiable, specific and stable genomic alterations, positioning HRD status as a pivotal factor in cancer treatment strategies and prognosis. A close association exists between HRD and the resistance to PARP inhibitors in advanced breast\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ecancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our analysis showed that SSRP1 expression negatively correlates with HRD in THYM while positively correlates with ACC (\u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA\u003c/b\u003e). Furthermore, polyploidy represents a hallmark of cancer, where estimating tumor purity and ploidy enhances our understanding of cancer genomic evolution and intratumoral heterogeneity. Total mRNA expression from tumor cells across 15 cancer types has been shown to predict tumor progression[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. SSRP1 showed a positive correlation with tumor purity in READ and a unfavorable correlation with ploidy in UVM (\u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eB,D\u003c/b\u003e). We evaluated the correlation between SSRP1 expression and LOH/mutational burden (MATH) in our study of SNV deletions (\u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eE,F\u003c/b\u003e). Based on prior research on neoantigen data across various tumor types [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], we found a favorable correlation between SSRP1 and CHOL, and an opposing relation with COAD (\u003cb\u003eFigure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eC\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe TP53 gene, a critical tumor suppressor, plays an essential role in regulating other genes, and its mutations may have significant implications for gene expression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In our study, we employed TIMER2.0 to analyze the expression of SSRP1 within the TP53 mutation cohort. The violin plot indicated significantly higher SSRP1 expression in this group compared to the control group across multiple cancer types, including BRCA, LUAD, ACC, KICH, LIHC and PAAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Correlation scatter plots from the EWAS database show that SSRP1 expression is typically inversely related to DNA methylation levels in most tumors.However, a positive correlation is notable in testicular cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, \u003cb\u003eFigure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA\u003c/b\u003e). The expression levels of SSRP1 also vary across different tissue types (\u003cb\u003eFigure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eB\u003c/b\u003e). We further utilized this analytical approach to investigate specific segments of the SSRP1 gene, incorporating both normal and cancer patient data regarding DNA methylation levels (\u003cb\u003eFigure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eD,E\u003c/b\u003e). The UALCAN tool was used to examine SSRP1 promoter methylation across various cancers, revealing elevated levels in KIRC, KIRP, and THCA (\u003cb\u003eFigure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC\u003c/b\u003e). Notably, only in the case of LAML did patients with low SSRP1 expression exhibit improved prognoses, whereas, in other cancers, higher levels of SSRP1 methylation correlated with better clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Collectively, these analyses underscore a significant link between SSRP1, genomic instability, DNA methylation, and clinical prognostic outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eSSRP1 is associated with RNA splicing, RNA modification, and multiple oncogenic pathways\u003c/h2\u003e\u003cp\u003eWe carried out a Spearman relationship analysis to evaluate the association between SSRP1 and critical pathways in tumor proliferation, DNA replication, and repair. Our analysis indicated a strong positive correlation between SSRP1 and these cancer progression pathways, highlighting the potential role of SSRP1 in facilitating tumor advancement through these mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The CancerSEA single-cell database corroborated a strong link between SSRP1 and pathways related to the cell cycle, DNA repair, and tumor stemness (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). We also identified a positive relationship between SSRP1 expression across various cancer types and genes related to the Homologous Recombination Repair (HRR) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Furthermore, research has underscored the immunological relevance of RNA splicing in Treg cells, noting that RNA splicing contributes to immune regulation in tumor progression [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Utilizing the Oncosplicing tool, we found that SSRP1 RNA splicing events are consistently linked with more favorable clinical\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eoutcomes in LGG and LUSC, whereas an inverse association was found in BLCA, LIHC, SARC, and OV (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additional findings revealed that SSRP1 is positively correlated with genes involved in RNA splicing across multiple cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). It is acknowledged that epigenetic factors can influence tumor progression through immune pathways [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], resultantly, we analyzed the link between SSRP1 along with genes associated with RNA modification processes, uncovering a significant positive association across pan-cancer data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). DNA methylation is crucial, and in CHOL, SSRP1 and DNMT3L expression are negatively correlated, unlike the positive correlations observed in other cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Alterations in signaling pathways like mTOR, WNT, NRF2, and P53/Rb may cause variations in SSRP1 protein expression, indicating SSRP1's potential role in these pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Interaction networks involving SSRP1 and other molecules were further analyzed using the GeneMANIA tool (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). In our investigation of SSRP1 and potential drug sensitivities, we discovered a negative correlation between SSRP1 expression and sensitivities to GSK1070916 and PIK-93 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Collectively, these results suggest that SSRP1 may exert a significant influence on tumor progression through various cancer pathways.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eSSRP1 is bound to Immune suppression\u003c/h2\u003e\u003cp\u003eThe Estimate algorithm was applied to conduct a relation analysis amid single-gene expression and immune infiltration matrix data within muti-cancer studies. The expression of SSRP1 is adversely correlated with Immune Score, Stromal Score, and Estimate Score. In KIRC, SSRP1 showed a positive correlation with the Stromal Score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We then evaluated the connection between SSRP1 and both TMB and MSI. Significant positive correlations were identified between SSRP1 and TMB in both ACC and STAD and an optimistic interrelation with MSI in TGCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE,F). The GEPIA2 tool was also used to analyze SSRP1 expression about the T cell exhaustion signature across different cancer types.Correlation scatter plots revealed a positive association in ACC and KICH, contrasting with a negative association noted in OV and PAAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The relationship between SSRP1 and CD8\u0026thinsp;+\u0026thinsp;T cell infiltration as well as myeloid-derived suppressor cells (MDSC) was investigated using the TIMER2.0 database. Although no significant correspondence was observed between SSRP1 and CD8\u0026thinsp;+\u0026thinsp;T cell infiltration across pan-cancer, a notable positive correlation was found between SSRP1 and MDSC infiltration. This suggests that SSRP1 may inhibit the immune microenvironment via MDSC infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD,E). Furthermore, the relationships between SSRP1 and various other immune cell infiltrations were examined (\u003cb\u003eFigure \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e\u003c/b\u003e). The TISDIB database confirmed that SSRP1 expression differs among immune subtypes, with significant downregulation in the C3 (Inflammatory) subtype, suggesting a potential functional association with inflammatory signaling pathways (\u003cb\u003eFigure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eA\u003c/b\u003e). We also examined the link among SSRP1 and various immune molecules, such as chemokines, receptors, immunostimulators, and MHC molecules(\u003cb\u003eFigure \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eHigh expression of SSRP1 suppresses immunotherapy response\u003c/h2\u003e\u003cp\u003eAs an increasing number of patients receive combined immune checkpoint blockade (ICB) therapies, the importance of comprehensive biomarker research has become evident, revealing potential new avenues for investigation [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].The complex immune microenvironment leads to effective immunotherapy responses in only a subset of patients [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therefore, it is vital to search for novel biomarkers that can enhance immunotherapy in current clinical settings. We exploited the TIDE database to examine the relationship between SSRP1 expression and responses to ICB therapy. In cohorts from Zhao2019_PD1, Gide2019_PD1, Lauss2017_ACT, and Braun2020_PD1, patients with low SSRP1 expression exhibited enhanced OS and RFS following ICB treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). This finding indicated SSRP1 may play a regulatory role within the immune microenvironment, as the extent of cytotoxic T lymphocyte (CTL) infiltration is critical for the response toward immunotherapy [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. We scrutinized the interconnection between SSRP1 expression and CTL levels in ICB cohorts, discovering a positive correlation in the GSE17536 cohort, whereas other cohorts showed negative correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cb\u003eFigure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eB\u003c/b\u003e). In the ICB cohorts, high SSRP1 expression was linked to decreased CTL infiltration levels across AML, BRCA, COADREAD, HNSC, LUAD, and OV, potentially compromising patient survival and adversely affecting immune treatment responses. Conversely, this pattern did not hold in patients with low SSRP1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Using the TIDE algorithm to predict potential immune treatment responses, we found that only BRCA patients coupled with low SSRP1 expression possessed higher TIDE scores in comparison with those with high expression. In contrast, other tumor types consistently showed higher scores in the high expression group (\u003cb\u003eFigure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eB\u003c/b\u003e). This suggests that targeting SSRP1 may improve responses to ICB therapy. SSRP1 demonstrated superior predictive ability for ICB treatment outcomes compared to other standardized biomarkers, consistently achieving an AUC greater than 0.5 across nine cohorts, with notable performance in the HNSC (Uppaluri2020_PD1) and Melanoma (Riaz2017_PD1) cohorts (\u003cb\u003eFigure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA\u003c/b\u003e). We utilized the TISMO tool to evaluate SSRP1 expression in cytokine-treated cancer cell lines (\u003cb\u003eFigure \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eA\u003c/b\u003e) and with samples before and after anti-PD-1 coupled with anti-CTLA4 treatments (\u003cb\u003eFigure \u003cspan refid=\"MOESM11\" class=\"InternalRef\"\u003eS11\u003c/span\u003eB\u003c/b\u003e). The results indicated significantly lower SSRP1 expression levels in responding samples following cytokine, anti-CTLA4, and anti-PD-1 therapy. Thus, we conclude that high SSRP1 expression may lead to resistance in patients undergoing immunotherapy.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eSingle cell and spatial transcriptome analysis of SSRP1 localization\u003c/h2\u003e\u003cp\u003eTo determine whether SSRP1 expression is associated with cancer-associated immune responses, we began by evaluating the spatial positioning of SSRP1 in a pan-cancer context using the CROST database (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). We obtained spatial transcriptomic images from SpatialDB, revealing a substantial spatial connection between SSRP1 expression and the T cell exhaustion marker CD44 in addition to the MDSC marker CD14 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). To further substantiate the involvement of SSRP1 in the immune microenvironment, we employed TISCH2 to evaluate the enrichment of SSRP1 in particular immune cell populations. Our analysis revealed that SSRP1 was primarily enriched in CD8\u0026thinsp;+\u0026thinsp;T cells, exhausted CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, and T proliferating cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, \u003cb\u003eFigure \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003eA\u003c/b\u003e).While this does not provide a comprehensive understanding of SSRP1's mechanisms, this implied that SSRP1 may affect the immune microenvironment by affecting the functioning of immune cells. Moreover, to provide further evidence for the immunosuppressive role of SSRP1, a correlation heatmap analysis was conducted involving SSRP1 and various immunosuppressive molecules in TCGA-PAAD. The findings demonstrated a significant positive correlation between SSRP1 and these immunosuppressive factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, \u003cb\u003eFigure \u003cspan refid=\"MOESM12\" class=\"InternalRef\"\u003eS12\u003c/span\u003eB\u003c/b\u003e). Given the proposed mechanisms of SSRP1, it can be postulated that it may facilitate the expression of these immunosuppressive genes, thereby contributing to immune suppression and, ultimately, the progression of pancreatic cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eThe role relating to SSRP1 in pancreatic cancer\u003c/h2\u003e\u003cp\u003eGEO datasets analysis uncovered that SSRP1 expression is markedly elevated in pancreatic cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Consistent with this, the results of the Western blotting analysis indicated that SSRP1 protein levels were heightened within pancreatic cancer cells contrasted with normal pancreatic epithelial cells (HPDE) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Correlational analysis about SSRP1 expression in the TCGA database indicates that higher SSRP1 levels correlate with poorer outcomes, including OS, DSS, and PFI events. Higher expression concerning SSRP1 was also noted in patients with more advanced pathological grades of pancreatic cancer. A drop in expression was observed at grade G4, likely attributable toward the limited number of patients in TCGA cohort. Conversely, no significant correlations were identified between SSRP1 expression and factors including gender, pathological stage, N stage, or M stage in gastric cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Subsequent Kaplan-Meier curve analyses indicated a persistent link between high SSRP1 expression and poorer clinical outcomes across all clinical subgroups analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). To Supplementallly elucidate the functional part about SSRP1 amid pancreatic cancer, we conducted a transfection experiment in which we introduced SSRP1-targeting siRNAs into MIAPaCa-2 and PANC-1 cells. The silencing efficiency of these siRNAs is depicted in a bar graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB,C). The CCK-8 and colony formation assays revealed that knockdown of SSRP1 consequentially restrained pancreatic cancer cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD,F). Similarly, the Edu incorporation assay demonstrated a notable decline in proliferation in cells transfected with siSSRP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). Targeted disruption of SSRP1 significantly reduced the migratory and invasive abilities of these cells evaluated in wound healing and Transwell assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG,H). In conclusion, these findings provide robust support for the premise that targeting SSRP1 effectively impairs the proliferation, migration, and invasion of pancreatic cancer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNumerous previous studies have extensively described the role of SSRP1 in promoting target gene transcription, DNA damage repair, and tumor drug resistance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Recent studies have shown that CBL0137, a small molecule inhibitor of SSRP1, can not only increase the sensitivity of high-grade serous ovarian cancer (HGSCs) to PARP inhibitors, but also the combination of CBL0137 and PARP inhibition represents a new therapeutic strategy [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, the combination effect of SSRP1 and immunosuppressors is also being studied.When CBL0137 is used in combination with dual immune checkpoint inhibitors, the tumor growth of diffuse pleural mesothelioma (DPM) is significantly inhibited[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This indicates that the relationship between SSRP1 and tumor microenvironment is very complex and needs to be further explored. Building on prior research, our observation systematically analyzed the prognostic relevance, expression and function about SSRP1 across various cancer types.\u003c/p\u003e\u003cp\u003eAlterations in gene expression levels in tumor tissues are essential for regulatory functions, and it is notable that SSRP1 protein expression and mRNA in pan-cancer and adjacent tissues exhibited strong concordance. Interestingly, SSRP1 mRNA levels were reduced, whereas protein levels were elevated in ACC, LAML and OV compared to normal tissues which possibly attributed to modifications of SSRP1 occurring after transcription and translation. In acute myeloid leukemia cells lacking METTL3, in spite of a 2\u0026ndash;5 log 2-fold boost in mRNA expression, the protein levels of c-MYC, PTEN and Bcl-2 existed lowered [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This study identified an association between SSRP1 and the expression of various RNA modification-related genes, indicating that discrepancies between SSRP1 mRNA and protein expression could stem from post-transcriptional modifications.While low SSRP1 expression correlates with a favorable prognosis in ACC, LAML, MESO, PAAD, SARC, and LIHC, it appears to act as a protective factor in KIRC and UCS.Polo-like kinase 1 (Plk1) is frequently overexpressed in various human tumors and is considered an oncogene and a promising cancer target. However, some researchers argue that Plk1 overexpression can induce chromosome instability and inhibit tumor development [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The dual effect of SSRP1 likely stems from the triggering of particular molecular pathways across various cancers emphasizing the intrication and heterogeneity about cancer biology.\u003c/p\u003e\u003cp\u003eGiven role of SSRP1 in mediating DNA damage repair, its correlation with DNA repair pathways was analyzed across various cancer types.Unsurprisingly, SSRP1 expression was positively correlated with DNA repair, and surprisingly, SSRP1 expression was almost significantly correlated with HR related genes. Lysin-specific demethylase 1 (LSD1) is a key regulator of OC, inhibiting the transcription of BRCA1/2 and RAD51 to impact HR [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Therefore, we can infer that the potential mechanism of SSRP1 is to promote DNA damage repair by promoting the transcription of HR-related genes.The correlation heatmap of the CancerSEA single-cell database also revealed the correlation of SSRP1 and DNA damage. DNA replication and tumor proliferation pathway analysis also implied the potential of SSRP1 as an oncogene.\u003c/p\u003e\u003cp\u003eTMB and MSI are predictive parameters for a tumor's sensitivity to immune checkpoint inhibitors (ICIs), which have revolutionized cancer therapy by reactivating T-cells. While tumors with high MSI or TMB are more predisposed to respond toward ICIs, the complexity of the immune response necessitates considering TMB alongside various other factors to optimize ICI outcome predictions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. CTL are crucial in anti-tumor activity due to their diverse molecular characteristics, which allow them to directly kill tumor cells upon recognizing target cells [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Our study identified an inverse correlation between SSRP1 expression and CTL levels.It is reasonable to assume that SSRP1 suppresses the immune microenvironment by affecting CTL. While SSRP1 positively correlates with T cell exhaustion signatures in ACC, KICH, PRAD, and SARC, it shows a negative correlation in CESC, GBM, OV, and SKCM. This discrepancy may be due to the balanced regulatory nature of T cell exhaustion.Regulated by complex processes such as transcriptome, epigenome and metabolic alterations, the emergence of ICB therapy enables exhausted cells to restore some function against various cancer types [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSingle-cell RNA sequencing technology have led to the development of innovative approaches for analyzing genetic, epigenetic, spatial, proteomic and lineage information[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In hepatocellular carcinoma, SSRP1 has been shown to be associated with depleted CD8\u0026thinsp;+\u0026thinsp;T cell infiltration[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Interestingly, our findings demonstrated that SSRP1 was predominantly enriched in CD8\u0026thinsp;+\u0026thinsp;and CD4\u0026thinsp;+\u0026thinsp;T cells in pancreatic cancer. Additionally, spatial transcriptomics revealed co-localization of SSRP1 with CD14 and CD44. The role of CD14 in innate immunity has been increasingly recognized in recent studie[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In gastric cancer, CD14 is associated with increased tumor invasiveness [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]。An accumulation of CD14\u0026thinsp;+\u0026thinsp;monocytes has been observed in renal cell carcinoma with their increased presence correlating with poorer survival outcomes in patients[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. CD44 as a tumor marker for cancer stem cells is primarily associated with iron endocytosis-mediated cellular plasticity. Researchers have defined CD44-CXCR2\u0026thinsp;+\u0026thinsp;neutrophils as tumor-specific neutrophils in both mouse and human gastric cancer through single-cell analysis, characterizing the tumor-specific neutrophil population in gastric cancer[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In conclusion, our findings highlight the crucial role of SSRP1 in tumor microenvironment suggesting its potential as a target for enhancing immune therapy.\u003c/p\u003e\u003cp\u003eOur study still has limitations. Firstly, we have only investigated the differential expression and prognosis of SSRP1 across various cancer types highlighting SSRP1 as an oncogene in pancreatic cancer. However, we do not conduct a deeper exploration of the underlying molecular mechanisms. Therefore, our future research will focus on exploring the specific mechanisms of SSRP1 in pancreatic cancer. Additionally, our findings require further validation with clinical data. Finally, the use of bioinformatics methods introduce some inevitable sample bias which may lead to inherent biases in the resulting datas.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study utilized bioinformatics to investigate the role of SSRP1 in tumorigenesis with highlighting its regulatory function in TME. Subsequently, the potential capacity belonging to SSRP1 was validated in pancreatic cancer. These findings hold remarkable translational implications for the diagnosis and treatment of tumors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Abbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecific Recognition Protein 1\u003c/p\u003e\n \u003cp\u003eFacilitates chromatin transcription complex\u003c/p\u003e\n \u003cp\u003eGenotype-Tissue Expression\u003c/p\u003e\n \u003cp\u003eCancer Cell Line Encyclopedia\u003c/p\u003e\n \u003cp\u003eSingle-Nucleotide Variation\u003c/p\u003e\n \u003cp\u003eTranscripts Per Million\u003c/p\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003cp\u003eDisease-Specific Survival\u003c/p\u003e\n \u003cp\u003eProgression-Free Interval\u003c/p\u003e\n \u003cp\u003eDisease-Free Interval\u003c/p\u003e\n \u003cp\u003eTumor Immune Dysfunction and Exclusion\u003c/p\u003e\n \u003cp\u003eCopy Number Variation\u003c/p\u003e\n \u003cp\u003eTumor Mutational Burden\u003c/p\u003e\n \u003cp\u003eHomologous Recombination Deficiency\u003c/p\u003e\n \u003cp\u003eMicrosatellite Instability\u003c/p\u003e\n \u003cp\u003eMismatch Repair\u003c/p\u003e\n \u003cp\u003eDNA methyltransferases\u003c/p\u003e\n \u003cp\u003eHomologous Recombination Repair\u003c/p\u003e\n \u003cp\u003eDifferentially Methylated Probes-based stemness index\u003c/p\u003e\n \u003cp\u003eN1‐methyladenosine\u003c/p\u003e\n \u003cp\u003e5-methylcytosine\u003c/p\u003e\n \u003cp\u003eN6‐methyladenosine\u003c/p\u003e\n \u003cp\u003eAlternative Splicing\u003c/p\u003e\n \u003cp\u003ePercent Spliced-In\u003c/p\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003cp\u003eGene set enrichment analysis\u003c/p\u003e\n \u003cp\u003eTumor Immune Syngeneic MOuse\u003c/p\u003e\n \u003cp\u003eTumor Immune Single-cell Hub 2\u003c/p\u003e\n \u003cp\u003eMechanisms of Action\u003c/p\u003e\n \u003cp\u003eCytotoxic T Lymphocytes\u003c/p\u003e\n \u003cp\u003eImmune Checkpoint Inhibitor\u003c/p\u003e\n \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e\n \u003cp\u003eBladder Urothelial Carcinoma\u003c/p\u003e\n \u003cp\u003eBreast invasive carcinoma;\u003c/p\u003e\n \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e\n \u003cp\u003eCholangiocarcinoma\u003c/p\u003e\n \u003cp\u003eColon adenocarcinoma\u003c/p\u003e\n \u003cp\u003eAdenocarcinoma of the colon and rectum\u003c/p\u003e\n \u003cp\u003eDiffuse Large B-cell Lymphoma, a type of Lymphoid Neoplasm\u003c/p\u003e\n \u003cp\u003eEsophageal carcinoma\u003c/p\u003e\n \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e\n \u003cp\u003eGlioma\u003c/p\u003e\n \u003cp\u003eSquamous cell carcinoma of the head and neck\u003c/p\u003e\n \u003cp\u003eKidney Chromophobe\u003c/p\u003e\n \u003cp\u003eRenal clear cell carcinoma of the kidney\u003c/p\u003e\n \u003cp\u003eRenal papillary cell carcinoma of the kidney\u003c/p\u003e\n \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e\n \u003cp\u003eBrain Lower Grade Glioma\u003c/p\u003e\n \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e\n \u003cp\u003eLung adenocarcinoma\u003c/p\u003e\n \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e\n \u003cp\u003eMesothelioma\u003c/p\u003e\n \u003cp\u003eNon-Small Cell Lung Carcinoma\u003c/p\u003e\n \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e\n \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e\n \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e\n \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e\n \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e\n \u003cp\u003eSarcoma\u003c/p\u003e\n \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e\n \u003cp\u003eSkin Cutaneous Melanoma\u003c/p\u003e\n \u003cp\u003eTesticular Germ Cell Tumors\u003c/p\u003e\n \u003cp\u003eThyroid carcinoma\u003c/p\u003e\n \u003cp\u003eThymoma\u003c/p\u003e\n \u003cp\u003eUterine Corpus Endometrial Carcinoma\u003c/p\u003e\n \u003cp\u003eUterine Carcinosarcoma\u003c/p\u003e\n \u003cp\u003eUveal Melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSSRP1\u003c/p\u003e\n \u003cp\u003eFACT\u003c/p\u003e\n \u003cp\u003eGTEx\u003c/p\u003e\n \u003cp\u003eCCLE\u003c/p\u003e\n \u003cp\u003eSNV\u003c/p\u003e\n \u003cp\u003eTPM\u003c/p\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003cp\u003eDSS\u003c/p\u003e\n \u003cp\u003ePFI\u003c/p\u003e\n \u003cp\u003eDFI\u003c/p\u003e\n \u003cp\u003eTIDE\u003c/p\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003cp\u003eHRD\u003c/p\u003e\n \u003cp\u003eMSI\u003c/p\u003e\n \u003cp\u003eMMR\u003c/p\u003e\n \u003cp\u003eDNMTs\u003c/p\u003e\n \u003cp\u003eHRR\u003c/p\u003e\n \u003cp\u003eDMPsi\u003c/p\u003e\n \u003cp\u003em1A\u003c/p\u003e\n \u003cp\u003em1C\u003c/p\u003e\n \u003cp\u003em6A\u003c/p\u003e\n \u003cp\u003eAS\u003c/p\u003e\n \u003cp\u003ePSI\u003c/p\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003cp\u003eTISMO\u003c/p\u003e\n \u003cp\u003eTISCH2\u003c/p\u003e\n \u003cp\u003eMoA\u003c/p\u003e\n \u003cp\u003eCTLs\u003c/p\u003e\n \u003cp\u003eICI\u003c/p\u003e\n \u003cp\u003eACC\u003c/p\u003e\n \u003cp\u003eBLCA\u003c/p\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003cp\u003eCESC\u003c/p\u003e\n \u003cp\u003eCHOL\u003c/p\u003e\n \u003cp\u003eCOAD\u003c/p\u003e\n \u003cp\u003eCOADREAD\u003c/p\u003e\n \u003cp\u003eDLBC\u003c/p\u003e\n \u003cp\u003eESCA\u003c/p\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003cp\u003eGBMLGG\u003c/p\u003e\n \u003cp\u003eHNSC\u003c/p\u003e\n \u003cp\u003eKICH\u003c/p\u003e\n \u003cp\u003eKIRC\u003c/p\u003e\n \u003cp\u003eKIRP\u003c/p\u003e\n \u003cp\u003eLAML\u003c/p\u003e\n \u003cp\u003eLGG\u003c/p\u003e\n \u003cp\u003eLIHC\u003c/p\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003cp\u003eLUSC\u003c/p\u003e\n \u003cp\u003eMESO\u003c/p\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003cp\u003ePAAD\u003c/p\u003e\n \u003cp\u003ePCPG\u003c/p\u003e\n \u003cp\u003ePRAD\u003c/p\u003e\n \u003cp\u003eREAD\u003c/p\u003e\n \u003cp\u003eSARC\u003c/p\u003e\n \u003cp\u003eSTAD\u003c/p\u003e\n \u003cp\u003eSKCM\u003c/p\u003e\n \u003cp\u003eTGCT\u003c/p\u003e\n \u003cp\u003eTHCA\u003c/p\u003e\n \u003cp\u003eTHYM\u003c/p\u003e\n \u003cp\u003eUCEC\u003c/p\u003e\n \u003cp\u003eUCS\u003c/p\u003e\n \u003cp\u003eUVM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that the omics datasets employed in this study were retrieved from publicly accessible repositories that have undergone prior ethical approval and consent procedures, the requirement for additional ethical approval and consent is deemed inapplicable in the context of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCL: Conceptualization, Methodology, Formal analysis, Writing-original draft preparation; HW: Investigation, Writing - review and editing; JW: Conceptualization, Investigation, Formal analysis; Writing-original draft preparation; HH: Investigation, Writing-review and editing; QG: Methodology; Writing-review and editing; YL: Formal analysis; Writing-review and editing; PW: Writing - review and editing, Resources, Supervision, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to the personnel responsible for the curation and maintenance of these public databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Hepatobiliary Surgery, Weihai Central Hospital Affiliated to Qingdao University, Weihai, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Endocrinology, Weihai Central Hospital Affiliated to Qingdao University, Weihai, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eShandong Provincial Key Medical and Health Laboratory of Iron Metabolism Clinical Research, Weihai, China.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A. 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Protein Cell. 2023;14(7):513\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pan-cancer, TCGA, SSRP1, Tumor microenvironment, Immunoinhibition","lastPublishedDoi":"10.21203/rs.3.rs-6927777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6927777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eResearch increasingly showed a correlation between Structure Specific Recognition Protein 1 (SSRP1) and the progression of diverse cancers. Nonetheless, the influences of SSRP1 on pan-cancer remains inadequately understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eDifferential expression of SSRP1 at mRNA levels was systematically assessed across 33 cancer types utilizing TCGA, GTEx and GEO datasets. Analysis of SSRP1 protein expression levels was conducted through the UALCAN tool. Extensive bioinformatics analyses on 33 cancer types, encompassing tumor mutational burden, prognostic, methylation, and immune microenvironment analyses, we employed Sangerbox 3.0, PanCanSurvPlot ,TISIDB, TIDE, TISCH2 and CancerSEA platform for comprehensive analysis. To elucidate the spatial distribution and functional relevance of SSRP1, we first performed spatial transcriptomic analysis by querying CROST and SpatialDB. Subsequently, siRNA-mediated knockdown of SSRP1 was conducted in pancreatic cancer cell lines followed by a panel of functional assays to assess its role in tumorigenesis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIt was observed that SSRP1 expression increased in the majority of tumors acting an essential role in prognosis and diagnosis. There was a strong correlation between terrible clinical outcomes and SSRP1 expression in ACC, LAML, MESO, PAAD and SARC. The association between SSRP1 and tumor heterogeneity, stemness, DNA methyltransferases and Homologous Recombination Repair (HRR) genes were examined. We investigated the relationship between SSRP1 and immune infiltration as well as immunotherapy in pan-cancer. In the analysis of immune microenvironment, SSRP1 was positively correlated with immune supression and Patients exhibiting elevated expression levels of SSRP1 had poor response to immunotherapy. In vitro, the knockdown of SSRP1 inhibited the proliferation, migration and invasion of pancreatic cancer was firstly detected in this study.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn summary, our research offers a comprehensive analysis of SSRP1's functional mechanisms across various cancers and confirms its role in pancreatic cancer, underscoring its Prognostic and therapeutic potential.\u003c/p\u003e","manuscriptTitle":"Comprehensive analysis reveals immunosuppressive part of SSRP1 in pan-cancer and its potential funtion in pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 07:17:02","doi":"10.21203/rs.3.rs-6927777/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56c820af-f6cc-4c88-b8e3-425717d47228","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T18:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 07:17:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6927777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6927777","identity":"rs-6927777","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}