Multi-omics and single-cell analysis reveals TM9SF1 as a biomarker in pan-cancer diagnosis and prognosis, with a special focus on hepatocellular carcinoma

Multi-omics and single-cell analysis reveals TM9SF1 as a biomarker in pan-cancer diagnosis and prognosis, with a special focus on hepatocellular carcinoma | 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 Multi-omics and single-cell analysis reveals TM9SF1 as a biomarker in pan-cancer diagnosis and prognosis, with a special focus on hepatocellular carcinoma Fuxiang Luan, Yuying Cui, Yuxuan Li, Jiahang Hu, Shuwen Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6992142/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 TM9SF1, a transmembrane protein implicated in various cancers, has yet to receive the attention it deserves in oncological research. Leveraging machine learning and publicly available datasets—including TCGA, GTEx, and UALCAN—this study examined TM9SF1 expression patterns across multiple cancer types. We evaluated its prognostic significance through Cox regression and Kaplan-Meier survival analyses, while also delving into genetic mutations, methylation profiles, immune infiltration, and therapeutic drug responses. Our findings revealed that TM9SF1 is markedly overexpressed in numerous cancers and correlates with unfavorable patient outcomes. The protein’s presence was tied to heightened mutation rates, stronger immune and stromal activity, and interactions with diverse immune cell populations and checkpoint molecules. Additionally, TM9SF1 showed associations with tumor heterogeneity, stem-like properties, and DNA methylation regulators. In hepatocellular carcinoma (HCC), it emerged as an independent risk factor, influenced drug sensitivity, and appeared to mediate its effects through Tex cells, as indicated by single-cell sequencing. This multifaceted investigation highlights TM9SF1’s promise as both a prognostic biomarker and a candidate for immunotherapy, paving the way for broader exploration in pan-cancer studies. TM9SF1 Pan-cancer hepatocellular carcinoma Biomarker Multi-omics analysis Tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction TM9SF1, also termed MP70 and HMP70, is a key player in the TM9SF1 superfamily ( 1 ). This family boasts nine transmembrane proteins that are found all over the place in human tissues, and are just as prevalent in yeast, plants, and mammals. Their existence is a testament to their high degree of conservation across various species. ( 2 ). The TM9SF protein family is a group known for its sizeable extracellular section and consists of nine transmembrane spans, which makes it rather distinct ( 3 , 4 ). Yet, despite this, their actual roles in biological processes are still largely a mystery. To date, only a smattering of research suggests that these proteins' activity might correlate with cell sticking together and the development of tumors. ( 5 , 6 ). Research on TM9SF1 has historically been limited, but emerging evidence points to its significant role in tumor development and progression. TM9SF1 collaborates with EBAG9 to modulate prostate cancer cell migration by targeting genes involved in epithelial-mesenchymal transition ( 7 ). Recent investigations reveal that elevated TM9SF1 levels enhance bladder cancer cell proliferation, migration, and invasiveness, whereas suppressing TM9SF1 curbs these malignant behaviors ( 8 ). Genome-wide microarray analyses of bladder cancer tissues have consistently identified TM9SF1 as a differentially expressed gene, underscoring its potential as a key player in oncogenesis. These findings emphasize the importance of delving deeper into TM9SF1's mechanisms, particularly its impact on bladder cancer ( 9 ). In esophageal squamous cell carcinoma, TM9SF1 has been recognized as one of two critical marker genes tied to patients' 4-year overall survival rates, forming the basis of a prognostic nomogram( 10 ). Additionally, TM9SF1 mRNA serves as a target for PCIF1—the first known m6Am methyltransferase—and acts as a tumor suppressor in gastric cancer ( 11 ). TM9SF1 is also implicated in cervical cancer, where its oncogenic activity correlates with poorer clinical outcomes ( 12 ). These diverse roles highlight TM9SF1's multifaceted influence across various cancer types. While TM9SF1's connection to cancer has been largely overlooked in previous studies, our research sought to fill this gap by conducting a comprehensive, multi-omics investigation across various tumor types. Leveraging pan-cancer data, we meticulously examined TM9SF1's behavior—from its expression profiles and survival implications to genetic mutations, epigenetic modifications, and involvement in key biological pathways. We also explored its influence on tumor immunology, response to immunotherapy, cancer cell heterogeneity, and stem-like properties. For hepatocellular carcinoma specifically, we developed a predictive nomogram based on TM9SF1 expression and evaluated its relationship with drug responsiveness. This wide-ranging, pan-cancer perspective has yielded novel insights, suggesting TM9SF1 could serve as both a reliable diagnostic/prognostic indicator and a potential therapeutic bullseye. Our work significantly advances the understanding of TM9SF1's clinical utility in oncology. Materials and methods Examining TM9SF1 Expression Patterns Across Tissues To investigate TM9SF1 expression in both healthy and cancerous tissues, we compiled data from several authoritative genomic databases. Normal tissue expression profiles were extracted from the GTEx database ( 13 ). while cancer cell line data came from the CCLE repository( 14 ). For a broader comparison across malignancies, we accessed TCGA, which provided expression data for 33 tumor types alongside matched normal tissues. Due to limited normal tissue samples in TCGA, we expanded our analysis using the standardized PANCAN dataset from UCSC ( https://xenabrowser.net/ ) ( 15 ). This comprehensive approach ensured robust comparisons between normal and diseased states. Cases with less than three tumor or control samples were omitted from the study. We investigate the protein levels of TM9SF1 by UALCAN database ( https://ualcan.path.uab.edu/index.html ) ( 16 ). To enhance our study, we sourced immunohistochemically stained tissue section images from the Human Protein Atlas database ( https://www.proteinatlas.org/ ), which illustrate TM9SF1 expression patterns in various cancers. This publicly available resource provided valuable visual data to support our findings ( 17 ), specifically utilizing the HPA059249 antibody. Diagnostic and prognostic significance of TM9SF1 and its clinical correlation analysis To gauge the diagnostic prowess of TM9SF1 across various cancers, we pieced together ROC curves from a blend of cohorts sourced from the TCGA dataset, setting our sights on an AUC threshold of over 0.7. We integrated TM9SF1 expression profiles with critical prognostic variables and performed a Cox proportional hazards analysis using the coxph function in the "survival" package. We also turned to ROC curves to suss out TM9SF1's knack for forecasting 1-, 3-, and 5-year survival rates. The timeROC package [0.4] was our sidekick in crunching the numbers, while ggplot2 [3.3.6] handled the visual presentation.( 18 ) We also delved into the relationship between TM9SF1 expression and a host of clinicopathological factors. We cherry-picked the right statistical tools based on our data's quirks, using the stats [4.2.1] and car [3.1-0] packages. In the LIHC cohort, we stratified our prognostication based on clinical factors like albumin levels, gender, lymph node status, BMI, weight, and tumor status to see how TM9SF1 played into LIHC's prognosis. We zeroed in on the ideal cut-off for categorization using the surv_cutpoint function from the survminer package [0.4.9]. Next, we checked the proportional hazards assumption and fitted our survival regression model with the survival package [3.3.1], visualizing the results with the survminer and ggplot2 packages [3.3.6]. Mutation and methylation status of TM9SF1 Using the cBioPortal database ( 19 ), we investigated mutations in the TM9SF1 gene to assess the prevalence and spectrum of genomic alterations across various cancers. A comparative analysis was conducted to evaluate mutation frequencies between different tumor types. We examined the association between TM9SF1 expression levels and copy-number variations, applying Spearman and Pearson correlation tests for statistical validation. For structural context, we retrieved a 3D protein model (UniProt ID: O15321) from UniProt ( https://www.uniprot.org/ ) ( 20 ). We also identified the top ten genes with the highest mutation rates in samples harboring TM9SF1 mutations compared to wildtype cases. To explore epigenetic regulation, we leveraged the SMART database ( http://www.bioinfo-zs.com/smartapp/ ) to analyze TM9SF1 methylation profiles. ( 21 ). For a comprehensive genomic and epigenomic evaluation, we utilized GSCALite ( http://bioinfo.life.hust.edu.cn/web/GSCALite/)(22) . Functional Enrichment and Protein-Protein Interaction Network Analysis of TM9SF1 Across Multiple Cancers To investigate TM9SF1's protein-protein interactions (PPI), we employed the STRING database ( 23 ), For identifying genes associated with TM9SF1, we used the GEPIA2 platform. ( 24 ). To assess TM9SF1’s role in tumor cell functionality, we used the CancerSEA database ( http://biocc.hrbmu.edu.cn/CancerSEA/ ) to analyze its association with 14 distinct functional states across various cancers ( 25 ). In-Depth Analysis of Immune Landscape, Tumor Diversity, and Stemness Linked to TM9SF1 To elucidate the intricate relationship between the immune microenvironment and TM9SF1 across diverse tumor types, we performed a multifaceted assessment of immune-related metrics, including immune cell infiltration patterns, immunomodulatory factors, and tumor immunophenotypic characteristics. Leveraging the "ESTIMATE" package, we calculated immune, stromal, and composite scores. For a granular examination of TM9SF1's association with distinct immune cell subsets, utilizing the TIMER2.0 framework, we integrated outputs from multiple algorithms (including TIMER, CIBERSORT, quanTIseq, xCell, MCP-counter, and EPIC) to assess immune cell infiltration levels. We further explored TM9SF1's interplay with key immune components—such as tumor-infiltrating lymphocytes, immunoregulatory molecules, MHC proteins, chemokines, and their receptors—using TIMER2.0. These analyses provided critical insights into how TM9SF1 influences immune dynamics. To evaluate genomic instability and clonal diversity, we calculated tumor mutational burden (TMB) and mutant-allele tumor heterogeneity (MATH) scores via the "maftools" package. Additional heterogeneity indices were sourced from established studies ( 26 , 27 ). Stemness properties were assessed using RNA- and DNA-based metrics (RNAss, EREG.EXPss, DNAss, DMPss, ENHss, and EREG-METHss) derived from prior methylation and expression analyses ( 28 ). TM9SF1's Impact on Immunotherapy Groups TIDE is an open-access resource for analyzing tumor immune evasion via genomic expression analysis ( 29 , 30 ). In our study, we assessed the biomarker potential of TM9SF1 alongside other well-characterized markers to determine its predictive value for patient responses to ICB therapy. We also leveraged TIDE to explore how TM9SF1 influences T cell dysfunction and its broader implications for immunotherapy efficacy across multiple cohorts. These findings shed light on TM9SF1’s regulatory role in immune evasion and its clinical relevance in treatment outcomes. Our analysis compared TM9SF1 expression levels between responders and non-responders before and after ICB administration. We also examined changes in TM9SF1 expression following cytokine treatment in various cell lines. To further investigate genotype-immunophenotype associations, we turned to the TCIA database ( https://tcia.at/ ), analyzing the link between TM9SF1 expression and IPS as a predictor of ICB therapy response( 31 ). Investigating Potential Therapeutic Agents Targeting TM9SF1 in Hepatocellular Carcinoma Drug sensitivity data was compiled from three major pharmacogenomic databases: GDSC, CTRP, and the PRISM Repurposing dataset ( 32 – 34 ). To assess the relationship between TM9SF1 expression levels and drug efficacy (measured by IC50 values), we performed a comprehensive analysis across ten independent HCC patient cohorts. Our approach identified compounds showing significant positive or negative correlations with TM9SF1 expression, with results visualized through detailed heatmap representations. Single-cell sequencing analysis To investigate the role of TM9SF1 in HCC at single-cell resolution, we analyzed data from the GSE235057 database. Single-cell RNA sequencing samples from liver cancer were processed using the R package *Seurat*. PCA was conducted via the *RunPCA* function, followed by the construction of a K-nearest neighbor model using “FindNeighbors”. Cell clusters exhibiting the most pronounced gene expression changes were integrated using “FindClusters”. Malignant aneuploid cells were identified and annotated with the “Copykat” R package, while non-malignant cell populations were characterized using “scCATCH”. Finally, DEGs among microenvironmental cell types were pinpointed through the “FindMarkers” function. Statistical analysis The gene expression data underwent normalization via log2 transformation. To assess differences between normal and cancerous tissues, t-tests were employed, while survival outcomes were evaluated using Kaplan-Meier curves, Cox proportional hazards models, and log-rank tests. For correlation analysis with a p-value threshold of less than 0.05 considered statistically significant. All statistical computations were executed in R (Version 4.2.1). Results TM9SF1 expression levels across various cancers Our research delved into the mRNA and protein levels of TM9SF1 across various cancers, aiming to uncover its involvement in the development of cancer. According to the GTEx database, we found that TM9SF1 levels are sky-high in the fallopian tube but barely detectable in most normal tissues, particularly in blood ( Fig. 2 a ) . The CCLE database backed this up, revealing that TM9SF1 is present in high concentrations in a variety of cancer cell lines, and these levels remain steady across different lines ( Fig. 2 b ) . The TCGA database's comparative study of normal and tumor tissues further illuminated that TM9SF1 is boosted in 15 types of cancer, such as BLCA, BRCA, and GBM, while it's downregulated in only two types, KICH and THCA ( Fig. 2 c ) . When we merged data from the TCGA and GTEx, we noted a significant increase in TM9SF1 levels in 21 cancers, including BLCA, BRCA, GBM, and LUAD, with a notable drop in TGCT ( Fig. 2 d ) . To probe deeper into the protein's levels, we accessed immunohistochemical images from the HPA database, which depicted the TM9SF1 protein's expression patterns across different cancers (Figs. 1a) . In normal tissues, TM9SF1 protein levels are usually up there, with exceptions in areas like the caudate nucleus, lung, and oral mucosa (Figs. 1b) . The protein is abundant in most cancers, with the exception of lymphoma (Figs. 1c) . Furthermore, analysis of the CPTAC database revealed that TM9SF1 protein levels were significantly higher in cancerous tissues—particularly in colon, HNSC, GBM, and lung cancers—compared to normal samples. Conversely, BRCA, ccRCC, and PAAD exhibited notably reduced TM9SF1 expression. ( Fig. 2 e ) . Pancancer diagnostic and prognostic value of TM9SF1 To assess the diagnostic potential of TM9SF1 across various cancers, we analyzed ROC curves to determine AUC values for differentiating between healthy and malignant tissue samples. The biomarker showed remarkable predictive accuracy (AUC > 0.7) in identifying at least 19 cancer types, including cholangiocarcinoma (0.990), glioblastoma (0.937), and sarcoma (0.918). Other notable malignancies where TM9SF1 demonstrated strong diagnostic capability included esophageal squamous cell carcinoma (0.902), stomach adenocarcinoma (0.901), and liver hepatocellular carcinoma (0.900). The results reveal particularly impressive performance in esophageal adenocarcinoma (0.874), colorectal cancer (0.821), and head and neck squamous cell carcinoma (0.820). Additional cancers with significant AUC values ranged from oral squamous cell carcinoma (0.819) to breast invasive carcinoma (0.707), collectively underscoring TM9SF1's substantial clinical utility as a diagnostic biomarker. ( Fig. 3 a ) . After extracting survival data from the TCGA database, we conducted univariate Cox regression and Kaplan-Meier analyses to assess the prognostic significance of TM9SF1. The overall survival findings revealed a dual role for TM9SF1—acting as a risk factor in cancers such as GBMLGG, LGG, CESC, LUSC, UVM, and BLCA, while surprisingly functioning as a protective factor in patients with KIRC. ( Fig.s 2 a ). The Kaplan-Meier survival curves revealed consistent overall survival (OS) outcomes, with TM9SF1 emerging as a significant prognostic indicator. This biomarker was associated with poorer outcomes in ACC, BLCA, CESC, GBMLGG, KICH, LGG, LUAD, LUSC, READ, THCA, UVM, and OV cases. Conversely, it appeared to confer a protective effect in CHOL, GBM, KIPAN, KIRC, MESO, PCPG, and PRAD patient cohorts. The data clearly demonstrates TM9SF1's dual role as both a risk factor and a potential safeguard across different cancer types ( Fig.s 3 a ) . Additionally, TM9SF1 demonstrated a negative correlation with DSS in GBMLGG, LGG, CESC, LUSC, UVM, and BLCA, but had a positive association in KIPAN and KIRC ( Fig.s 2 b ). KM analyses confirmed these findings. Furthermore, TM9SF1 serves as a risk factor in ACC, BLCA, CESC, GBM, GBMLGG, KICH, LGG, LUSC, PCPG THCA and UVM, while acting as a protective factor in CHOL, COAD, COADREAD, KIPAN and KIRC, our analysis revealed a strong association between TM9SF1 expression levels and clinical outcomes across multiple cancer types. Elevated TM9SF1 expression consistently correlated with improved DFI in several malignancies, including ACC, CESC, CHOL, KIPAN, KIRC, KIRP, LIHC, and PCPG (Fig. s3b) . However, this trend reversed in LUAD cases, where higher TM9SF1 levels predicted poorer DFI outcomes (Fig. s4a) . Kaplan-Meier survival analyses further demonstrated that low TM9SF1 expression served as an unfavorable prognostic marker in ACC and CESC (Fig. s2c) . Analysis of progression-free survival (PFI) revealed that TM9SF1 served as an unfavorable biomarker across multiple cancer types—including ACC, GBMLGG, LGG, CESC, LUSC, UVM, and BLCA—yet curiously demonstrated a protective effect in both KIPAN and KIRC cohorts. This paradoxical duality highlights the context-dependent role of TM9SF1 in tumor progression (Fig. s2d) . Interestingly, TM9SF1 exhibited a dual role—acting as a risk factor in aggressive cancers like ACC, BLCA, CESC, GBM, GBMLGG, LGG, LIHC, LUSC, and UVM, while showing detrimental effects in KIPAN and KIRC (Fig. s4b) . The results underscore how TM9SF1's role in cancer progression and outcomes varies depending on biological context. To evaluate TM9SF1's predictive power, we conducted ROC curve analyses for 1-, 3-, and 5-year survival rates across multiple cancer types. The data revealed significant prognostic potential in ACC, CESC, COAD, COADREAD, GBM, GBMLGG, HNSC, KICH, LGG, LIHC, LUAD, LUADLUSC, OSCC, PAAD, PCPG, SARC, THCA, UCEC, UCS, UVM, and BLCA, reinforcing its clinical relevance in survival prediction (Fig. s5a) . Pancancer analysis of TM9SF1 expression and tumor immune infiltration We utilized the "ESTIMATE" algorithm to assess immune infiltration, stromal content, and composite tumor purity scores across various malignancies. Our pan-cancer investigation explored possible associations between these tumor microenvironment metrics and TM9SF1 gene expression patterns. The analysis demonstrated a robust direct correlation in gliomas (GBMLGG, LGG) and colorectal cancers (COAD, COADREAD, READ), where higher TM9SF1 levels coincided with elevated microenvironment scores. However, we identified an opposing trend in several other cancers - including cervical (CESC), prostate (PRAD), endometrial (UCEC), thyroid (THCA), skin (SKCM), and adrenal (ACC) tumors - where increased TM9SF1 expression paradoxically corresponded with lower microenvironment scores ( Fig. 4 a ) . To drive home this interconnection, we pinpointed the three tumors that showed the most significant link in terms of stromal or immune markers and depicted their relationship on scatter graphs. It's worth mentioning that TM9SF1 had a favorable connection with stromal grades in READ, TGCT, and COADREAD. In a similar vein, it demonstrated a favorable tie with immune scores in DLBC, READ, and UVM. ( Fig. 4 b, c ) . We leveraged the TIMER2.0 database along with multiple computational algorithms to investigate how TM9SF1 expression levels interact with immune cell infiltration. The resulting heatmap revealed a striking pattern: in the majority of tumors examined, TM9SF1 showed significant positive correlations with cancer-associated fibroblasts, neutrophils, endothelial cells, and macrophages. Notably, in UVM, TM9SF1 demonstrated particularly strong positive associations with nearly every type of immune cell analyzed. ( Fig. 4 d ) . Notably, neutrophil is significantly positively correlated with almost all cancers ( Fig. 4 e-n ) . The tumor microenvironment's cellular makeup plays a pivotal role in determining how effectively the immune system can combat cancer. Gaining insight into TM9SF1's impact on malignancies through modifications to the TME is essential. Researchers conducted a co-expression study examining TM9SF1 alongside genes associated with immune function. Interestingly, the findings revealed a striking positive association between TM9SF1 and MHC genes in uveal melanoma and low-grade gliomas, whereas thyroid carcinoma demonstrated a completely inverse relationship. ( Fig.s 6 a ) . TM9SF1 is positively correlated with chemokine receptors and immunosuppressive genes in most cancers, especially in UVM and DLBC ( Fig.s 6 b, c ) . Additionally, TM9SF1 is almost always positively correlated with chemokines in UVM and DLBC ( Fig.s 6 d ) . Besides, TM9SF1 is almost always positively correlated with immune activation genes ( Fig.s 6 e ) . TM9SF1 exhibits a robust positive link with numerous immune-related genes across UVM and DLBC, implying it may serve as a crucial immunotherapeutic focus for these malignancies. Assessment of Immunotherapies Based on TM9SF1 Expression In our thorough assessment, we looked at how TM9SF1 compared to other biomarkers in terms of their predictive prowess for treatment success and overall survival. Of the 25 studies we checked out, eight of them found that TM9SF1 had an AUC over 0.5—right up there with TMB and faring better than B. clonality and T. clonality ( Fig. 5 a ) . We also dove into how TM9SF1 interacts with immunotherapy. In some trials—like the ICB_Li’_PD1 Ipi_Naive, E-MTAB-179, and CAF FAP—we spotted a high TM9SF1 expression level through Cox-PH regression and T dysfunction measures in the immunotherapy set and immuno-suppressive cell type analyses. On the flip side, in other trials, such as ICB_Hugo2016_PD1 and Patel 2017, TM9SF1's presence was low based on T dysfunction readings and log2FC analysis in the CRISPR screen set ( Fig. 5 b ) . To understand TM9SF1's influence on immunotherapy's effectiveness, we turned to the TISMO database. TM9SF1's predictive powers were evident in one cohort using a living tumor model and another using lab-grown cell lines to test cytokine response ( Fig. 5 c & d) . What's more, we examined how TM9SF1 is tied to IPS scores across the board and found that in BRCA, KIRC, and LUAD, there was a definite negative link between TM9SF1 and the scores ( Fig. 5 e ) . Relationship between TM9SF1 expression and clinical features. To further investigate TM9SF1's involvement in cancer development, we analyzed its expression patterns across various clinical parameters using Wilcoxon rank sum testing. The data revealed age-dependent TM9SF1 upregulation in GBMLGG patients, contrasting with downregulation observed in BLCA, KIRP, and PAAD cohorts (Fig.s7a) . Gender-specific analysis showed heightened TM9SF1 levels in female KIRC, KIRP, and LUADLUSC patients versus male HNSC, MESO, and OSCC cases (Fig.s7b) . Notable variations emerged when examining T-stage classifications, with significant TM9SF1 expression differences detected in KIRC, MESO, and THCA (Fig.s7c) . Lymph node metastasis status revealed intriguing patterns: SKCM, TGCT, MESO, and THCA patients without nodal involvement showed elevated TM9SF1, while KIRP and OSCC displayed inverse correlations (Fig.s7d). Metastatic status comparisons demonstrated markedly higher TM9SF1 in CESC M1 versus M0 cases, contrasting with reduced expression in PRAD (Fig.s7e) . Tumor stage progression analyses uncovered increasing TM9SF1 levels in advanced KICH and LIHC, opposed by declining trends in KIRC, MESO, SKCM, and THCA (Fig.s7f) . Grading assessments showed TM9SF1 elevation correlating with higher tumor grades in GBMLGG, HNSC, LGG, LIHC, OSCC, and PAAD, while KIRC exhibited grade-dependent reduction (Fig.s7g) . Surprisingly, COAD, COADREAD, and READ samples demonstrated substantial TM9SF1 upregulation in cases presenting lymphatic metastasis or perineural invasion (Fig.s7h). Analysis of TM9SF1 Mutations and Methylation Patterns in Pan-Cancer Datasets Using the cBioPortal platform and TCGA pan-cancer datasets, we evaluated the mutation profile of TM9SF1. UCEC exhibited the highest mutation frequency at 4.95%. Missense mutations predominated across multiple tumor types, including UCEC, STAD, COADREAD, SKCM, HNSC, PAAD, CESC, KIRC, and GBM ( Fig. 6 a ) . Further classification revealed that SNPs and deletions were the primary mutation types, with SNPs being markedly more frequent. Notably, C > T transitions were the most common SNV, and TM9SF1 mutations were evenly distributed across samples ( Fig. 6 b ) . A pan-cancer analysis of TM9SF1 mutations highlighted missense mutations as the dominant alteration type. The 3D protein structure (upper right inset) and mutational mapping identified R230Q/* as the principal mutation site, localized within the EMP70 domain ( Fig. 6 c ) . CNV analysis revealed heterogeneous patterns, with hete.amp and hete.del being the most prevalent. READ, KIRC, CHOL, MESO, and UCS showed high frequencies of hete.del, while KICH, TGCT, HNSC, LUAD, SARC, and THYM were enriched for hete.amp. Correlation analysis showed a strong association between CNVs and TM9SF1 mRNA expression ( Fig. 6 d ) . Furthermore, CNVs significantly influenced survival outcomes in BLCA, KIRC, KIRP, LAML, LGG, MESO, and UCEC ( Fig. 6 e ) . A cross-cancer analysis of mutation burden and copy number alterations (CNAs) showed that uterine corpus endometrial carcinoma (UCEC) had the heaviest load of TM9SF1 mutations, driven primarily by missense mutations, structural variants, and gene amplifications. In contrast, adrenocortical carcinoma (ACC) displayed the lowest mutation frequency ( Fig. 6 f ) . Notably, TM9SF1 expression levels were strongly linked to increased CNAs, suggesting a clear dose-dependent relationship. ( Fig. 6 g ) . Elevated mRNA levels coincided with gene amplifications, whereas shallow and deep deletions corresponded to reduced expression ( Fig. 6 h ) . Additionally, the TM9SF1-altered cohort displayed higher mutation frequencies in CARMIL3, FITM1, IPO4, PCK2, PSME1, DCAF11, RNF31, ADCY4, TINF2, and TGM1 ( Fig. 6 i ) . Pan-cancer SNV analysis of these genes identified RNF31 (18%), ADCY4 (16%), IPO4 (14%), TGM1 (14%), and DCAF11 (11%) as the most frequently mutated, with missense mutations being the predominant type ( Fig. 6 j ) . Analysis of TM9SF1 methylation via the SMART database revealed elevated methylation in KIRC tumors but reduced levels in BLCA, CESC, CHOL, LIHC, LUAD, PRAD, READ, and UCEC (Fig. S8a) . Among 10 methylation sites assessed, cg14281756 and cg18432639 exhibited differential methylation across > 10 cancer types (Fig. S8b) . Promoter methylation analysis using UALCAN indicated higher TM9SF1 methylation in normal tissues of COAD, ESCA, LIHC, LUSC, PRAD, BLCA, and UCEC, whereas PAAD tumors showed elevated methylation. THCA and SARC displayed minimal methylation differences, suggesting limited involvement in these cancers (Fig. S9a) . A consistent inverse correlation between methylation and TM9SF1 mRNA expression was observed (Fig. S9b) . Clinically, methylation levels significantly impacted prognosis in THCA, HNSC, COAD, and THYM (Fig. S9c) . TM9SF1related networks and pathways To better understand TM9SF1's role in tumor development, we first identified its top 10 interacting proteins using the String database. The protein network analysis revealed strong connections between TM9SF1 and several key players, including VPS4A/B, SNF8, multiple CHMP family members, and other regulatory proteins (Fig. 7a) . Using the GEPIA2 platform, we then pinpointed the 100 genes most strongly associated with TM9SF1 across various cancers, with the top six showing particularly striking correlations (Fig. 7b) . A comprehensive heatmap analysis confirmed these relationships held true across all tumor types examined (Fig. 7c) . Diving deeper, we performed functional enrichment studies on 200 TM9SF1-linked genes from GEPIA2. The findings from the GO and KEGG pathway analyses strongly suggest that TM9SF1 is functionally involved in several key biological processes. These include regulating protein synthesis and intracellular transport, mediating cellular uptake pathways, influencing apoptosis, and participating in specialized metabolic functions such as N-Glycan and GPI-anchor biosynthesis. The data collectively indicate TM9SF1's multifaceted role in these critical cellular mechanisms. (Fig. 7d, e) . At the single-cell level, the CancerSEA database mining uncovered TM9SF1's intriguing functional profile. While it showed positive associations with processes like blood vessel formation, cellular specialization, inflammatory responses, and metastatic potential, it surprisingly demonstrated negative ties to DNA maintenance, cell division, and programmed cell death pathways. The exception to this pattern emerged in UVM, where TM9SF1 displayed nearly universal negative correlations with all functional states examined (Fig. 7f) . Figure 7. a . The TM9SF1 protein and its associated proteins were analyzed through the String platform to create a PPI network. b . The study identified the top six genes that show the strongest association with TM9SF1 across different types of cancer. c. A heatmap was generated to illustrate the connections between TM9SF1 and these six genes within a range of tumor types. d. GO analysis was conducted to delve into the functions of TM9SF1. e . KEGG analysis was performed to uncover the pathways in which TM9SF1 is involved. f. Using the CancerSEA database, we examined the functional states of TM9SF1 at the single-cell level across various cancers. The right panel showcases the ten functional states that are significantly linked to TM9SF1. *P < 0.05; **P < 0.01; ***P < 0.001. For a color guide to this figure's legend, please consult the online version of the article Association of TM9SF1 with Tumor Diversity, Stem Cell Traits, Mismatch Repair, and DNA Methylation To better understand TM9SF1's involvement in cancer development, we conducted a thorough analysis of its connections with tumor diversity, stem-like properties, DNA repair mechanisms, and epigenetic regulation. Tumor heterogeneity—a defining feature of aggressive cancers—reflects variations in proliferation rates, metastatic capacity, treatment sensitivity, and clinical outcomes, all driven by genetic instability. Our findings indicate that TM9SF1 generally exhibited an inverse relationship with these malignancy markers across most cancers, with TGCT showing particularly strong negative associations (Fig.s10a) . Interestingly, TM9SF1 displayed consistent positive relationships with both transcriptional and epigenetic stemness markers in the majority of malignancies (Fig.s10b) . Additionally, our investigation into TM9SF1's interaction with DNA repair and methylation systems uncovered robust positive correlations in numerous cancer types (Fig.s10c) . This pattern was especially pronounced in bladder, cervical, esophageal, head and neck, liver, pancreatic, and thyroid cancers, among others. The role of TM9SF1 in HCC To assess TM9SF1's clinical significance in diverse patient groups, we conducted a detailed subgroup analysis, categorizing participants by key criteria: serum albumin under 3.5 g/dL, a body mass index of 25 or lower, male sex, no evidence of tumor infiltration, body weight under 70 kg, and N0 lymph node status. In every single one of these subgroups, higher TM9SF1 expression reliably predicted worse clinical prognosis. The findings held true across the board—whether looking at nutritional status, body composition, or disease progression markers, increased TM9SF1 levels were invariably linked to poorer outcomes. This consistency suggests that TM9SF1 may serve as a robust biomarker, regardless of patient-specific variables. Essentially, no matter how we sliced the data, the same troubling pattern emerged. (Fig. 8a) . Subsequent univariate Cox regression analysis identified several factors influencing overall survival, with TM9SF1 expression emerging as a significant predictor of reduced survival, alongside pathological T stage, tumor status, metastatic involvement, and overall disease stage (Fig. 8b) . For additional verification of these results, a multivariate Cox regression analysis was conducted, reinforcing TM9SF1 as a standalone prognostic indicator for hepatocellular carcinoma (Fig. 8c) . To enhance clinical utility, we developed a prognostic nomogram that combines TM9SF1 expression levels with key clinical factors to predict survival probabilities at one, three, and five years for patients with hepatocellular carcinoma (Fig. 8d) . The accuracy of this model was supported by calibration plots, which showed strong alignment between predicted and actual survival outcomes (Fig. 8e) . To further explore TM9SF1's functional role in HCC, we conducted comprehensive pathway analyses using overrepresentation and gene set enrichment approaches. Our findings demonstrated that TM9SF1 predominantly participates in biosynthetic and metabolic regulation, along with molecular signaling cascades (Fig. 11a) . KEGG pathway mapping uncovered TM9SF1's involvement in multiple critical pathways, spanning from metabolic regulation and biosynthesis to oncogenic processes, metabolic disorders, and immune system modulation (Fig. 11b) . Gene set enrichment analysis across GO, KEGG, and HALLMARK databases yielded consistent results, showing TM9SF1's strong positive correlation with protein synthesis, intracellular transport, and post-translational modifications, while exhibiting inverse relationships with immune-related pathways (Fig. 11c) . Further KEGG examination reinforced TM9SF1's significant ties to cell cycle regulation, metabolic reprogramming, tumor initiation, and HCC progression (Fig. 11d) . Supporting these observations, hallmark pathway analysis confirmed TM9SF1's robust association with metabolic activity and cell cycle progression in HCC (Fig. 11e) . To explore how TM9SF1 expression influences drug resistance in HCC, we conducted correlation studies by cross-referencing TM9SF1 levels with three drug-gene interaction databases across ten distinct HCC patient cohorts. Our findings revealed a significant association between elevated TM9SF1 expression and reduced sensitivity to certain therapeutics, including Bosutinib, Dasatinib, JQ-1, and Crizotinib, suggesting a potential role in conferring drug resistance (Figs. 11f) . Figure 8. The relationship between TM9SF1 and clinical features in HCC. a . Prognostic stratification analysis based on Albumin, BMI, Gender, tumor stage, Weight and N0. b. Univariate Cox regression identified TM9SF1 as the independent risk factor. c. Multivariate Cox regression identified TM9SF1 as the independent risk factor. d. Construction of a nomogram to predict 1-, 3-, and 5-year survival probability. e . The calibration curves of the nomogram at 1-, 3-, and 5-year. *P < 0.05; **P < 0.01; ***P < 0.001. For a color guide to this figure's legend, please consult the online version of the article. Molecular features of TM9SF1 at the single-cell level in HCC. In addition to analyzing TM9SF1 at the molecular level, we conducted cellular localization studies of TM9SF1 using the GSE235057 dataset. The Copykat package was used to distinguish between aneuploid and diploid cells, with aneuploid cells being recognized as neoplastic cells (Fig. 9 a). Fifteen cell types were identified, including B cells, endothelial cells, CD8 T cells, plasma cells, gamma delta T cells, Treg cells, CD4 T cells, macrophage cells, MAIT cells, NKT cells, Tex cells, dendritic cells, fibroblast cells, monocytes, and hepatocyte cells (Fig. 9 b). Then we analyzed the expression characteristics of TM9SF1 in 15 cell types (Fig. 9 c, d). TM9SF1 expression level in different cell types was visualized, which verified that TM9SF1 highly correlated with Tex cells and plasma cells (Fig. 9 e ) . The DEGs across the 15 cell types were shown (Fig. 9 f ) . The t-SNE plot illustrated the dimensionality reduction of regulon modules (Fig. 9 g ) . We further analyzed the single-cell pseudotime trajectory of Tex cells in liver cancer. Utilizing monocle, we explored the distribution of CD4 T cells, CD8 T cells, and Tex cells, and it is evident that CD4 T cells and CD8 T cells are developing into Tex cells. As pseudotime increased, cells would progressively differentiate from cell state 1 to state 5. When CD4 T cells and CD8 T cells undergo exhaustion and transition into Tex cells, the expression level of TM9SF1 increases (Fig. 9 h ) . The genes that were downregulated and upregulated as pseudotime increased are displayed ( Fig.s13b) . GSVA analysis showed that the high-TM9SF1 and low-TM9SF1 groups played important roles in different biological processes. Tex cells with elevated TM9SF1 expression demonstrated enhanced activation of Granulocyte Colony-Stimulating Factor Production, Positive Regulation of Chemokine Production, Vascular Endothelial Growth Factor Receptor Activity and so on ( Fig.s13a) We used the iTalk R package to plot the ligand-receptor pairs of genes such as growth factors, checkpoint genes, and cytokines in the high TM9SF1 subgroup, creating a cell communication circle and network. We observed a strong interaction network between cells through various types of immune checkpoints, growth factors, cytokines, and other factors ( Fig.s12a-d) . Then, we performed cell communication analysis to study the interactions between TEX cells with different TM9SF1 expression levels and other cells. The roles of the 16 identified cell types in cellular communication were grouped into four main categories: sender, receiver, influencer, and mediator. Using the “CellChat” R package, we further categorized the interaction patterns of receiver and sender cell types into distinct types based on the expression of TM9SF1 in Tex cell ( Fig.s13c, d) . Tex cells with different levels of TM9SF1 form complex communication networks with other cell populations, suggesting that TM9SF1 plays an important role in liver cancer tumor immune microenvironment, particularly in Tex cells ( Fig.s13e-h) . Next, the connection between TM9SF1 expression and specific signaling pathways was explored in more detail. Notably, Tex cells with elevated TM9SF1 levels had significant interactions with other cells, primarily through the CD99, CypA, MHC-Ⅰ and SIRP pathways ( Fig.s14) . In contrast, Tex cells with low TM9SF1 levels engaged strongly with other cells via the CLEC, BAG, LCK and PARs pathways ( Fig.s15) . Discussion Currently, research on TM9SF1 remains quite limited. Studies have shown that TM9SF1 is associated with Fuchs endothelial corneal dystrophy and endoepithelial corneal dystrophy ( 35 , 36 ). TM9SF1 is highly expressed in the vessel walls of ruptured intracranial aneurysms, suggesting its potential involvement in inflammatory responses and vascular wall protein degradation during the formation and rupture of intracranial aneurysms ( 37 ). As TM9SF1 expression levels increase, both the severity of ARDS and patient mortality rates rise, this suggests that TM9SF1 is closely associated with the disease state and clinical prognosis of ARDS ( 38 ). Moreover, TM9SF1 plays a key role in autophagy and intracellular transport ( 39 , 40 ). Although some studies have suggested a link between TM9SF1 and cancer, its role in cancer has not been widely explored. Therefore, it is crucial to explore the role of TM9SF1 in tumor progression through integrated multi-omics pan-cancer analysis. This groundbreaking study represents the first comprehensive investigation into the oncogenic potential of TM9SF1 across diverse cancer types through advanced bioinformatics methodologies. Leveraging cutting-edge analytical techniques—including Kaplan-Meier survival analysis, ROC curve evaluation, and multi-platform data integration (TCGA, GTEx, CCLE, CPTAC, and HPA)—we mapped TM9SF1 expression profiles and assessed their clinical relevance. Our findings demonstrate that TM9SF1 is markedly upregulated in most malignancies relative to normal tissue counterparts. Protein-level analysis via CPTAC revealed elevated TM9SF1 expression in colon cancer, HNSC, GBM, UCEC, LIHC, and lung cancer, while showing reduced levels in BRCA, ccRCC, and PAAD. Intriguingly, HPA data indicated consistently high TM9SF1 presence across both normal and neoplastic tissues, suggesting context-dependent functionality. The observed variations in TM9SF1 expression patterns across tumor types underscore its multifaceted role in cancer pathogenesis. Diagnostically, ROC analysis established TM9SF1 as a highly reliable biomarker for numerous cancers, including CHOL, GBM, SARC, ESCC, STAD, LIHC, and others. Prognostically, elevated TM9SF1 expression correlated with adverse outcomes in most cancers, though paradoxically, it emerged as a favorable indicator in CHOL, COAD, COADREAD, KIRC, and KIPAN. These dualistic findings position TM9SF1 as a compelling, albeit complex, biomarker with significant potential for clinical application in cancer diagnostics and prognostication. TM9SF1 genetic alterations were detected in 23 of the 32 cancer types examined, with these changes manifesting as various genomic modifications such as point mutations, copy number amplifications, and complete gene deletions. Among these alterations, simple mutations emerged as the most common variant, appearing in 19 different cancers. Interestingly, gene amplifications showed a more restricted pattern, occurring exclusively in COADREAD, PAAD, CESC, KIRC, and GBM. Existing literature confirms that mutations in oncogenes frequently occur in solid human tumors and contribute significantly to cancer advancement ( 41 ). The regulation of gene activity through DNA methylation—where increased promoter methylation typically suppresses gene expression while reduced methylation enhances it—has been well documented ( 42 , 43 ). Our investigation uncovered notable variations in TM9SF1-related methylation patterns across different malignancies. These results highlight the critical need for deeper exploration of DNA methylation patterns in subsequent research to elucidate their precise involvement in cancer development and progression.. In this research, we investigated the intricate biological network and signaling mechanisms associated with TM9SF1 through GO and KEGG analyses. Our findings demonstrated that TM9SF1 is notably enriched across a spectrum of pivotal biological processes and signaling pathways, hinting at its indispensable function in the synthesis of life, the movement of molecules, the recycling of cellular components, and the birth of tumors. The GO analysis illuminated TM9SF1's abundance in pathways pertinent to the making and moving of substances, such as the construction of proteins, the conveyance of vesicles, and the process of self-eating, known as autophagy. Furthermore, the KEGG pathway analysis revealed TM9SF1's participation in a host of critical signaling tracks, including protein modification, the intake of molecules, programmed cell death, and the assembly of life-sustaining molecules. These pathways are fundamental to how cells divide, change, and survive. The data suggest that TM9SF1 could be a key player in the synthesis, movement, and recycling of cellular components. In recent years, research into other genes of the transmembrane 9 superfamily has increased, gradually revealing their functions. TM9SF2 was involved in the regulation of adult-repopulating HSCs and showed significantly altered expression during the development of the AGM region, which might be related to embryonic development ( 44 ). Studies have shown that N-sulfate in HS is essential for CHIKV infection of HAP1 cells, with the NDST1 enzyme catalyzing the N-sulfation of HS. TM9SF2 regulated the localization and stability of NDST1, promoting the N-sulfation of HS, thereby facilitating CHIKV infection of host cells. In addition, TM9SF2 played a critical role in tumor development ( 45 ). Clark et al. identified TM9SF2 as a novel colorectal oncogene transposon mutagenesis screening in mice. High TM9SF2 expression was associated with tumor staging, while low expression correlated with recurrence-free survival. Knocking out TM9SF2 significantly reduced tumor growth ( 46 ). LINC01232 enhanced the stability of TM9SF2 mRNA by recruiting EIF4A3, thereby upregulating TM9SF2 expression and promoting pancreatic cancer progression ( 47 ). TM9SF3 was highly expressed in various tumor tissues and played a pro-tumor role. Its expression level correlated with gastric cancer invasion depth, tumor staging, and undifferentiated gastric cancer, strongly associating it with poor prognosis. Temporary knockdown of TM9SF3 reduced tumor cell invasion ( 48 ). TM9SF3 was also highly expressed in T-cell leukemia cells, and knocking down TM9SF3 inhibited the proliferation and metastasis of human T-cell leukemia cells, suggesting that TM9SF3 could been a potential molecular target for cancer therapies ( 49 ). TM9SF4 played an important role in both immunity and cancer. It was essential for the innate immune response of Drosophila through its role in cell adhesion and phagocytosis ( 50 ). TM9SF4 was highly expressed in metastatic malignant melanoma and positively correlated with tumor malignancy, silencing TM9SF4 significantly inhibited metastasis ( 51 ). TM9SF4 also reduced endoplasmic reticulum stress, protecting drug-resistant breast cancer cells from apoptosis and necrotic cell death, while its knockdown inhibited cell growth and induced cell death ( 52 ). The tumor microenvironment consists of various cell types, including immune cells, stromal cells, cancer-associated fibroblasts, and endothelial cells, which form a crucial part of the tumor. Increasing evidence suggests that the TME significantly influences therapeutic responses and clinical outcomes ( 52 , 53 ). Immunological profiling indicates that TM9SF1 expression exhibits an inverse relationship with stromal and immune activity, as well as ESTIMATE scores, while also showing reduced infiltration of multiple immune cell subsets across different cancers. Among these immune players, NK T cells are frontline defenders in antitumor immunity, capable of directly targeting and eliminating malignant cells in early disease stages. Beyond their cytotoxic function, they modulate immune activity by releasing signaling molecules like cytokines, chemokines, and growth factors ( 54 ). Clinical data further support that robust NK T cell presence in solid tumors correlates with improved patient outcomes ( 55 ). Macrophages, however, display a dual nature in cancer progression. Initially, they act as tumor suppressors by engulfing cancerous cells or hindering their growth through cytotoxic mediators such as TNF-α, NO, and ROS( 56 ). Yet, as tumors evolve, these cells often undergo reprogramming into TAMs, shifting toward pro-tumorigenic behavior. Predominantly adopting an M2 phenotype, TAMs secrete factors like VEGF, EGF, and TGF-β, which drive angiogenesis, fuel tumor expansion, and enable metastatic spread ( 57 – 59 ). Endothelial cells, meanwhile, serve as a critical reservoir for CAFs, facilitating tumor metastasis. Their unchecked proliferation not only shelters malignant cells but also fosters their survival, accelerating disease progression ( 60 – 62 ). Additionally, TM9SF1 expression shows negative associations with multiple immune-modulating factors, chemokines, and receptors. Immune checkpoints are another linchpin in immune regulation. Elevated checkpoint expression in patients often signals heightened immune activation, including amplified T cell responses and more efficient tumor antigen presentation—key factors in mounting a potent antitumor defense. Our findings demonstrate a significant positive association between TM9SF1 expression and key tumor biomarkers including TMB, MSI, LOH, and HRD across multiple cancer types. As established biomarkers, both TMB and MSI play crucial roles in predicting immunotherapy outcomes ( 63 , 64 ). with TMB particularly serving as a reliable indicator of response to PD-1/PD-L1 blockade therapies. Interestingly, our data reveal that TM9SF1 exhibits comparable predictive power to TMB in forecasting immunotherapy responses. MSI, arising from impaired DNA mismatch repair in tumors, represents another clinically valuable marker. Meanwhile, HRD status serves as a pivotal factor in therapeutic decision-making and prognosis assessment, directly influencing sensitivity to platinum-based regimens and PARP inhibitors. Furthermore, our analysis uncovered a link between elevated TM9SF1 levels and increased tumor stemness. Current research indicates that while stemness correlates positively with tumor heterogeneity, it inversely relates to anti-tumor immune activity ( 65 ). The stemness burden has emerged as an important prognostic tool for solid tumors ( 66 ), where high stemness independently predicts recurrence risk. These observations suggest that TM9SF1 may modulate the tumor immune microenvironment and therapeutic response through its potential regulation of cancer stem cell populations. Conclusions This study dived headfirst into the burgeoning field of bioinformatics, introducing the role of TM9SF1 in tumor growth and spread. It highlighted the protein's crucial role in determining the health of a patient and its prognostic significance, suggesting that TM9SF1 could potentially become a valuable tumor biomarker. This discovery paves the way for future investigations into TM9SF1's role in cancer development. Limitations While this study provides the first comprehensive pan-cancer analysis of TM9SF1 using multi-omics and single-cell data, several limitations should be acknowledged to contextualize the findings and guide future research. Our analysis is based exclusively on data from public repositories such as TCGA, GTEx, and GEO, and is therefore subject to the inherent biases and limitations of these sources, including potential batch effects, variations in sample processing, and incomplete clinical annotations. Consequently, the findings presented are primarily correlational and cannot establish causality; for instance, while we demonstrate a strong association between high TM9SF1 expression and poor prognosis, our in-silico approach cannot prove that TM9SF1 is the direct driver of these phenomena. Furthermore, as this study is entirely computational, it lacks the in vitro and in vivo experimental validation necessary to confirm the proposed biological functions and signaling pathways. Similarly, the diagnostic and prognostic models developed, including the nomogram for HCC, require rigorous validation in independent, prospective clinical cohorts to establish their true clinical utility. Future laboratory and clinical studies are essential to verify our findings and elucidate the precise molecular mechanisms of TM9SF1 in cancer. Abbreviations KIRP Kidney renal papillary cell carcinoma EGF epidermal growth factor COADREAD Colon adenocarcinoma/rectum adenocarcinoma UCS Uterine carcinosarcoma PPI Protein–protein interaction THCA Thyroid carcinoma GBMLGG Glioma DFI Disease-free interval CAFs Cancer-associated fibroblasts MATH Mutational and clonal intratumoral heterogeneity ACC Adrenocortical cancer SNV Single nucleotide variants KIPAN Pan-kidney cohort (KICH + KIRC + KIRP) THYM Thymoma LAML Acute myeloid leukemia COAD Colon adenocarcinoma ICB Immune checkpoint blockade MSI Microsatellite instability CNA Copy number alteration ROC Receiver operating characteristic LUAD Lung adenocarcinoma NEO Neoantigen load OS Overall survival HRD Homologous recombination deficiency TILs Tumor-infiltrating lymphocytes HCC Hepatocellular carcinoma LGG Brain lower grade glioma KEGG Kyoto encyclopedia of genes and genomes OV Ovarian serous cystadenocarcinoma BRCA Breast cancer SARC Sarcoma PFI Progression-free interval KM Kaplan–Meier TNBC Triple-negative breast cancer MESO Mesothelioma MDSCs Myeloid-derived suppressor cells IPS Immune phenotype scores GSEA Gene set enrichment analysis DLBC Lymphoid neoplasm diffuse large B-cell lymphoma READ Rectum adenocarcinoma MAIT༚mucosa-associated invariant T TM9SF1 Transmembrane 9 superfamily member 1 TGF-β༚transforming growth factor-beta TME Tumor microenvironment HNSC Head and neck squamous cell carcinoma CHOL Cholangiocarcinoma SKCM Skin cutaneous melanoma AUC Curve area BLCA Bladder carcinoma STAD Stomach adenocarcinoma CNV Copy number variants KIRC Kidney renal clear cell carcinoma TMB Tumor mutational burden KICH Kidney chromophobe GO Gene ontology STES Stomach and esophageal carcinoma Tex༚exhausted T TAMs༚tumor-associated macrophages MF Molecular function VEGF༚vascular endothelial growth factor DSS Disease-specific survival LIHC Liver hepatocellular carcinoma PCPG Pheochromocytoma and paraganglioma BP Biological pathway CC Cellular component PRAD Prostate adenocarcinoma ESCA Esophageal carcinoma LOH Loss of heterozygosity GBM Glioblastoma multiforme LUSC Lung squamous cell carcinoma NSCLC Non-small cell lung cancer UCEC Uterine corpus endometrial carcinoma PAAD Pancreatic adenocarcinoma UVM Uveal melanoma IC50 Half-maximal inhibitory concentration TGCT Testicular germ cell tumors MMR Mismatch repair PCA principal component analysis CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma. Declarations Ethics statement Not applicable, the patient data used in this study were acquired from the publicly available databases. Conflict of interest The authors confirm no financial or commercial conflicts of interest influenced this research. Consent for publication Not applicable Funding Not applicable. Author Contribution FL and YC designed this study, collected the database, and took on the majority of the bioinformatics analysis. YL was responsible for single-cell analysis, visualization, and collecting related datasets. JH, BZ, SZ and YG were responsible for database collection and visualization. DC and ZY modified the manuscript. All of the authors reviewed and approved the final manuscript. Acknowledgement We sincerely acknowledge the contributions from the GTEx, TCGA and UCSC projects, which were invaluable for this study. 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Endothelial Cells in the Tumor Microenvironment. In: Birbrair A, editor. Tumor Microenvironment [Internet]. Cham: Springer International Publishing; 2020 [cited 2024 Aug 19]. pp. 71–86. (Advances in Experimental Medicine and Biology; vol. 1234). Available from: http://link.springer.com/ 10.1007/978-3-030-37184-5_6 Lim H, Moon A. Inflammatory fibroblasts in cancer. Arch Pharm Res. 2016;39(8):1021–31. Hida K, Maishi N, Annan D, Hida Y. Contribution of Tumor Endothelial Cells in Cancer Progression. Int J Mol Sci. 2018;19(5):1272. Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019;30(1):44–56. Imai K, Yamamoto H. Carcinogenesis and microsatellite instability: the interrelationship between genetics and epigenetics. Carcinogenesis. 2008;29(4):673–80. Miranda A, Hamilton PT, Zhang AW, Pattnaik S, Becht E, Mezheyeuski A, et al. Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proc Natl Acad Sci. 2019;116(18):9020–9. Huang T, Song X, Xu D, Tiek D, Goenka A, Wu B, et al. Stem cell programs in cancer initiation, progression, and therapy resistance. Theranostics. 2020;10(19):8721–43. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx 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. 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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-6992142","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495718206,"identity":"0452b682-51c1-46f1-a69e-4855ad0f453f","order_by":0,"name":"Fuxiang Luan","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Fuxiang","middleName":"","lastName":"Luan","suffix":""},{"id":495718207,"identity":"b351f672-d1bc-474e-aadf-88c3abee790e","order_by":1,"name":"Yuying Cui","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yuying","middleName":"","lastName":"Cui","suffix":""},{"id":495718208,"identity":"7319045a-ac40-4acd-aade-09c321e0d947","order_by":2,"name":"Yuxuan Li","email":"","orcid":"","institution":"Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Yuxuan","middleName":"","lastName":"Li","suffix":""},{"id":495718210,"identity":"d16ce60f-5d77-434f-a153-83216f6432eb","order_by":3,"name":"Jiahang Hu","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiahang","middleName":"","lastName":"Hu","suffix":""},{"id":495718212,"identity":"5ff2eac4-7faf-417f-9d0d-56d5239994ab","order_by":4,"name":"Shuwen Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shuwen","middleName":"","lastName":"Zhang","suffix":""},{"id":495718214,"identity":"bce37764-532b-4985-b186-6d7cad0cfc58","order_by":5,"name":"Boyi Zhang","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Boyi","middleName":"","lastName":"Zhang","suffix":""},{"id":495718216,"identity":"e923cb0f-2410-48b6-bb90-ca4ed2859c19","order_by":6,"name":"Yibing Guan","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yibing","middleName":"","lastName":"Guan","suffix":""},{"id":495718218,"identity":"327dc779-1485-45ad-9ec6-50c9ec29ff72","order_by":7,"name":"Dejun Cao","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dejun","middleName":"","lastName":"Cao","suffix":""},{"id":495718220,"identity":"a5e293f9-f5a9-43b1-8842-df1f13191316","order_by":8,"name":"Zhenbo Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYHACNiA+wMDPwJAA4jE2EK1FsoEhsYE0LQYHIKoJazG4kf7swccdd+SMzx94/piHwUZ2wwHmZw/wa0lIN5x55pmx2Y2ExGYehjTjDQfYzA0IaDkmzdt2OHHbDQaQlsOJGw7wsEng15LYJv0XqGVz/wGQlv/EaElmk2YEatnAAHbYAcJaJM88Y5PsbXtmLAH0y8w5BsnGMw+zmeHVwnc8/ZnEz7Y7cvz9ZxI+vKmwk+073vwMrxaFA3AmTwLQnUCaGZ96IJBvgDPZD+BUNQpGwSgYBSMbAACyN1GorIc/dAAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Zhenbo","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-06-27 13:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6992142/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6992142/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88526426,"identity":"5b057742-0d3a-4913-8a5b-cef4d4f90a4f","added_by":"auto","created_at":"2025-08-07 10:35:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":724824,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study design.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/6f8ac84107d082aa798f3aa7.png"},{"id":88526427,"identity":"c9099b6e-e682-4ce4-b0fe-1c1a7d2a0b74","added_by":"auto","created_at":"2025-08-07 10:35:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3757011,"visible":true,"origin":"","legend":"\u003cp\u003eTM9SF1 expression levels were examined in both normal and cancerous tissues. \u003cstrong\u003ea.\u003c/strong\u003e The GTEx database was utilized to analyze TM9SF1 expression in healthy tissues. \u003cstrong\u003eb.\u003c/strong\u003e The CCLE database was the source for TM9SF1 expression data in tumors. \u003cstrong\u003ec.\u003c/strong\u003e \u0026nbsp;Comparison of TM9SF1 mRNA levels was made between cancerous and healthy tissues, as recorded in the TCGA database. \u003cstrong\u003ed.\u003c/strong\u003e Difference in TM9SF1 mRNA expression between tumor tissues and normal tissues in the TCGA and GTEx databases. \u003cstrong\u003ee.\u003c/strong\u003e The UALCAN database was used to compare TM9SF1 protein levels in normal and cancerous tissues.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/9e82b25fed462c4f1602f716.png"},{"id":88526428,"identity":"fde0bf22-6f5c-4755-b560-2df91c90349e","added_by":"auto","created_at":"2025-08-07 10:35:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5046383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. The prognostic significance of TM9SF1.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/2ddeb788fb66c8be619c80bd.png"},{"id":88526430,"identity":"f6b1cd83-ff78-4509-a962-84ca95cc91fd","added_by":"auto","created_at":"2025-08-07 10:35:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15074946,"visible":true,"origin":"","legend":"\u003cp\u003eTM9SF1 is deeply tied to immune cell migration in several forms of cancer, as demonstrated by various analytical models. \u003cstrong\u003ea.\u003c/strong\u003e Insights into the interplay between TM9SF1 and scores for stroma, immunity, and the ESTIMATE model.\u003cstrong\u003e b.\u003c/strong\u003e Identification of the top three cancers exhibiting the strongest link between TM9SF1 and stromal scores. \u003cstrong\u003ec.\u003c/strong\u003e The top three cancers with the most pronounced association between TM9SF1 and immune scores.\u003cstrong\u003e d.\u003c/strong\u003e A heat map depicting the ties between TM9SF1 and various immune cells. \u003cstrong\u003ee-n.\u003c/strong\u003e Correlation studies between TM9SF1 and neutrophils across diverse cancers. *P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001. For a color guide to this figure's legend, please consult the online version of the article.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/2d92e3a40b12671055c4b16a.png"},{"id":88526429,"identity":"f985c3ec-f681-4025-b6f3-46a9fd3ed3a6","added_by":"auto","created_at":"2025-08-07 10:35:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7809470,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluating TM9SF1's Role in Immunotherapy Response. \u003cstrong\u003ea\u003c/strong\u003e. Assessing the biomarker significance of TM9SF1 in tumor immune evasion compared to established biomarkers across ICB therapy cohorts. \u003cstrong\u003eb\u003c/strong\u003e. Examining the relationship between TM9SF1 and T cell dysfunction, along with clinical outcomes following ICB treatment. \u003cstrong\u003ec\u003c/strong\u003e. In vivo investigation of TM9SF1's impact on immunotherapy response using murine models treated with ICB. \u003cstrong\u003ed\u003c/strong\u003e. In vitro analysis of TM9SF1's influence on immunotherapy sensitivity across various cell lines. \u003cstrong\u003ee\u003c/strong\u003e. Correlation analysis between TM9SF1 expression and IPS metrics utilizing data from the TCIA database. *P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001. For a color guide to this figure's legend, please consult the online version of the article.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/dae1e61b2343518ce7ebe47e.png"},{"id":88527726,"identity":"165bd968-49be-43de-9eb3-d87c8bcd4fe3","added_by":"auto","created_at":"2025-08-07 10:43:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":9228533,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of TM9SF1 Mutations Across Diverse Tumor Types. \u003cstrong\u003ea.\u003c/strong\u003ePrevalence of TM9SF1 genetic alterations in pan-cancer datasets. \u003cstrong\u003eb. \u003c/strong\u003eBreakdown of TM9SF1 variants by classification, mutation type, and SNV category. \u003cstrong\u003ec.\u003c/strong\u003e Schematic representation of TM9SF1’s 3D protein structure, highlighting key mutation sites. \u003cstrong\u003ed.\u003c/strong\u003e GSCALite-derived pie chart illustrating the distribution of TM9SF1 CNV types in pan-cancer, with purity-adjusted Spearman’s rho correlations (left). Light/dark red and green segments denote varying CNV associations. The accompanying dot plot (right) correlates TM9SF1 CNVs with mRNA expression in 16 cancers—dot size reflects CNV frequency, while color intensity (red-blue gradient) indicates correlation strength via Spearman analysis. \u003cstrong\u003ee.\u003c/strong\u003e Survival outcomes stratified by TM9SF1 CNV status in pan-cancer cohorts. \u003cstrong\u003ef.\u003c/strong\u003e Quantification of TM9SF1 mutation occurrences. \u003cstrong\u003eg.\u003c/strong\u003e Relationship between TM9SF1 expression levels and copy-number values. \u003cstrong\u003eh. \u003c/strong\u003eDifferential TM9SF1 expression patterns across distinct copy-number alterations. \u003cstrong\u003ei.\u003c/strong\u003e Top ten genes exhibiting significant mutation rate disparities between TM9SF1-altered and unaltered groups. \u003cstrong\u003ej.\u003c/strong\u003e Waterfall plot delineating TM9SF1 and associated gene mutation profiles by cancer type, with tumor mutation burden annotated (top). Cancer types (right) and mutation classifications (left) are labeled, while adjacent stacked bars depict SNV type frequencies (color-coded; see web version for color references). For detailed color interpretations, refer to the digital version of this article\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/5a9adadd9b9e342a65b67586.png"},{"id":88528107,"identity":"c5fe8938-687b-4658-8cae-4e5a01e01e91","added_by":"auto","created_at":"2025-08-07 10:51:01","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":9448841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e. The TM9SF1 protein and its associated proteins were analyzed through the String platform to create a PPI network. \u003cstrong\u003eb\u003c/strong\u003e. The study identified the top six genes that show the strongest association with TM9SF1 across different types of cancer. \u003cstrong\u003ec.\u003c/strong\u003e A heatmap was generated to illustrate the connections between TM9SF1 and these six genes within a range of tumor types. \u003cstrong\u003ed.\u003c/strong\u003e GO analysis was conducted to delve into the functions of TM9SF1. \u003cstrong\u003ee\u003c/strong\u003e. \u0026nbsp;KEGG analysis was performed to uncover the pathways in which TM9SF1 is involved. \u003cstrong\u003ef.\u003c/strong\u003e \u0026nbsp;Using the CancerSEA database, we examined the functional states of TM9SF1 at the single-cell level across various cancers. The right panel showcases the ten functional states that are significantly linked to TM9SF1. *P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001. For a color guide to this figure's legend, please consult the online version of the article\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/924f80ddf055960ec20782c4.png"},{"id":88529062,"identity":"0bd0bece-a7c8-486c-ba6c-ce6fb2e28062","added_by":"auto","created_at":"2025-08-07 10:59:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4291108,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between TM9SF1 and clinical features in HCC. \u003cstrong\u003ea\u003c/strong\u003e. Prognostic stratification analysis based on Albumin, BMI, Gender, tumor stage, Weightand N0. \u003cstrong\u003eb.\u003c/strong\u003e Univariate Cox regression identified TM9SF1 as the independent risk factor. \u003cstrong\u003ec.\u003c/strong\u003e Multivariate Cox regression identified TM9SF1 as the independent risk factor.\u003cstrong\u003e d.\u003c/strong\u003e Construction of a nomogram to predict 1-, 3-, and 5-year survival probability. \u003cstrong\u003ee\u003c/strong\u003e. The calibration curves of the nomogram at 1-, 3-, and 5-year. *P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001. For a color guide to this figure's legend, please consult the online version of the article.\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/106f6780bb0d2600d649cc25.png"},{"id":88527735,"identity":"5f374737-6f99-420d-8edd-e54e91cdebe7","added_by":"auto","created_at":"2025-08-07 10:43:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":26393617,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular features of TM9SF1 at the single-cell level. \u003cstrong\u003ea. \u003c/strong\u003et-SNE for the dimension reduction and visualization of aneuploid cells and diploid cells. \u003cstrong\u003eb.\u003c/strong\u003e 15 cell types within the tumor microenvironment. \u003cstrong\u003ec.\u003c/strong\u003e t-SNE for the dimension reduction and visualization of cells with high or low TM9SF1 expression. \u003cstrong\u003ed.\u003c/strong\u003e t-SNE plot showing the TM9SF1 expression in different cell clusters. \u003cstrong\u003ee. \u003c/strong\u003eThe expression level of TM9SF1 in 15 cell clusters. \u003cstrong\u003ef. \u003c/strong\u003eThe differentially expressed genes among the identified 15 cell types. \u003cstrong\u003eg. \u003c/strong\u003et- SNE plot for the dimension reduction of regulon modules. \u003cstrong\u003eh. \u003c/strong\u003ePseudotime trajectory analysis based on TM9SF1 expression.\u003c/p\u003e","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/4071dc3860c1b01ffd913264.png"},{"id":91617024,"identity":"55ac9c60-b209-4d79-969a-6fd902eb3ff0","added_by":"auto","created_at":"2025-09-18 10:48:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":74494922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/ccf8d571-09a9-4ce9-99eb-d0dfc9184f33.pdf"},{"id":88526487,"identity":"ef902766-bc60-405d-a3ed-dc10533d111d","added_by":"auto","created_at":"2025-08-07 10:35:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33908168,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6992142/v1/f533049bf5b292d716bcf755.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMulti-omics and single-cell analysis reveals TM9SF1 as a biomarker in pan-cancer diagnosis and prognosis, with a special focus on hepatocellular carcinoma\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTM9SF1, also termed MP70 and HMP70, is a key player in the TM9SF1 superfamily (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This family boasts nine transmembrane proteins that are found all over the place in human tissues, and are just as prevalent in yeast, plants, and mammals. Their existence is a testament to their high degree of conservation across various species. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The TM9SF protein family is a group known for its sizeable extracellular section and consists of nine transmembrane spans, which makes it rather distinct (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Yet, despite this, their actual roles in biological processes are still largely a mystery. To date, only a smattering of research suggests that these proteins' activity might correlate with cell sticking together and the development of tumors. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearch on TM9SF1 has historically been limited, but emerging evidence points to its significant role in tumor development and progression. TM9SF1 collaborates with EBAG9 to modulate prostate cancer cell migration by targeting genes involved in epithelial-mesenchymal transition (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Recent investigations reveal that elevated TM9SF1 levels enhance bladder cancer cell proliferation, migration, and invasiveness, whereas suppressing TM9SF1 curbs these malignant behaviors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Genome-wide microarray analyses of bladder cancer tissues have consistently identified TM9SF1 as a differentially expressed gene, underscoring its potential as a key player in oncogenesis. These findings emphasize the importance of delving deeper into TM9SF1's mechanisms, particularly its impact on bladder cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In esophageal squamous cell carcinoma, TM9SF1 has been recognized as one of two critical marker genes tied to patients' 4-year overall survival rates, forming the basis of a prognostic nomogram(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, TM9SF1 mRNA serves as a target for PCIF1\u0026mdash;the first known m6Am methyltransferase\u0026mdash;and acts as a tumor suppressor in gastric cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). TM9SF1 is also implicated in cervical cancer, where its oncogenic activity correlates with poorer clinical outcomes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These diverse roles highlight TM9SF1's multifaceted influence across various cancer types.\u003c/p\u003e\u003cp\u003eWhile TM9SF1's connection to cancer has been largely overlooked in previous studies, our research sought to fill this gap by conducting a comprehensive, multi-omics investigation across various tumor types. Leveraging pan-cancer data, we meticulously examined TM9SF1's behavior\u0026mdash;from its expression profiles and survival implications to genetic mutations, epigenetic modifications, and involvement in key biological pathways. We also explored its influence on tumor immunology, response to immunotherapy, cancer cell heterogeneity, and stem-like properties. For hepatocellular carcinoma specifically, we developed a predictive nomogram based on TM9SF1 expression and evaluated its relationship with drug responsiveness. This wide-ranging, pan-cancer perspective has yielded novel insights, suggesting TM9SF1 could serve as both a reliable diagnostic/prognostic indicator and a potential therapeutic bullseye. Our work significantly advances the understanding of TM9SF1's clinical utility in oncology.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eExamining TM9SF1 Expression Patterns Across Tissues\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate TM9SF1 expression in both healthy and cancerous tissues, we compiled data from several authoritative genomic databases. Normal tissue expression profiles were extracted from the GTEx database (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). while cancer cell line data came from the CCLE repository(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). For a broader comparison across malignancies, we accessed TCGA, which provided expression data for 33 tumor types alongside matched normal tissues. Due to limited normal tissue samples in TCGA, we expanded our analysis using the standardized PANCAN dataset from UCSC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This comprehensive approach ensured robust comparisons between normal and diseased states. Cases with less than three tumor or control samples were omitted from the study. We investigate the protein levels of TM9SF1 by UALCAN database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/index.html\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). To enhance our study, we sourced immunohistochemically stained tissue section images from the Human Protein Atlas database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which illustrate TM9SF1 expression patterns in various cancers. This publicly available resource provided valuable visual data to support our findings (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), specifically utilizing the HPA059249 antibody.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiagnostic and prognostic significance of TM9SF1 and its clinical correlation analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo gauge the diagnostic prowess of TM9SF1 across various cancers, we pieced together ROC curves from a blend of cohorts sourced from the TCGA dataset, setting our sights on an AUC threshold of over 0.7. We integrated TM9SF1 expression profiles with critical prognostic variables and performed a Cox proportional hazards analysis using the coxph function in the \"survival\" package. We also turned to ROC curves to suss out TM9SF1's knack for forecasting 1-, 3-, and 5-year survival rates. The timeROC package [0.4] was our sidekick in crunching the numbers, while ggplot2 [3.3.6] handled the visual presentation.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) We also delved into the relationship between TM9SF1 expression and a host of clinicopathological factors. We cherry-picked the right statistical tools based on our data's quirks, using the stats [4.2.1] and car [3.1-0] packages. In the LIHC cohort, we stratified our prognostication based on clinical factors like albumin levels, gender, lymph node status, BMI, weight, and tumor status to see how TM9SF1 played into LIHC's prognosis. We zeroed in on the ideal cut-off for categorization using the surv_cutpoint function from the survminer package [0.4.9]. Next, we checked the proportional hazards assumption and fitted our survival regression model with the survival package [3.3.1], visualizing the results with the survminer and ggplot2 packages [3.3.6].\u003c/p\u003e\u003cp\u003e\u003cb\u003eMutation and methylation status of TM9SF1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the cBioPortal database (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), we investigated mutations in the TM9SF1 gene to assess the prevalence and spectrum of genomic alterations across various cancers. A comparative analysis was conducted to evaluate mutation frequencies between different tumor types. We examined the association between TM9SF1 expression levels and copy-number variations, applying Spearman and Pearson correlation tests for statistical validation. For structural context, we retrieved a 3D protein model (UniProt ID: O15321) from UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). We also identified the top ten genes with the highest mutation rates in samples harboring TM9SF1 mutations compared to wildtype cases. To explore epigenetic regulation, we leveraged the SMART database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinfo-zs.com/smartapp/\u003c/span\u003e\u003cspan address=\"http://www.bioinfo-zs.com/smartapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to analyze TM9SF1 methylation profiles. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For a comprehensive genomic and epigenomic evaluation, we utilized GSCALite (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/web/GSCALite/)(22)\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/web/GSCALite/)(22)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Enrichment and Protein-Protein Interaction Network Analysis of TM9SF1 Across Multiple Cancers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate TM9SF1's protein-protein interactions (PPI), we employed the STRING database (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), For identifying genes associated with TM9SF1, we used the GEPIA2 platform. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). To assess TM9SF1\u0026rsquo;s role in tumor cell functionality, we used the 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) to analyze its association with 14 distinct functional states across various cancers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn-Depth Analysis of Immune Landscape, Tumor Diversity, and Stemness Linked to TM9SF1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo elucidate the intricate relationship between the immune microenvironment and TM9SF1 across diverse tumor types, we performed a multifaceted assessment of immune-related metrics, including immune cell infiltration patterns, immunomodulatory factors, and tumor immunophenotypic characteristics. Leveraging the \"ESTIMATE\" package, we calculated immune, stromal, and composite scores. For a granular examination of TM9SF1's association with distinct immune cell subsets, utilizing the TIMER2.0 framework, we integrated outputs from multiple algorithms (including TIMER, CIBERSORT, quanTIseq, xCell, MCP-counter, and EPIC) to assess immune cell infiltration levels. We further explored TM9SF1's interplay with key immune components\u0026mdash;such as tumor-infiltrating lymphocytes, immunoregulatory molecules, MHC proteins, chemokines, and their receptors\u0026mdash;using TIMER2.0. These analyses provided critical insights into how TM9SF1 influences immune dynamics. To evaluate genomic instability and clonal diversity, we calculated tumor mutational burden (TMB) and mutant-allele tumor heterogeneity (MATH) scores via the \"maftools\" package. Additional heterogeneity indices were sourced from established studies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Stemness properties were assessed using RNA- and DNA-based metrics (RNAss, EREG.EXPss, DNAss, DMPss, ENHss, and EREG-METHss) derived from prior methylation and expression analyses (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTM9SF1's Impact on Immunotherapy Groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTIDE is an open-access resource for analyzing tumor immune evasion via genomic expression analysis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In our study, we assessed the biomarker potential of TM9SF1 alongside other well-characterized markers to determine its predictive value for patient responses to ICB therapy. We also leveraged TIDE to explore how TM9SF1 influences T cell dysfunction and its broader implications for immunotherapy efficacy across multiple cohorts. These findings shed light on TM9SF1\u0026rsquo;s regulatory role in immune evasion and its clinical relevance in treatment outcomes. Our analysis compared TM9SF1 expression levels between responders and non-responders before and after ICB administration. We also examined changes in TM9SF1 expression following cytokine treatment in various cell lines. To further investigate genotype-immunophenotype associations, we turned to the TCIA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.at/\u003c/span\u003e\u003cspan address=\"https://tcia.at/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), analyzing the link between TM9SF1 expression and IPS as a predictor of ICB therapy response(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInvestigating Potential Therapeutic Agents Targeting TM9SF1 in Hepatocellular Carcinoma\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDrug sensitivity data was compiled from three major pharmacogenomic databases: GDSC, CTRP, and the PRISM Repurposing dataset (\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). To assess the relationship between TM9SF1 expression levels and drug efficacy (measured by IC50 values), we performed a comprehensive analysis across ten independent HCC patient cohorts. Our approach identified compounds showing significant positive or negative correlations with TM9SF1 expression, with results visualized through detailed heatmap representations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-cell sequencing analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the role of TM9SF1 in HCC at single-cell resolution, we analyzed data from the GSE235057 database. Single-cell RNA sequencing samples from liver cancer were processed using the R package *Seurat*. PCA was conducted via the *RunPCA* function, followed by the construction of a K-nearest neighbor model using \u0026ldquo;FindNeighbors\u0026rdquo;. Cell clusters exhibiting the most pronounced gene expression changes were integrated using \u0026ldquo;FindClusters\u0026rdquo;. Malignant aneuploid cells were identified and annotated with the \u0026ldquo;Copykat\u0026rdquo; R package, while non-malignant cell populations were characterized using \u0026ldquo;scCATCH\u0026rdquo;. Finally, DEGs among microenvironmental cell types were pinpointed through the \u0026ldquo;FindMarkers\u0026rdquo; function.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe gene expression data underwent normalization via log2 transformation. To assess differences between normal and cancerous tissues, t-tests were employed, while survival outcomes were evaluated using Kaplan-Meier curves, Cox proportional hazards models, and log-rank tests. For correlation analysis with a p-value threshold of less than 0.05 considered statistically significant. All statistical computations were executed in R (Version 4.2.1).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eTM9SF1 expression levels across various cancers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur research delved into the mRNA and protein levels of TM9SF1 across various cancers, aiming to uncover its involvement in the development of cancer. According to the GTEx database, we found that TM9SF1 levels are sky-high in the fallopian tube but barely detectable in most normal tissues, particularly in blood \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The CCLE database backed this up, revealing that TM9SF1 is present in high concentrations in a variety of cancer cell lines, and these levels remain steady across different lines \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. The TCGA database's comparative study of normal and tumor tissues further illuminated that TM9SF1 is boosted in 15 types of cancer, such as BLCA, BRCA, and GBM, while it's downregulated in only two types, KICH and THCA \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. When we merged data from the TCGA and GTEx, we noted a significant increase in TM9SF1 levels in 21 cancers, including BLCA, BRCA, GBM, and LUAD, with a notable drop in TGCT \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. To probe deeper into the protein's levels, we accessed immunohistochemical images from the HPA database, which depicted the TM9SF1 protein's expression patterns across different cancers \u003cb\u003e(Figs.\u0026nbsp;1a)\u003c/b\u003e. In normal tissues, TM9SF1 protein levels are usually up there, with exceptions in areas like the caudate nucleus, lung, and oral mucosa \u003cb\u003e(Figs.\u0026nbsp;1b)\u003c/b\u003e. The protein is abundant in most cancers, with the exception of lymphoma \u003cb\u003e(Figs.\u0026nbsp;1c)\u003c/b\u003e. Furthermore, analysis of the CPTAC database revealed that TM9SF1 protein levels were significantly higher in cancerous tissues\u0026mdash;particularly in colon, HNSC, GBM, and lung cancers\u0026mdash;compared to normal samples. Conversely, BRCA, ccRCC, and PAAD exhibited notably reduced TM9SF1 expression. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePancancer diagnostic and prognostic value of TM9SF1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess the diagnostic potential of TM9SF1 across various cancers, we analyzed ROC curves to determine AUC values for differentiating between healthy and malignant tissue samples. The biomarker showed remarkable predictive accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) in identifying at least 19 cancer types, including cholangiocarcinoma (0.990), glioblastoma (0.937), and sarcoma (0.918). Other notable malignancies where TM9SF1 demonstrated strong diagnostic capability included esophageal squamous cell carcinoma (0.902), stomach adenocarcinoma (0.901), and liver hepatocellular carcinoma (0.900). The results reveal particularly impressive performance in esophageal adenocarcinoma (0.874), colorectal cancer (0.821), and head and neck squamous cell carcinoma (0.820). Additional cancers with significant AUC values ranged from oral squamous cell carcinoma (0.819) to breast invasive carcinoma (0.707), collectively underscoring TM9SF1's substantial clinical utility as a diagnostic biomarker. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter extracting survival data from the TCGA database, we conducted univariate Cox regression and Kaplan-Meier analyses to assess the prognostic significance of TM9SF1. The overall survival findings revealed a dual role for TM9SF1\u0026mdash;acting as a risk factor in cancers such as GBMLGG, LGG, CESC, LUSC, UVM, and BLCA, while surprisingly functioning as a protective factor in patients with KIRC. \u003cb\u003e(\u003c/b\u003eFig.s\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e The Kaplan-Meier survival curves revealed consistent overall survival (OS) outcomes, with TM9SF1 emerging as a significant prognostic indicator. This biomarker was associated with poorer outcomes in ACC, BLCA, CESC, GBMLGG, KICH, LGG, LUAD, LUSC, READ, THCA, UVM, and OV cases. Conversely, it appeared to confer a protective effect in CHOL, GBM, KIPAN, KIRC, MESO, PCPG, and PRAD patient cohorts. The data clearly demonstrates TM9SF1's dual role as both a risk factor and a potential safeguard across different cancer types \u003cb\u003e(\u003c/b\u003eFig.s\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Additionally, TM9SF1 demonstrated a negative correlation with DSS in GBMLGG, LGG, CESC, LUSC, UVM, and BLCA, but had a positive association in KIPAN and KIRC \u003cb\u003e(\u003c/b\u003eFig.s\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb\u003cb\u003e).\u003c/b\u003e KM analyses confirmed these findings. Furthermore, TM9SF1 serves as a risk factor in ACC, BLCA, CESC, GBM, GBMLGG, KICH, LGG, LUSC, PCPG THCA and UVM, while acting as a protective factor in CHOL, COAD, COADREAD, KIPAN and KIRC, our analysis revealed a strong association between TM9SF1 expression levels and clinical outcomes across multiple cancer types. Elevated TM9SF1 expression consistently correlated with improved DFI in several malignancies, including ACC, CESC, CHOL, KIPAN, KIRC, KIRP, LIHC, and PCPG \u003cb\u003e(Fig. s3b)\u003c/b\u003e. However, this trend reversed in LUAD cases, where higher TM9SF1 levels predicted poorer DFI outcomes \u003cb\u003e(Fig. s4a)\u003c/b\u003e. Kaplan-Meier survival analyses further demonstrated that low TM9SF1 expression served as an unfavorable prognostic marker in ACC and CESC \u003cb\u003e(Fig. s2c)\u003c/b\u003e. Analysis of progression-free survival (PFI) revealed that TM9SF1 served as an unfavorable biomarker across multiple cancer types\u0026mdash;including ACC, GBMLGG, LGG, CESC, LUSC, UVM, and BLCA\u0026mdash;yet curiously demonstrated a protective effect in both KIPAN and KIRC cohorts. This paradoxical duality highlights the context-dependent role of TM9SF1 in tumor progression \u003cb\u003e(Fig. s2d)\u003c/b\u003e. Interestingly, TM9SF1 exhibited a dual role\u0026mdash;acting as a risk factor in aggressive cancers like ACC, BLCA, CESC, GBM, GBMLGG, LGG, LIHC, LUSC, and UVM, while showing detrimental effects in KIPAN and KIRC \u003cb\u003e(Fig. s4b)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe results underscore how TM9SF1's role in cancer progression and outcomes varies depending on biological context. To evaluate TM9SF1's predictive power, we conducted ROC curve analyses for 1-, 3-, and 5-year survival rates across multiple cancer types. The data revealed significant prognostic potential in ACC, CESC, COAD, COADREAD, GBM, GBMLGG, HNSC, KICH, LGG, LIHC, LUAD, LUADLUSC, OSCC, PAAD, PCPG, SARC, THCA, UCEC, UCS, UVM, and BLCA, reinforcing its clinical relevance in survival prediction \u003cb\u003e(Fig. s5a)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePancancer analysis of TM9SF1 expression and tumor immune infiltration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe utilized the \"ESTIMATE\" algorithm to assess immune infiltration, stromal content, and composite tumor purity scores across various malignancies. Our pan-cancer investigation explored possible associations between these tumor microenvironment metrics and TM9SF1 gene expression patterns. The analysis demonstrated a robust direct correlation in gliomas (GBMLGG, LGG) and colorectal cancers (COAD, COADREAD, READ), where higher TM9SF1 levels coincided with elevated microenvironment scores. However, we identified an opposing trend in several other cancers - including cervical (CESC), prostate (PRAD), endometrial (UCEC), thyroid (THCA), skin (SKCM), and adrenal (ACC) tumors - where increased TM9SF1 expression paradoxically corresponded with lower microenvironment scores \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. To drive home this interconnection, we pinpointed the three tumors that showed the most significant link in terms of stromal or immune markers and depicted their relationship on scatter graphs. It's worth mentioning that TM9SF1 had a favorable connection with stromal grades in READ, TGCT, and COADREAD. In a similar vein, it demonstrated a favorable tie with immune scores in DLBC, READ, and UVM. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c\u003cb\u003e)\u003c/b\u003e. We leveraged the TIMER2.0 database along with multiple computational algorithms to investigate how TM9SF1 expression levels interact with immune cell infiltration. The resulting heatmap revealed a striking pattern: in the majority of tumors examined, TM9SF1 showed significant positive correlations with cancer-associated fibroblasts, neutrophils, endothelial cells, and macrophages. Notably, in UVM, TM9SF1 demonstrated particularly strong positive associations with nearly every type of immune cell analyzed. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Notably, neutrophil is significantly positively correlated with almost all cancers \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-n\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe tumor microenvironment's cellular makeup plays a pivotal role in determining how effectively the immune system can combat cancer. Gaining insight into TM9SF1's impact on malignancies through modifications to the TME is essential. Researchers conducted a co-expression study examining TM9SF1 alongside genes associated with immune function. Interestingly, the findings revealed a striking positive association between TM9SF1 and MHC genes in uveal melanoma and low-grade gliomas, whereas thyroid carcinoma demonstrated a completely inverse relationship. \u003cb\u003e(\u003c/b\u003eFig.s\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. TM9SF1 is positively correlated with chemokine receptors and immunosuppressive genes in most cancers, especially in UVM and DLBC \u003cb\u003e(\u003c/b\u003eFig.s\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, c\u003cb\u003e)\u003c/b\u003e. Additionally, TM9SF1 is almost always positively correlated with chemokines in UVM and DLBC \u003cb\u003e(\u003c/b\u003eFig.s\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Besides, TM9SF1 is almost always positively correlated with immune activation genes \u003cb\u003e(\u003c/b\u003eFig.s\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. TM9SF1 exhibits a robust positive link with numerous immune-related genes across UVM and DLBC, implying it may serve as a crucial immunotherapeutic focus for these malignancies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of Immunotherapies Based on TM9SF1 Expression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn our thorough assessment, we looked at how TM9SF1 compared to other biomarkers in terms of their predictive prowess for treatment success and overall survival. Of the 25 studies we checked out, eight of them found that TM9SF1 had an AUC over 0.5\u0026mdash;right up there with TMB and faring better than B. clonality and T. clonality \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. We also dove into how TM9SF1 interacts with immunotherapy. In some trials\u0026mdash;like the ICB_Li’_PD1 Ipi_Naive, E-MTAB-179, and CAF FAP\u0026mdash;we spotted a high TM9SF1 expression level through Cox-PH regression and T dysfunction measures in the immunotherapy set and immuno-suppressive cell type analyses. On the flip side, in other trials, such as ICB_Hugo2016_PD1 and Patel 2017, TM9SF1's presence was low based on T dysfunction readings and log2FC analysis in the CRISPR screen set \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. To understand TM9SF1's influence on immunotherapy's effectiveness, we turned to the TISMO database. TM9SF1's predictive powers were evident in one cohort using a living tumor model and another using lab-grown cell lines to test cytokine response \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ec \u003cb\u003e\u0026amp; d)\u003c/b\u003e. What's more, we examined how TM9SF1 is tied to IPS scores across the board and found that in BRCA, KIRC, and LUAD, there was a definite negative link between TM9SF1 and the scores \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationship between TM9SF1 expression and clinical features.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further investigate TM9SF1's involvement in cancer development, we analyzed its expression patterns across various clinical parameters using Wilcoxon rank sum testing. The data revealed age-dependent TM9SF1 upregulation in GBMLGG patients, contrasting with downregulation observed in BLCA, KIRP, and PAAD cohorts \u003cb\u003e(Fig.s7a)\u003c/b\u003e. Gender-specific analysis showed heightened TM9SF1 levels in female KIRC, KIRP, and LUADLUSC patients versus male HNSC, MESO, and OSCC cases \u003cb\u003e(Fig.s7b)\u003c/b\u003e. Notable variations emerged when examining T-stage classifications, with significant TM9SF1 expression differences detected in KIRC, MESO, and THCA \u003cb\u003e(Fig.s7c)\u003c/b\u003e. Lymph node metastasis status revealed intriguing patterns: SKCM, TGCT, MESO, and THCA patients without nodal involvement showed elevated TM9SF1, while KIRP and OSCC displayed inverse correlations \u003cb\u003e(Fig.s7d).\u003c/b\u003e Metastatic status comparisons demonstrated markedly higher TM9SF1 in CESC M1 versus M0 cases, contrasting with reduced expression in PRAD \u003cb\u003e(Fig.s7e)\u003c/b\u003e. Tumor stage progression analyses uncovered increasing TM9SF1 levels in advanced KICH and LIHC, opposed by declining trends in KIRC, MESO, SKCM, and THCA \u003cb\u003e(Fig.s7f)\u003c/b\u003e. Grading assessments showed TM9SF1 elevation correlating with higher tumor grades in GBMLGG, HNSC, LGG, LIHC, OSCC, and PAAD, while KIRC exhibited grade-dependent reduction \u003cb\u003e(Fig.s7g)\u003c/b\u003e. Surprisingly, COAD, COADREAD, and READ samples demonstrated substantial TM9SF1 upregulation in cases presenting lymphatic metastasis or perineural invasion \u003cb\u003e(Fig.s7h).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of TM9SF1 Mutations and Methylation Patterns in Pan-Cancer Datasets\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the cBioPortal platform and TCGA pan-cancer datasets, we evaluated the mutation profile of TM9SF1. UCEC exhibited the highest mutation frequency at 4.95%. Missense mutations predominated across multiple tumor types, including UCEC, STAD, COADREAD, SKCM, HNSC, PAAD, CESC, KIRC, and GBM \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Further classification revealed that SNPs and deletions were the primary mutation types, with SNPs being markedly more frequent. Notably, C\u0026thinsp;\u0026gt;\u0026thinsp;T transitions were the most common SNV, and TM9SF1 mutations were evenly distributed across samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eA pan-cancer analysis of TM9SF1 mutations highlighted missense mutations as the dominant alteration type. The 3D protein structure (upper right inset) and mutational mapping identified R230Q/* as the principal mutation site, localized within the EMP70 domain \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. CNV analysis revealed heterogeneous patterns, with hete.amp and hete.del being the most prevalent. READ, KIRC, CHOL, MESO, and UCS showed high frequencies of hete.del, while KICH, TGCT, HNSC, LUAD, SARC, and THYM were enriched for hete.amp. Correlation analysis showed a strong association between CNVs and TM9SF1 mRNA expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Furthermore, CNVs significantly influenced survival outcomes in BLCA, KIRC, KIRP, LAML, LGG, MESO, and UCEC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eA cross-cancer analysis of mutation burden and copy number alterations (CNAs) showed that uterine corpus endometrial carcinoma (UCEC) had the heaviest load of TM9SF1 mutations, driven primarily by missense mutations, structural variants, and gene amplifications. In contrast, adrenocortical carcinoma (ACC) displayed the lowest mutation frequency \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. Notably, TM9SF1 expression levels were strongly linked to increased CNAs, suggesting a clear dose-dependent relationship. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eg\u003cb\u003e)\u003c/b\u003e. Elevated mRNA levels coincided with gene amplifications, whereas shallow and deep deletions corresponded to reduced expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eh\u003cb\u003e)\u003c/b\u003e. Additionally, the TM9SF1-altered cohort displayed higher mutation frequencies in CARMIL3, FITM1, IPO4, PCK2, PSME1, DCAF11, RNF31, ADCY4, TINF2, and TGM1 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ei\u003cb\u003e)\u003c/b\u003e. Pan-cancer SNV analysis of these genes identified RNF31 (18%), ADCY4 (16%), IPO4 (14%), TGM1 (14%), and DCAF11 (11%) as the most frequently mutated, with missense mutations being the predominant type \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ej\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eAnalysis of TM9SF1 methylation via the SMART database revealed elevated methylation in KIRC tumors but reduced levels in BLCA, CESC, CHOL, LIHC, LUAD, PRAD, READ, and UCEC \u003cb\u003e(Fig. S8a)\u003c/b\u003e. Among 10 methylation sites assessed, cg14281756 and cg18432639 exhibited differential methylation across \u0026gt;\u0026thinsp;10 cancer types \u003cb\u003e(Fig. S8b)\u003c/b\u003e. Promoter methylation analysis using UALCAN indicated higher TM9SF1 methylation in normal tissues of COAD, ESCA, LIHC, LUSC, PRAD, BLCA, and UCEC, whereas PAAD tumors showed elevated methylation. THCA and SARC displayed minimal methylation differences, suggesting limited involvement in these cancers \u003cb\u003e(Fig. S9a)\u003c/b\u003e. A consistent inverse correlation between methylation and TM9SF1 mRNA expression was observed \u003cb\u003e(Fig. S9b)\u003c/b\u003e. Clinically, methylation levels significantly impacted prognosis in THCA, HNSC, COAD, and THYM \u003cb\u003e(Fig. S9c)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTM9SF1related networks and pathways\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo better understand TM9SF1's role in tumor development, we first identified its top 10 interacting proteins using the String database. The protein network analysis revealed strong connections between TM9SF1 and several key players, including VPS4A/B, SNF8, multiple CHMP family members, and other regulatory proteins \u003cb\u003e(Fig.\u0026nbsp;7a)\u003c/b\u003e. Using the GEPIA2 platform, we then pinpointed the 100 genes most strongly associated with TM9SF1 across various cancers, with the top six showing particularly striking correlations \u003cb\u003e(Fig.\u0026nbsp;7b)\u003c/b\u003e. A comprehensive heatmap analysis confirmed these relationships held true across all tumor types examined \u003cb\u003e(Fig.\u0026nbsp;7c)\u003c/b\u003e. Diving deeper, we performed functional enrichment studies on 200 TM9SF1-linked genes from GEPIA2. The findings from the GO and KEGG pathway analyses strongly suggest that TM9SF1 is functionally involved in several key biological processes. These include regulating protein synthesis and intracellular transport, mediating cellular uptake pathways, influencing apoptosis, and participating in specialized metabolic functions such as N-Glycan and GPI-anchor biosynthesis. The data collectively indicate TM9SF1's multifaceted role in these critical cellular mechanisms. \u003cb\u003e(Fig.\u0026nbsp;7d, e)\u003c/b\u003e. At the single-cell level, the CancerSEA database mining uncovered TM9SF1's intriguing functional profile. While it showed positive associations with processes like blood vessel formation, cellular specialization, inflammatory responses, and metastatic potential, it surprisingly demonstrated negative ties to DNA maintenance, cell division, and programmed cell death pathways. The exception to this pattern emerged in UVM, where TM9SF1 displayed nearly universal negative correlations with all functional states examined \u003cb\u003e(Fig.\u0026nbsp;7f)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;7. a\u003c/b\u003e. The TM9SF1 protein and its associated proteins were analyzed through the String platform to create a PPI network. \u003cb\u003eb\u003c/b\u003e. The study identified the top six genes that show the strongest association with TM9SF1 across different types of cancer. \u003cb\u003ec.\u003c/b\u003e A heatmap was generated to illustrate the connections between TM9SF1 and these six genes within a range of tumor types. \u003cb\u003ed.\u003c/b\u003e GO analysis was conducted to delve into the functions of TM9SF1. \u003cb\u003ee\u003c/b\u003e. KEGG analysis was performed to uncover the pathways in which TM9SF1 is involved. \u003cb\u003ef.\u003c/b\u003e Using the CancerSEA database, we examined the functional states of TM9SF1 at the single-cell level across various cancers. The right panel showcases the ten functional states that are significantly linked to TM9SF1. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. For a color guide to this figure's legend, please consult the online version of the article\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociation of TM9SF1 with Tumor Diversity, Stem Cell Traits, Mismatch Repair, and DNA Methylation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo better understand TM9SF1's involvement in cancer development, we conducted a thorough analysis of its connections with tumor diversity, stem-like properties, DNA repair mechanisms, and epigenetic regulation. Tumor heterogeneity\u0026mdash;a defining feature of aggressive cancers\u0026mdash;reflects variations in proliferation rates, metastatic capacity, treatment sensitivity, and clinical outcomes, all driven by genetic instability. Our findings indicate that TM9SF1 generally exhibited an inverse relationship with these malignancy markers across most cancers, with TGCT showing particularly strong negative associations \u003cb\u003e(Fig.s10a)\u003c/b\u003e. Interestingly, TM9SF1 displayed consistent positive relationships with both transcriptional and epigenetic stemness markers in the majority of malignancies \u003cb\u003e(Fig.s10b)\u003c/b\u003e. Additionally, our investigation into TM9SF1's interaction with DNA repair and methylation systems uncovered robust positive correlations in numerous cancer types \u003cb\u003e(Fig.s10c)\u003c/b\u003e. This pattern was especially pronounced in bladder, cervical, esophageal, head and neck, liver, pancreatic, and thyroid cancers, among others.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe role of TM9SF1 in HCC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess TM9SF1's clinical significance in diverse patient groups, we conducted a detailed subgroup analysis, categorizing participants by key criteria: serum albumin under 3.5 g/dL, a body mass index of 25 or lower, male sex, no evidence of tumor infiltration, body weight under 70 kg, and N0 lymph node status. In every single one of these subgroups, higher TM9SF1 expression reliably predicted worse clinical prognosis. The findings held true across the board\u0026mdash;whether looking at nutritional status, body composition, or disease progression markers, increased TM9SF1 levels were invariably linked to poorer outcomes. This consistency suggests that TM9SF1 may serve as a robust biomarker, regardless of patient-specific variables. Essentially, no matter how we sliced the data, the same troubling pattern emerged. \u003cb\u003e(Fig.\u0026nbsp;8a)\u003c/b\u003e. Subsequent univariate Cox regression analysis identified several factors influencing overall survival, with TM9SF1 expression emerging as a significant predictor of reduced survival, alongside pathological T stage, tumor status, metastatic involvement, and overall disease stage \u003cb\u003e(Fig.\u0026nbsp;8b)\u003c/b\u003e. For additional verification of these results, a multivariate Cox regression analysis was conducted, reinforcing TM9SF1 as a standalone prognostic indicator for hepatocellular carcinoma \u003cb\u003e(Fig.\u0026nbsp;8c)\u003c/b\u003e. To enhance clinical utility, we developed a prognostic nomogram that combines TM9SF1 expression levels with key clinical factors to predict survival probabilities at one, three, and five years for patients with hepatocellular carcinoma \u003cb\u003e(Fig.\u0026nbsp;8d)\u003c/b\u003e. The accuracy of this model was supported by calibration plots, which showed strong alignment between predicted and actual survival outcomes \u003cb\u003e(Fig.\u0026nbsp;8e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTo further explore TM9SF1's functional role in HCC, we conducted comprehensive pathway analyses using overrepresentation and gene set enrichment approaches. Our findings demonstrated that TM9SF1 predominantly participates in biosynthetic and metabolic regulation, along with molecular signaling cascades \u003cb\u003e(Fig.\u0026nbsp;11a)\u003c/b\u003e. KEGG pathway mapping uncovered TM9SF1's involvement in multiple critical pathways, spanning from metabolic regulation and biosynthesis to oncogenic processes, metabolic disorders, and immune system modulation \u003cb\u003e(Fig.\u0026nbsp;11b)\u003c/b\u003e. Gene set enrichment analysis across GO, KEGG, and HALLMARK databases yielded consistent results, showing TM9SF1's strong positive correlation with protein synthesis, intracellular transport, and post-translational modifications, while exhibiting inverse relationships with immune-related pathways \u003cb\u003e(Fig.\u0026nbsp;11c)\u003c/b\u003e. Further KEGG examination reinforced TM9SF1's significant ties to cell cycle regulation, metabolic reprogramming, tumor initiation, and HCC progression \u003cb\u003e(Fig.\u0026nbsp;11d)\u003c/b\u003e. Supporting these observations, hallmark pathway analysis confirmed TM9SF1's robust association with metabolic activity and cell cycle progression in HCC \u003cb\u003e(Fig.\u0026nbsp;11e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTo explore how TM9SF1 expression influences drug resistance in HCC, we conducted correlation studies by cross-referencing TM9SF1 levels with three drug-gene interaction databases across ten distinct HCC patient cohorts. Our findings revealed a significant association between elevated TM9SF1 expression and reduced sensitivity to certain therapeutics, including Bosutinib, Dasatinib, JQ-1, and Crizotinib, suggesting a potential role in conferring drug resistance \u003cb\u003e(Figs.\u0026nbsp;11f)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;8.\u003c/b\u003e The relationship between TM9SF1 and clinical features in HCC. \u003cb\u003ea\u003c/b\u003e. Prognostic stratification analysis based on Albumin, BMI, Gender, tumor stage, Weight and N0. \u003cb\u003eb.\u003c/b\u003e Univariate Cox regression identified TM9SF1 as the independent risk factor. \u003cb\u003ec.\u003c/b\u003e Multivariate Cox regression identified TM9SF1 as the independent risk factor. \u003cb\u003ed.\u003c/b\u003e Construction of a nomogram to predict 1-, 3-, and 5-year survival probability. \u003cb\u003ee\u003c/b\u003e. The calibration curves of the nomogram at 1-, 3-, and 5-year. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. For a color guide to this figure's legend, please consult the online version of the article.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular features of TM9SF1 at the single-cell level in HCC.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn addition to analyzing TM9SF1 at the molecular level, we conducted cellular localization studies of TM9SF1 using the GSE235057 dataset. The Copykat package was used to distinguish between aneuploid and diploid cells, with aneuploid cells being recognized as neoplastic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). Fifteen cell types were identified, including B cells, endothelial cells, CD8 T cells, plasma cells, gamma delta T cells, Treg cells, CD4 T cells, macrophage cells, MAIT cells, NKT cells, Tex cells, dendritic cells, fibroblast cells, monocytes, and hepatocyte cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). Then we analyzed the expression characteristics of TM9SF1 in 15 cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003ec, d). TM9SF1 expression level in different cell types was visualized, which verified that TM9SF1 highly correlated with Tex cells and plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. The DEGs across the 15 cell types were shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. The t-SNE plot illustrated the dimensionality reduction of regulon modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003eg\u003cb\u003e)\u003c/b\u003e. We further analyzed the single-cell pseudotime trajectory of Tex cells in liver cancer. Utilizing monocle, we explored the distribution of CD4 T cells, CD8 T cells, and Tex cells, and it is evident that CD4 T cells and CD8 T cells are developing into Tex cells. As pseudotime increased, cells would progressively differentiate from cell state 1 to state 5. When CD4 T cells and CD8 T cells undergo exhaustion and transition into Tex cells, the expression level of TM9SF1 increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e9\u003c/span\u003eh\u003cb\u003e)\u003c/b\u003e. The genes that were downregulated and upregulated as pseudotime increased are displayed (\u003cb\u003eFig.s13b)\u003c/b\u003e. GSVA analysis showed that the high-TM9SF1 and low-TM9SF1 groups played important roles in different biological processes. Tex cells with elevated TM9SF1 expression demonstrated enhanced activation of Granulocyte Colony-Stimulating Factor Production, Positive Regulation of Chemokine Production, Vascular Endothelial Growth Factor Receptor Activity and so on (\u003cb\u003eFig.s13a)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used the iTalk R package to plot the ligand-receptor pairs of genes such as growth factors, checkpoint genes, and cytokines in the high TM9SF1 subgroup, creating a cell communication circle and network. We observed a strong interaction network between cells through various types of immune checkpoints, growth factors, cytokines, and other factors (\u003cb\u003eFig.s12a-d)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThen, we performed cell communication analysis to study the interactions between TEX cells with different TM9SF1 expression levels and other cells. The roles of the 16 identified cell types in cellular communication were grouped into four main categories: sender, receiver, influencer, and mediator. Using the \u0026ldquo;CellChat\u0026rdquo; R package, we further categorized the interaction patterns of receiver and sender cell types into distinct types based on the expression of TM9SF1 in Tex cell (\u003cb\u003eFig.s13c, d)\u003c/b\u003e. Tex cells with different levels of TM9SF1 form complex communication networks with other cell populations, suggesting that TM9SF1 plays an important role in liver cancer tumor immune microenvironment, particularly in Tex cells (\u003cb\u003eFig.s13e-h)\u003c/b\u003e. Next, the connection between TM9SF1 expression and specific signaling pathways was explored in more detail. Notably, Tex cells with elevated TM9SF1 levels had significant interactions with other cells, primarily through the CD99, CypA, MHC-Ⅰ and SIRP pathways (\u003cb\u003eFig.s14)\u003c/b\u003e. In contrast, Tex cells with low TM9SF1 levels engaged strongly with other cells via the CLEC, BAG, LCK and PARs pathways (\u003cb\u003eFig.s15)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, research on TM9SF1 remains quite limited. Studies have shown that TM9SF1 is associated with Fuchs endothelial corneal dystrophy and endoepithelial corneal dystrophy (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). TM9SF1 is highly expressed in the vessel walls of ruptured intracranial aneurysms, suggesting its potential involvement in inflammatory responses and vascular wall protein degradation during the formation and rupture of intracranial aneurysms (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). As TM9SF1 expression levels increase, both the severity of ARDS and patient mortality rates rise, this suggests that TM9SF1 is closely associated with the disease state and clinical prognosis of ARDS (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Moreover, TM9SF1 plays a key role in autophagy and intracellular transport (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Although some studies have suggested a link between TM9SF1 and cancer, its role in cancer has not been widely explored. Therefore, it is crucial to explore the role of TM9SF1 in tumor progression through integrated multi-omics pan-cancer analysis.\u003c/p\u003e\u003cp\u003eThis groundbreaking study represents the first comprehensive investigation into the oncogenic potential of TM9SF1 across diverse cancer types through advanced bioinformatics methodologies. Leveraging cutting-edge analytical techniques\u0026mdash;including Kaplan-Meier survival analysis, ROC curve evaluation, and multi-platform data integration (TCGA, GTEx, CCLE, CPTAC, and HPA)\u0026mdash;we mapped TM9SF1 expression profiles and assessed their clinical relevance. Our findings demonstrate that TM9SF1 is markedly upregulated in most malignancies relative to normal tissue counterparts. Protein-level analysis via CPTAC revealed elevated TM9SF1 expression in colon cancer, HNSC, GBM, UCEC, LIHC, and lung cancer, while showing reduced levels in BRCA, ccRCC, and PAAD. Intriguingly, HPA data indicated consistently high TM9SF1 presence across both normal and neoplastic tissues, suggesting context-dependent functionality. The observed variations in TM9SF1 expression patterns across tumor types underscore its multifaceted role in cancer pathogenesis. Diagnostically, ROC analysis established TM9SF1 as a highly reliable biomarker for numerous cancers, including CHOL, GBM, SARC, ESCC, STAD, LIHC, and others. Prognostically, elevated TM9SF1 expression correlated with adverse outcomes in most cancers, though paradoxically, it emerged as a favorable indicator in CHOL, COAD, COADREAD, KIRC, and KIPAN. These dualistic findings position TM9SF1 as a compelling, albeit complex, biomarker with significant potential for clinical application in cancer diagnostics and prognostication.\u003c/p\u003e\u003cp\u003eTM9SF1 genetic alterations were detected in 23 of the 32 cancer types examined, with these changes manifesting as various genomic modifications such as point mutations, copy number amplifications, and complete gene deletions. Among these alterations, simple mutations emerged as the most common variant, appearing in 19 different cancers. Interestingly, gene amplifications showed a more restricted pattern, occurring exclusively in COADREAD, PAAD, CESC, KIRC, and GBM. Existing literature confirms that mutations in oncogenes frequently occur in solid human tumors and contribute significantly to cancer advancement (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The regulation of gene activity through DNA methylation\u0026mdash;where increased promoter methylation typically suppresses gene expression while reduced methylation enhances it\u0026mdash;has been well documented (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Our investigation uncovered notable variations in TM9SF1-related methylation patterns across different malignancies. These results highlight the critical need for deeper exploration of DNA methylation patterns in subsequent research to elucidate their precise involvement in cancer development and progression..\u003c/p\u003e\u003cp\u003eIn this research, we investigated the intricate biological network and signaling mechanisms associated with TM9SF1 through GO and KEGG analyses. Our findings demonstrated that TM9SF1 is notably enriched across a spectrum of pivotal biological processes and signaling pathways, hinting at its indispensable function in the synthesis of life, the movement of molecules, the recycling of cellular components, and the birth of tumors. The GO analysis illuminated TM9SF1's abundance in pathways pertinent to the making and moving of substances, such as the construction of proteins, the conveyance of vesicles, and the process of self-eating, known as autophagy. Furthermore, the KEGG pathway analysis revealed TM9SF1's participation in a host of critical signaling tracks, including protein modification, the intake of molecules, programmed cell death, and the assembly of life-sustaining molecules. These pathways are fundamental to how cells divide, change, and survive. The data suggest that TM9SF1 could be a key player in the synthesis, movement, and recycling of cellular components.\u003c/p\u003e\u003cp\u003eIn recent years, research into other genes of the transmembrane 9 superfamily has increased, gradually revealing their functions. TM9SF2 was involved in the regulation of adult-repopulating HSCs and showed significantly altered expression during the development of the AGM region, which might be related to embryonic development (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Studies have shown that N-sulfate in HS is essential for CHIKV infection of HAP1 cells, with the NDST1 enzyme catalyzing the N-sulfation of HS. TM9SF2 regulated the localization and stability of NDST1, promoting the N-sulfation of HS, thereby facilitating CHIKV infection of host cells. In addition, TM9SF2 played a critical role in tumor development (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Clark et al. identified TM9SF2 as a novel colorectal oncogene transposon mutagenesis screening in mice. High TM9SF2 expression was associated with tumor staging, while low expression correlated with recurrence-free survival. Knocking out TM9SF2 significantly reduced tumor growth (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). LINC01232 enhanced the stability of TM9SF2 mRNA by recruiting EIF4A3, thereby upregulating TM9SF2 expression and promoting pancreatic cancer progression (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). TM9SF3 was highly expressed in various tumor tissues and played a pro-tumor role. Its expression level correlated with gastric cancer invasion depth, tumor staging, and undifferentiated gastric cancer, strongly associating it with poor prognosis. Temporary knockdown of TM9SF3 reduced tumor cell invasion (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). TM9SF3 was also highly expressed in T-cell leukemia cells, and knocking down TM9SF3 inhibited the proliferation and metastasis of human T-cell leukemia cells, suggesting that TM9SF3 could been a potential molecular target for cancer therapies (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). TM9SF4 played an important role in both immunity and cancer. It was essential for the innate immune response of Drosophila through its role in cell adhesion and phagocytosis (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). TM9SF4 was highly expressed in metastatic malignant melanoma and positively correlated with tumor malignancy, silencing TM9SF4 significantly inhibited metastasis (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). TM9SF4 also reduced endoplasmic reticulum stress, protecting drug-resistant breast cancer cells from apoptosis and necrotic cell death, while its knockdown inhibited cell growth and induced cell death (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe tumor microenvironment consists of various cell types, including immune cells, stromal cells, cancer-associated fibroblasts, and endothelial cells, which form a crucial part of the tumor. Increasing evidence suggests that the TME significantly influences therapeutic responses and clinical outcomes (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Immunological profiling indicates that TM9SF1 expression exhibits an inverse relationship with stromal and immune activity, as well as ESTIMATE scores, while also showing reduced infiltration of multiple immune cell subsets across different cancers. Among these immune players, NK T cells are frontline defenders in antitumor immunity, capable of directly targeting and eliminating malignant cells in early disease stages. Beyond their cytotoxic function, they modulate immune activity by releasing signaling molecules like cytokines, chemokines, and growth factors (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Clinical data further support that robust NK T cell presence in solid tumors correlates with improved patient outcomes (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Macrophages, however, display a dual nature in cancer progression. Initially, they act as tumor suppressors by engulfing cancerous cells or hindering their growth through cytotoxic mediators such as TNF-α, NO, and ROS(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Yet, as tumors evolve, these cells often undergo reprogramming into TAMs, shifting toward pro-tumorigenic behavior. Predominantly adopting an M2 phenotype, TAMs secrete factors like VEGF, EGF, and TGF-β, which drive angiogenesis, fuel tumor expansion, and enable metastatic spread (\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Endothelial cells, meanwhile, serve as a critical reservoir for CAFs, facilitating tumor metastasis. Their unchecked proliferation not only shelters malignant cells but also fosters their survival, accelerating disease progression (\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Additionally, TM9SF1 expression shows negative associations with multiple immune-modulating factors, chemokines, and receptors. Immune checkpoints are another linchpin in immune regulation. Elevated checkpoint expression in patients often signals heightened immune activation, including amplified T cell responses and more efficient tumor antigen presentation\u0026mdash;key factors in mounting a potent antitumor defense.\u003c/p\u003e\u003cp\u003eOur findings demonstrate a significant positive association between TM9SF1 expression and key tumor biomarkers including TMB, MSI, LOH, and HRD across multiple cancer types. As established biomarkers, both TMB and MSI play crucial roles in predicting immunotherapy outcomes (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). with TMB particularly serving as a reliable indicator of response to PD-1/PD-L1 blockade therapies. Interestingly, our data reveal that TM9SF1 exhibits comparable predictive power to TMB in forecasting immunotherapy responses. MSI, arising from impaired DNA mismatch repair in tumors, represents another clinically valuable marker. Meanwhile, HRD status serves as a pivotal factor in therapeutic decision-making and prognosis assessment, directly influencing sensitivity to platinum-based regimens and PARP inhibitors. Furthermore, our analysis uncovered a link between elevated TM9SF1 levels and increased tumor stemness. Current research indicates that while stemness correlates positively with tumor heterogeneity, it inversely relates to anti-tumor immune activity (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). The stemness burden has emerged as an important prognostic tool for solid tumors (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e), where high stemness independently predicts recurrence risk. These observations suggest that TM9SF1 may modulate the tumor immune microenvironment and therapeutic response through its potential regulation of cancer stem cell populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study dived headfirst into the burgeoning field of bioinformatics, introducing the role of TM9SF1 in tumor growth and spread. It highlighted the protein's crucial role in determining the health of a patient and its prognostic significance, suggesting that TM9SF1 could potentially become a valuable tumor biomarker. This discovery paves the way for future investigations into TM9SF1's role in cancer development.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile this study provides the first comprehensive pan-cancer analysis of TM9SF1 using multi-omics and single-cell data, several limitations should be acknowledged to contextualize the findings and guide future research. Our analysis is based exclusively on data from public repositories such as TCGA, GTEx, and GEO, and is therefore subject to the inherent biases and limitations of these sources, including potential batch effects, variations in sample processing, and incomplete clinical annotations. Consequently, the findings presented are primarily correlational and cannot establish causality; for instance, while we demonstrate a strong association between high TM9SF1 expression and poor prognosis, our in-silico approach cannot prove that TM9SF1 is the direct driver of these phenomena. Furthermore, as this study is entirely computational, it lacks the in vitro and in vivo experimental validation necessary to confirm the proposed biological functions and signaling pathways. Similarly, the diagnostic and prognostic models developed, including the nomogram for HCC, require rigorous validation in independent, prospective clinical cohorts to establish their true clinical utility. Future laboratory and clinical studies are essential to verify our findings and elucidate the precise molecular mechanisms of TM9SF1 in cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKIRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEGF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eepidermal growth factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOADREAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColon adenocarcinoma/rectum adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUterine carcinosarcoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProtein\u0026ndash;protein interaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTHCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThyroid carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBMLGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlioma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease-free interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAFs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCancer-associated fibroblasts\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMATH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMutational and clonal intratumoral heterogeneity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAdrenocortical cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSingle nucleotide variants\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKIPAN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePan-kidney cohort (KICH\u0026thinsp;+\u0026thinsp;KIRC\u0026thinsp;+\u0026thinsp;KIRP)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTHYM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThymoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLAML\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute myeloid leukemia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eColon adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmune checkpoint blockade\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMicrosatellite instability\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCopy number alteration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLung adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNEO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeoantigen load\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHomologous recombination deficiency\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTILs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor-infiltrating lymphocytes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHepatocellular carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBrain lower grade glioma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKyoto encyclopedia of genes and genomes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBRCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBreast cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSARC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSarcoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgression-free interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKaplan\u0026ndash;Meier\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTriple-negative breast cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMESO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMesothelioma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMDSCs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMyeloid-derived suppressor cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIPS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmune phenotype scores\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene set enrichment analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDLBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLymphoid neoplasm diffuse large B-cell lymphoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eREAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRectum adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAIT༚mucosa-associated invariant T\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTM9SF1\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTransmembrane 9 superfamily member 1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTGF-β༚transforming growth factor-beta\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor microenvironment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHNSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHead and neck squamous cell carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHOL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCholangiocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSKCM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSkin cutaneous melanoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCurve area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBLCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBladder carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStomach adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCopy number variants\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKIRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTMB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor mutational burden\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKICH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKidney chromophobe\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene ontology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStomach and esophageal carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTex༚exhausted T\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTAMs༚tumor-associated macrophages\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMolecular function\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVEGF༚vascular endothelial growth factor\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDSS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisease-specific survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCPG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePheochromocytoma and paraganglioma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBiological pathway\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCellular component\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePRAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProstate adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eESCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEsophageal carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLOH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLoss of heterozygosity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlioblastoma multiforme\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-small cell lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUCEC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUterine corpus endometrial carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUVM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUveal melanoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIC50\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHalf-maximal inhibitory concentration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTGCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTesticular germ cell tumors\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMismatch repair\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCESC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eEthics statement\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNot applicable, the patient data used in this study were acquired from the publicly available databases.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eThe authors confirm no financial or commercial conflicts of interest influenced this research.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFL and YC designed this study, collected the database, and took on the majority of the bioinformatics analysis. YL was responsible for single-cell analysis, visualization, and collecting related datasets. JH, BZ, SZ and YG were responsible for database collection and visualization. DC and ZY modified the manuscript. All of the authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely acknowledge the contributions from the GTEx, TCGA and UCSC projects, which were invaluable for this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed for this study can be found in the UCSC XENA and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE235057. The original data generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang L, Yu C, Lu Y, He P, Guo J, Zhang C, et al. 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Theranostics. 2020;10(19):8721\u0026ndash;43.\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":"TM9SF1, Pan-cancer, hepatocellular carcinoma, Biomarker, Multi-omics analysis, Tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-6992142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6992142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTM9SF1, a transmembrane protein implicated in various cancers, has yet to receive the attention it deserves in oncological research. Leveraging machine learning and publicly available datasets\u0026mdash;including TCGA, GTEx, and UALCAN\u0026mdash;this study examined TM9SF1 expression patterns across multiple cancer types. We evaluated its prognostic significance through Cox regression and Kaplan-Meier survival analyses, while also delving into genetic mutations, methylation profiles, immune infiltration, and therapeutic drug responses. Our findings revealed that TM9SF1 is markedly overexpressed in numerous cancers and correlates with unfavorable patient outcomes. The protein\u0026rsquo;s presence was tied to heightened mutation rates, stronger immune and stromal activity, and interactions with diverse immune cell populations and checkpoint molecules. Additionally, TM9SF1 showed associations with tumor heterogeneity, stem-like properties, and DNA methylation regulators. In hepatocellular carcinoma (HCC), it emerged as an independent risk factor, influenced drug sensitivity, and appeared to mediate its effects through Tex cells, as indicated by single-cell sequencing. This multifaceted investigation highlights TM9SF1\u0026rsquo;s promise as both a prognostic biomarker and a candidate for immunotherapy, paving the way for broader exploration in pan-cancer studies.\u003c/p\u003e","manuscriptTitle":"Multi-omics and single-cell analysis reveals TM9SF1 as a biomarker in pan-cancer diagnosis and prognosis, with a special focus on hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 10:34:56","doi":"10.21203/rs.3.rs-6992142/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":"e001670d-4081-40bf-ba93-ea4bd0dbcb38","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:39:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 10:34:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6992142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6992142","identity":"rs-6992142","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}