Integrating Bulk-seq and Single-cell-seq Reveals TNFSF9 as a Key Regulator in Microsatellite Instability- Positive Stomach Adenocarcinoma

Integrating Bulk-seq and Single-cell-seq Reveals TNFSF9 as a Key Regulator in Microsatellite Instability- Positive Stomach Adenocarcinoma | 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 Integrating Bulk-seq and Single-cell-seq Reveals TNFSF9 as a Key Regulator in Microsatellite Instability- Positive Stomach Adenocarcinoma Jianlong Zhou, Yucheng Zhang, Yongfeng Liu, Jiehui Li, Wenxing Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4455639/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Stomach adenocarcinoma (STAD) with microsatellite instability (MSI) have better prognosis compared with nonMSI. This study aims to elucidate the distinctions in the tumor microenvironment (TME) of MSI and explore its potential mechanisms in STAD. Methods: We analyzed TME differences between MSI and non-MSI using integrated single-cell RNA sequencing (N = 26) and bulk RNA sequencing (N = 237). Differentially expressed genes unveiled key pathways and hub genes, and TNFSF9 expression was validated through immunohistochemistry (IHC) quantitative polymerase chain reaction (qPCR) and Western blot analysis (WB). Results: The results demonstrated a significant association between MSI and improved prognosis ( p < 0.05), along with a higher tumor mutation burden ( p < 0.05). Our study revealed increased abundance of antigen-presenting cells (APCs) in MSI, including M1 cells (40.1% vs. 27.9%) and activated dendritic cells (22.1% vs . 10.5%). Signaling pathway and cell communication analyses indicated the enrichment of cytokine-related pathways in MSI. The findings further revealed an increased expression of TNFSF9 by tumor epithelial cells in MSI. Correlation analysis revealed a positive association between TNFSF9 expression and increased APC abundance. IHC, qPCR, and WB validation revealed increased TNFSF9 expression in MSI tumor epithelial cells. Conclusions: These results offer new insights into the TME in MSI, emphasizing the significant role of TNFSF9 in mediating MSI status, enhancing immunotherapy efficacy, and improving patient survival in STAD. Microsatellite instability Stomach adenocarcinoma TME TNFSF9 Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Stomach adenocarcinoma (STAD) is divided into four subtypes: MSI (microsatellite unstable tumors), EBV (tumors positive for Epstein–Barr virus), GS (genomically stable tumors) and CIN (chromosomal instability tumors) basing on gene phenotype in TCGA database( 1 ), and among them MSI has better prognosis compared with others. Microsatellite instability is defined as the lack of DNA mismatch repair (MMR), which is mainly detected by immunostaining or PCR amplification( 2 , 3 ). Clinical trials showed that stomach adenocarcinoma with MSI has better prognosis combining with inhibitor immunotherapy, such as PD1 or PD-L1( 4 – 6 ). Moreover, recent studies have indicated that MSI, with or without adjuvant therapy, leads to a more favorable prognosis in stomach adenocarcinoma (STAD)( 7 – 9 ). In summary, MSI tends to be regarded as an independent protective factor in STAD. MSI, with higher frequency of gene mutations and the proportion of neoantigen peptides, promotes high immunogenic environment in STAD( 10 ). MSI is a better prognosis in STAD, through enhancing tumor immune response with abundant immune infiltration CD8 + T cells ( 11 , 12 ). These studies focused on analyzing the differences of tumor microenvironment (TME) based on bulk RNA-seq, while it could not reflect the interaction among various cell types. Besides, they did not analyze the potential molecular mechanisms of the different immune infiltration( 13 ). Recently single-cell RNA sequencing (scRNA-seq) has shown advantages in description of tumor complexity and heterogeneity( 14 – 18 ). Integrating bulk RNA-seq and scRNA-seq, we can systematically explore the differences of TMB, molecular mechanisms and prognosis between MSI and nonMSI. Our study, we aim at unravelling the differences of TME and mechanisms between MSI and nonMSI. The finding could facilitate clinical diagnosis and provide new therapeutic methods of STAD. 2. Materials and Methods 2.1 Data collection The study sourced 23 stomach adenocarcinoma pathological sections from the Guangdong Provincial People's Hospital (GDPH) in China, categorized into two groups based on their postoperative pathological immunohistochemistry results: 13 samples were identified as MSI, while the remaining 10 were recognized as nonMSI. The study was conducted with the necessary ethical approval obtained from the Institutional Review Board. Informed written consent was obtained from all participating patients, ensuring their voluntary participation and understanding of the study's objectives and procedures. We obtained the single-cell transcriptome files and clinical information of GSE183904 from the Gene Expression Omnibus (GEO) database. Additionally, we obtained the comprehensive transcriptome and clinical data for GSE62254 from the GEO database, as well as gastric adenocarcinoma data from The Cancer Genome Atlas (TCGA) database. 2.2 Estimation of Immune Cell Infiltration in Bulk RNA-seq The “CIBERSORT” algorithm( 19 ) was applied to calculate the proportion of 22 immune infiltrating cells for each sample based on the LM22 signature for 100 permutations. The differences in immune cell subtypes between the MSI and nonMSI from the bulk RNA-seq data (GSE62254) were analyzed. 2.3 Estimation of Immune Cell Infiltration in Single-Cell RNA Sequencing ScRNA-seq data were transformed into Seurat objects using the “CreateSeuratObject” algorithm in the “Seurat” package( 20 ). Data quality control met the following criteria: UMI count less than 6000, gene count greater than or equal to 250, and mitochondrial gene ratio less than 0.20. We used package “Harmony” to remove batch effects between different patients( 21 ). Non-linear dimensional reduction was performed with the UMAP method. Cell clustering was performed using the “FindClusters” function in Seurat, and the clusters were annotated by the expression of canonical marker genes( 16 – 18 ). The R package “CellChat” is a tool used to infer, analyze and visualize intercellular communication networks( 22 ). “CellChat” can quantitatively characterize and compare intercellular communications through the major referred signaling inputs and outputs for cell populations based on the known structural composition of ligand–receptor interactions. 2.4 Differentially Expressed Gene (DEG) and Pathway Analysis The “Limma” package was used to perform the DEG analysis. An empirical Bayesian method was applied to estimate the fold change between cluster one and cluster two identified by the consensus clustering method using moderated T tests( 23 ). The adjusted P value for multiple testing was calculated using the Benjamini–Hochberg correction. The genes with an absolute log2 fold change greater than 0.25 were identified as DEGs between MSI and nonMSI. Pathway and functional enrichment analysis of Kyoko encyclopedia of genes and genomes (KEGG) and Gene ontology (GO) were conducted by using R packages: “ClusterProfler” (version 4.0.5), “org.Hs.eg.db” (version 3.13.0), “ggplot2” (version 3.3.5), “enrichplot” (version 1.12.3)( 24 ). Differentially expressed genes were further used to analyze the underlying molecular pathway mechanisms. 2.5 Definition and Validation of Hub Genes and Key Genes We defined hub genes as the intersection of cytokine-related pathways identified through GO, KEGG, and GSEA KEGG enrichment analyses, resulting in the identification of 8 hub genes. We then verified their expression patterns in single-cell analysis, identifying pivotal cells correlated with their expression. Notably, among these hub genes, TNFSF9 exhibited exclusive expression in MSI tumor epithelial cells, designating it as a key gene. Lastly, we substantiated the significance of TNFSF9 through validation in the public database and basic experiment. 2.6 Immunohistochemistry For immunohistochemistry analysis, paraffin-embedded tissue sections were incubated with TNFSF9 antibody overnight at 4°C, treated with secondary antibodies, and visualized using the DAB staining protocol, followed by hematoxylin counterstaining for background clarity. The assessment of the area and density of stained regions, as well as the integrated optical density (IOD) of the immunohistochemistry (IHC) sections, was performed using Image-Pro Plus software version 6.0. The evaluation of density signals within five randomly selected fields of the tissue sections was carried out in a blinded manner, followed by a statistical analysis to determine their significance. 2.7 Cell culture and Quantitative Polymerase Chain Reaction The SNU-1 cell line, defined as MSI, and the AGS cell line, defined as nonMSI ( 25 ), were cultured in 1640 medium supplemented with 10% fetal bovine serum under conditions of 5% CO2, 37°C, and humidified environment. To assess the expression of TNFSF9 in these cell lines, Quantitative Polymerase Chain Reaction (qPCR) was employed. Total RNA extraction from the cells was performed using a commercial RNA extraction kit. Subsequently, reverse transcription was conducted to convert RNA into cDNA. For qPCR analysis, specific primers for TNFSF9 were utilized, with the forward primer sequence as 5'-AAATGTTCTGATCGATGGG-3' and the reverse primer sequence as 5'-CCGCAGCTCTAGTTGAAAGAAGA-3' ( 26 ). The expression levels were normalized, and the qPCR reaction was carried out in a real-time PCR instrument. The relative expression of TNFSF9 was determined using the 2^-ΔΔCt method. 2.8 Western blot analysis Cells were lysed utilizing the M-PER Mammalian Protein Extraction Reagent from Thermo Scientific, followed by the execution of sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The membranes were subsequently visualized on a Tanon 4600 chemiluminescence imaging system, based in Shanghai, China, employing the Immobilon Western Chemiluminescent HRP Substrate by Millipore. Quantification of a specific protein was achieved by calculating the ratio of its intensity bands to those of α-Tubulin, utilizing the Image J software for analysis. 2.9 Survival Analysis Statistical analysis was performed using R software (version 4.1.2). All gene expression data were log2 transformed and standardized. The Wilcoxon test was used to compare TMB and TNFSF9 expression between MSI and nonMSI. Cox proportional hazards model and Kaplan-Meier curve were used for survival analysis. Spearman's correlation was used to investigate the association between TNFSF9 expression and immune cell infiltration. A significance level of P < 0.05 was applied. Data visualization was performed using R software. 3. Results 3.1 MSI is associated with better survival Overview of the experiment design was depicted in Fig. 1 A. Survival analysis revealed that subgroup MSI was associated with better prognosis in STAD in bulk RNA-seq data of GSE62254 ( P < 0.05; Fig. 1 B). Cox analysis of unadjusted covariates showed that MSI had better prognosis, after adjusting covariates it still had better prognosis (Table S1). It meant that MSI was an independent factor in STAD. 3.2 Inter-tumor TME Heterogeneity between MSI and nonMSI in Bulk RNA-seq We assessed the heterogeneity of TME between MSI and nonMSI in Bulk RNA-seq. The results showed that immune stromal scores and tumor purity score between MSI and nonMSI were not significant (P > 0.05; Fig. 1 C-E). We further characterized their immunologic landscape across the 22 immune-related cell types with “CIBERSORT” algorithm. The results demonstrated that MSI had higher abundance of CD4 + memory activated T cells and M1 cells but lower abundance of CD4 + memory resting T cells, Plasma cells and Eosinophils cells compared with nonMSI (Fig. 1 F). 3.3 Inter-tumor TME Heterogeneity between MSI and nonMSI in scRNA-seq We analyzed the single-cell-level heterogeneity of the TME between MSI and nonMSI. Data quality control, PCA reduction, and the removal of patient batch effects are depicted in Fig. 2 A-C. Based on cell lineage-specific marker genes (Fig. S1A), these cells were categorized into eight types, including T&NK cells, B cells, epithelial cells, endothelial cells, fibroblast cells, mast cells, myeloid cells, and plasma cells (Fig. 2 D-E). The results indicated that T&NK cells constituted a higher proportion in MSI (46% vs . 34%), while plasma cells and fibroblast cells were more prevalent in nonMSI (Fig. 2 F). We further analyzed lymphatic cells within MSI (N = 7) and nonMSI (N = 19). Distinct clusters of T&NK cells were identified by specific makers (Fig. 3 A), including CD4 + T, CD8 + T, T-pro, and NK cells (Fig. S1B). The results showed an increased abundance of NK cells (14% vs . 11%) in MSI, while CD4 + T cells were more abundant in nonMSI (Fig. 3 B). No significant differences were observed in the abundance of Tpro cells and CD8 + T cells. Different clusters of CD8 + T cells were identified (Fig. S1C), including cytotoxic CD8 + T cells, effector memory CD8 + T cells, exhausted CD8 + T cells, MAIT CD8 + T cells, and naive CD8 + T cells (Fig. 3 C). The results indicated that exhausted CD8 + T cells (23% vs . 13%) were predominantly represented in MSI, whereas effector memory CD8 + T cells were more prominent in nonMSI (Fig. 3 D). Within the CD4 + T cell population, distinct clusters were identified using marker genes (Fig. S1D), including naive CD4 + T cells, effector memory CD4 + T cells, regulatory CD4 + T cells, Th1-like CD4 + T cells, and Th17 CD4 + T cells (Fig. 3 E). The results showcased that regulatory CD4 + T cells (33% vs . 26%) and Th1-like CD4 + T cells (15% vs . 11%) were particularly expressed in MSI, while naive CD4 + T cells and effector memory CD4 + T cells were enriched in nonMSI (Fig. 3 F). Additionally, we conducted a subset analysis of myeloid cells within the MSI and nonMSI groups. Unique clusters of myeloid cells were identified utilizing marker genes (Fig. S1E), encompassing monocyte cells, macrophage cells, and dendritic cells (Fig. 4 A). The results revealed that dendritic cells (19% vs. 16%) and monocyte cells (53% vs . 34%) were primarily prevalent in MSI, while macrophage cells were predominantly enriched in nonMSI (Fig. 4 B). Furthermore, among the macrophage cells, diverse clusters were distinguished based on marker genes (Fig. S1F), including M0 cells, M1 cells, and M2 cells (Fig. 4 C). The findings demonstrated that M1 cells (40% vs . 28%) were predominantly present in MSI, whereas M0 cells and M2 cells were primarily found in nonMSI (Fig. 4 D). Dendritic cells were also identified using marker genes (Fig. S1G), categorized as activated DC cells, cDC1 cells, cDC2 cells, and pDC cells (Fig. 4 E). It was observed that activated DC cells (22% vs . 10%) and cDC1 cells (13% vs . 6%) were particularly enriched in MSI, whereas cDC2 cells were more abundant in nonMSI (Fig. 4 F). In summary, MSI displayed increased immune cell infiltration, particularly with a noticeable rise in antigen-presenting cell populations. Additionally, we observed signs of immune exhaustion within the MSI. 3.4 Cytokine-Related Pathways Play an Important Role in MSI Status To further investigate the differences in the TME between MSI and nonMSI, we initially screened for DEGs between MSI and nonMSI using bulk RNA-seq data. A total of 338 DEGs were identified, with a selection criterion of Fold Change (FC) > 0.25 and an adjusted P-value < 0.05. This set comprised 112 upregulated DEGs and 225 downregulated DEGs (Fig. 5 A). GSEA analyses revealed significant enrichment of DEGs in the Cytokine-cytokine receptor interaction pathway (Fig. 5 B). GO analyses highlighted the enrichment of DEGs in the Cytokine activity pathway (Fig. 5 C). Similarly, KEGG analyses demonstrated a marked enrichment of DEGs in the Cytokine-cytokine receptor interaction pathway (Fig. 5 D). Based on these analyses, we hypothesized that MSI might exhibit enriched cytokine-related pathways. To validate this hypothesis, we identified hub genes by intersecting cytokine-related genes obtained from GO, KEGG, and GSEA pathway analyses (Fig. 5 E). A total of 8 genes were defined as hub genes (Table S2). Additionally, we explored intercellular communication patterns using the "CellChat" package. Our analyses, including RankSimilarity analysis (Fig. 5 F), NetAnalysis SignalingRole heatmap analysis (Fig. 5 G), and RankNet analysis (Fig. 5 H), consistently demonstrated an enrichment of the cytokine-related pathway CXCL within MSI. 3.5 TNFSF9 Expressed by Tumor Epithelial Cells Plays an Important Role in MSI Status The 8 hub genes identified through the intersection of cytokine pathways underwent single-cell level verification. Notably, TNFSF9 was observed to be significantly overexpressed in the tumor epithelium of the MSI (Fig. 6 A). Furthermore, elevated expression of TNFSF9 was consistently observed in the MSI across both GEO and TCGA datasets (Figures B-C). Based on these findings, we hypothesize that TNFSF9 may play a crucial role in the MSI status and, as such, is designated as a key gene. We further verified the result. Our findings demonstrated a significant elevation of TMB in MSI ( P < 0.05; Fig. 6 D). To elucidate the correlation between TNFSF9 expression and immune infiltration, we further confirmed the co-expression of TNFSF9 with 22 immune-related cell types. Our analysis indicated positive correlations between TNFSF9 and M1 cells, which was categorized as Antigen-Presenting Cells (APCs) (Fig. 6 E). IHC analysis revealed that TNFSF9 was expressed by tumor epithelial cells and exhibited enrichment in MSI (Fig. 6 F-G). qPCR analysis demonstrated higher expression of TNFSF9 in SNU-1 (MSI) cell line compared to AGS (nonMSI) cell line (Fig. 6 H). WB analysis also demonstrated higher expression of TNFSF9 in SNU-1 (MSI) cell line compared to AGS (nonMSI) cell line (Fig. 6 I). 4. Discussion TCGA database classified STAD into four subtypes, including MSI, EBV, CIN and GS( 1 ). Recently, immune checkpoint inhibitors (ICIs) have demonstrated significant clinical effects on patients with MSI, while little effect on nonMSI in STAD. However, recently studies have found that even without immunotherapy, MSI have better prognosis ( 7 ). Consistent with these observations, our study corroborates that MSI serves as an independent protective factor in STAD, prompting a deeper exploration into the mechanisms driving this phenomenon. Previous studies emphasized the correlation between MSI and high tumor mutational burden (TMB), fostering increased infiltration of CD4 + T and CD8 + T cells compared to nonMSI ( 11 ). However, these studies fell short of unraveling the intricate mechanisms underlying the abundance of immune cells. Thus, our approach integrates bulk RNA-seq and scRNA-seq to unveil a more comprehensive understanding of the disparities between MSI and nonMSI in STAD. Our findings highlight a higher TMB and increased APCs in MSI. The connection between TMB and antitumor immunity, particularly in MSI-H cancers, stems from the propensity of MSI-H to accumulate frameshift mutations, rendering these cancers highly immunogenic ( 10 ). The resulting neoantigens trigger APC activation, exemplified by the abundance of M1 cells and activated dendritic cells in MSI. Dendritic cells, as professional APCs, play a crucial role in shaping adaptive immune responses ( 27 ). M1 cells, induced by T-helper type-1 cytokines, assume a pivotal role in inhibiting cell proliferation and inducing tissue damage ( 28 ). Our results highlighted that the prevalence of tumor-associated antigens in MSI fosters the activation of APCs and stimulates antitumor immunity. Moreover, we observed an abundance of Th1-like CD4 + T cells in MSI. These cells are crucial in the antigen presentation process. Dendritic cells enhance antigen presentation with the assistance of CD4 + T cells ( 29 ). Th1 CD4 + T cells, in turn, induce type 1 immune responses to combat intracellular pathogens by activating M1 cell ( 30 ). Following activation by APCs, Th1 cells, which differentiate from Th0 cells, express CD40L and secrete cytokines such as IL-2, IFN-γ, and TNF to further enhance their immune function ( 31 ). Activated Th1 cells play a critical role in the immune response, including the secretion of IL-2 and other cytokines to promote cytotoxic T cell activation and assist cellular immunity ( 32 ). This collaboration between APCs and Th1 CD4 + T cells suggests a mechanism that enhances the antigen presentation process in MSI, further promoting effective anti-tumor immunity. Nevertheless, we also observed instances of immunosuppression within the MSI, primarily due to the heightened abundance of regulatory T cells and "exhausted" CD8 + T cells. Regulatory T cells are recognized for their role in curbing tumor immunity; however, they also serve to prevent autoimmunity by modulating excessive immune responses ( 29 ). We verified that MSI exhibited robust immune responses, postulating that the overexpression of Tregs might be attributed to an exaggerated immune reaction. Additionally, a substantial abundance of exhausted CD8 + T cells was noted in MSI. Prolonged antigenic stimulation can lead to a state of dysfunction known as "exhaustion" in tumor-specific CD8 + T cells ( 33 ). Although immunosuppressive cells are elevated in MSI, we posit that the sustained and potent antigenic stimulation, coupled with efficient antigen presentation, ultimately tilts the balance towards anti-tumor immunity. Moreover, studies have indicated that exhausted CD8 + T cells can regain their tumor-killing capabilities with the application of Immune Checkpoint Inhibitors (ICIs) ( 34 ). This phenomenon is one of the reasons underlying the benefits of immunotherapy in the MSI. Further analysis of differentially expressed genes (DEGs) and pathway enrichment revealed a significant association between MSI and cytokine-related pathways. Eight key hub genes were identified, with TNFSF9 standing out due to its exclusive expression in MSI tumor epithelial cells. TNFSF9, a ligand for CD137, is expressed in APCs and various tumor cells. TNFSF9, a member of the TNF superfamily, acts as a ligand for CD137 and is expressed in APCs ( 35 , 36 ). And, TNFSF9 is found to be expressed in various types of tumor cells as well( 37 , 38 ). CD137, prevalent in activated leukocytes, especially T cells and dendritic cells, plays a pivotal role in immune activation( 39 ). Interaction with its ligand, TNFSF9, triggers key signaling pathways—NF-κB, JNK/SAPK, and p38/MAPK—activating both CD4 + T and CD8 + T cells ( 40 ). Beyond T cells, this interaction bi-directionally activates antigen-presenting cells like monocytes and fosters the crucial initiation of cytotoxic T-cell responses by activating dendritic cells ( 41 ). In summary, elevated expression of TNFSF9 in MSI tumor cells, coupled with its positive correlation with APCs, suggests a pivotal role in mediating MSI status and enhancing the effectiveness of immunotherapy. While we combined data from both bulk RNA-seq and single-cell sequencing to comprehensively elucidate the disparities between MSI and nonMSI, it's important to acknowledge the inherent limitations of this study. Despite delving into potential mechanisms based on existing research, it's crucial to note that additional in-depth mechanistic studies are essential to solidify these findings. 5. Conclusions In summary, our finding underscored a more favorable prognosis for in MSI and offered insights into the distinct tumor microenvironment dynamics between MSI and nonMSI. Our analysis revealed an elevated presence of APCs in MSI. Notably, any observed immunosuppression in MSI could be attributed to the heightened strength of immune response. Mechanistic analysis highlighted the crucial role of tumor cells in utilizing TNFSF9 to enhance antigen presentation and bolster the anti-tumor immune response in MSI. These results provided new insights into the TME in MSI that TNFSF9 played an important role in mediating MSI status. Declarations Author Contributions Conceptualization : Jianlong Zhou, Yongfeng Liu, Yucheng Zhang; Methodology : Jianlong Zhou, Jiehui Li, Wenxing Zhang, Junjiang Wang; Software : Jianlong Zhou; Investigation : Jianlong Zhou; Data curation : Jianlong Zhou; Writing—original draft preparation : Jianlong Zhou; Writing—review and editing : Jianlong Zhou; Visualization : Jianlong Zhou; Funding acquisition : Xueqing Yao, Yong Li; Supervision : Yong Li; Project administration : Huolun Feng, Jiabin Zheng. Ethical approval and informed consent The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Ethics Committee of Guangdong Provincial People's Hospital, Ethical conduct of research Approved by the Ethics Committee of Guangdong Provincial People's Hospital (protocol code KY-Q-2021-263-01). Informed written consent was obtained from all participating patients, ensuring their voluntary participation and understanding of the study's objectives and procedures. Funding This work was supported by Leading Innovation Specialist Support Program of Guangdong Province, the Science and Technology Planning Project of Ganzhou (No. 202101074816), National Key Clinical Specialty Construction Project (2021–2024, No. 2023YW030009), Science and Technology Plan of Guangzhou, Guangdong Province, China (No. 202201011416), National Natural Science Foundation of China (No. 2370836) and Medical Scientific Research Foundation of Guangdong Province of China (B2022168). Data Availability Statement All the data used in this study can be found in TCGA and GEO database. All presented data in this study are available from the corresponding author upon reasonable request. Acknowledgments The authors would like to thank TCGA (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and GEO (https://www.ncbi.nlm.nih.gov/geo/) public datasets for gene expression and survival information collection. Conflicts of Interest The authors declare no conflict of interest. Consent to publish All authors have read and agreed to the published version of the manuscript. References Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513(7517):202-9. Baretti M, Le DT. DNA mismatch repair in cancer. Pharmacol Ther. 2018;189:45-62. Zhang L. Immunohistochemistry versus microsatellite instability testing for screening colorectal cancer patients at risk for hereditary nonpolyposis colorectal cancer syndrome. Part II. The utility of microsatellite instability testing. J Mol Diagn. 2008;10(4):301-7. Kang YK, Boku N, Satoh T, Ryu MH, Chao Y, Kato K, et al. 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CD137L-stimulated dendritic cells are more potent than conventional dendritic cells at eliciting cytotoxic T-cell responses. Oncoimmunology. 2013;2(11):e26859. Qian Y, Pei D, Cheng T, Wu C, Pu X, Chen X, et al. CD137 ligand-mediated reverse signaling inhibits proliferation and induces apoptosis in non-small cell lung cancer. Med Oncol. 2015;32(3):44. Dimberg J, Hugander A, Wågsäter D. Expression of CD137 and CD137 ligand in colorectal cancer patients. Oncol Rep. 2006;15(5):1197-200. Sica G, Chen L. Biochemical and immunological characteristics of 4-1BB (CD137) receptor and ligand and potential applications in cancer therapy. Arch Immunol Ther Exp (Warsz). 1999;47(5):275-9. Geuijen C, Tacken P, Wang LC, Klooster R, van Loo PF, Zhou J, et al. A human CD137×PD-L1 bispecific antibody promotes anti-tumor immunity via context-dependent T cell costimulation and checkpoint blockade. Nat Commun. 2021;12(1):4445. Langstein J, Michel J, Fritsche J, Kreutz M, Andreesen R, Schwarz H. CD137 (ILA/4-1BB), a member of the TNF receptor family, induces monocyte activation via bidirectional signaling. J Immunol. 1998;160(5):2488-94. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2025 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 29 Dec, 2024 Reviews received at journal 27 Dec, 2024 Reviewers agreed at journal 18 Dec, 2024 Reviews received at journal 05 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers agreed at journal 25 Jun, 2024 Reviewers invited by journal 23 Jun, 2024 Editor assigned by journal 25 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 21 May, 2024 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. 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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-4455639","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309636360,"identity":"195e6ed5-a507-47d5-abc4-ba971147cc0c","order_by":0,"name":"Jianlong Zhou","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianlong","middleName":"","lastName":"Zhou","suffix":""},{"id":309636363,"identity":"6130412e-4aba-401c-9ab5-ed8196a4e58b","order_by":1,"name":"Yucheng Zhang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yucheng","middleName":"","lastName":"Zhang","suffix":""},{"id":309636365,"identity":"6f46a8e9-c75c-4dbc-b802-2b239d753253","order_by":2,"name":"Yongfeng Liu","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongfeng","middleName":"","lastName":"Liu","suffix":""},{"id":309636366,"identity":"1790ca71-0b06-48e5-a501-ef19008d6043","order_by":3,"name":"Jiehui Li","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiehui","middleName":"","lastName":"Li","suffix":""},{"id":309636369,"identity":"a6000139-e403-4339-9e7b-1d28d129d39a","order_by":4,"name":"Wenxing Zhang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenxing","middleName":"","lastName":"Zhang","suffix":""},{"id":309636370,"identity":"8f67481d-5e0d-4fa6-b4ba-090f2544b572","order_by":5,"name":"Junjiang Wang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junjiang","middleName":"","lastName":"Wang","suffix":""},{"id":309636372,"identity":"2e6dc193-8af3-4e06-af6a-f6720f6bc3ea","order_by":6,"name":"Xueqing Yao","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueqing","middleName":"","lastName":"Yao","suffix":""},{"id":309636373,"identity":"f0de1b2d-dbe4-4cf1-aa28-e7bb4c794523","order_by":7,"name":"Huolun Feng","email":"","orcid":"","institution":"South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Huolun","middleName":"","lastName":"Feng","suffix":""},{"id":309636378,"identity":"637b27be-aa47-4b91-86a1-c562bbf462ed","order_by":8,"name":"Jiabin Zheng","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiabin","middleName":"","lastName":"Zheng","suffix":""},{"id":309636380,"identity":"1bccc4a7-8f6d-4c16-9757-e8c0084120bf","order_by":9,"name":"Yong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACPmYwZcPAcICHSC1sEC1ppGiBUIdJ0cLOY/i44Nd5u77bvQcYPu6pZeCf3UDIYTzGxjP7bifPvHMugXHGs+MMEncOENRiJs3bczvZ4EaOATPPgWMMBhIJRGk5R6oWnh8H7KBaaojRwlZszNuQnCB554zBwRkHDvBI3CCghZ//8MbHPH/s7Plu9xg++HCgTo5/BgEtDAwcBgyMbQyJDRLAqAFGEDGxw/6AgeEPgz2DBJhXR4SOUTAKRsEoGGkAAMU6QUBxUKNEAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-05-21 14:48:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4455639/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4455639/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-02471-0","type":"published","date":"2025-03-28T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57949703,"identity":"d2691dd1-2214-473f-873c-2f6c955a5210","added_by":"auto","created_at":"2024-06-07 20:52:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60575,"visible":true,"origin":"","legend":"\u003cp\u003eInter-tumor TME heterogeneity between MSI and nonMSI in bulk RNA-seq. (A) Experiment design overview. (B) Kaplan-Meier survival curves for STAD between MSI and nonMSI. (C-E) Differences in immune score, tumor purity, and stromal score between MSI and nonMSI. (F) Boxplots illustrating the ratio of 22 immune cell infiltrates between MSI and nonMSI. *, P \u0026lt; 0.05; **, P \u0026lt; 0.01; ***, P \u0026lt; 0.001; ****, P \u0026lt; 0.0001; ns = not significant.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/a685f9e19b61e2dea7a8930c.png"},{"id":57949702,"identity":"d35da9fe-a3f9-4d25-a58f-e40e458e62d9","added_by":"auto","created_at":"2024-06-07 20:52:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121869,"visible":true,"origin":"","legend":"\u003cp\u003eThe single-cell landscape in STAD. (A) VlnPlot of nCount_RNA, nFeature_RNA, and mitoRatio after data filtering. (B) DimPlot presenting patient-specific clustering through PCA for single-cell analysis. (C) UMAP visualizing patient IDs in STAD after batch effect removal. (D) UMAP depicting clustering of STAD. (E) UMAP revealing cell lineages marked by specific genes in STAD. (F) Histograms depicting the proportion of STAD cells between MSI and nonMSI.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/dc9266ff52fe524b0e1c428c.png"},{"id":57949542,"identity":"10cbed8e-1476-4352-9ceb-0bac6682af02","added_by":"auto","created_at":"2024-06-07 20:44:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105763,"visible":true,"origin":"","legend":"\u003cp\u003eA Single-Cell Atlas of Lymphatic cells. (A) UMAP illustrating cell lineages marked by specific genes in T\u0026amp;NK cells. (B) Histograms representing the proportion of T\u0026amp;NK cells between MSI and nonMSI. (C) UMAP displaying cell lineages marked by specific genes in CD8+ T cells. (D) Histograms showing the proportion of CD8+ T cells between MSI and nonMSI. (E) UMAP showcasing cell lineages marked by specific genes in CD4+ T cells. (F) Histograms indicating the proportion of CD4+ T cells between MSI and nonMSI.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/31812394390f451152c5e656.png"},{"id":57949540,"identity":"27655c93-2488-45a1-a4e7-c172d0664302","added_by":"auto","created_at":"2024-06-07 20:44:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84318,"visible":true,"origin":"","legend":"\u003cp\u003eA Single-Cell Atlas of Myeloid Cells. (A) UMAP illustrating cell lineages marked by specific genes in myeloid cells. (B) Histograms presenting the proportion of myeloid cells between MSI and nonMSI. (C) UMAP displaying cell lineages marked by specific genes in macrophage cells. (D) Histograms indicating the proportion of macrophage cells between MSI and nonMSI. (E) UMAP indicating cell lineages marked by specific genes in dendritic cells. (F) Histograms showing the proportion of dendritic cells between MSI and nonMSI.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/f85045811780c3e284087a09.png"},{"id":57949537,"identity":"06c2592a-563f-4977-83c8-ca4d86d20d8a","added_by":"auto","created_at":"2024-06-07 20:44:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57295,"visible":true,"origin":"","legend":"\u003cp\u003eSignificance of Cytokine-Related Pathways in MSI Status. (A) Volcano plots showcasing differential gene expression (average log fold change) between MSI and nonMSI. (B) KEGG annotation of DEGs, where dot size indicates gene count and color scale represents adjusted P value. (C) GO annotation of DEGs, with dot size indicating gene count and color scale indicating adjusted P value. (D) GSEA KEGG enriched pathway analysis highlighting the cytokine-cytokine receptor interaction pathway. (E) Venn diagram depicting gene intersections within cytokine-related pathways. (F-H) RankSimilarity analysis (F), NetAnalysis SignalingRole heatmap analysis (G), and RankNet analysis (H) through Cellchat, uncovering distinct patterns between MSI and nonMSI.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/8bf675e5ea07384ae0218378.png"},{"id":57949543,"identity":"efe03a73-450d-437b-bf24-6ef87e860ba7","added_by":"auto","created_at":"2024-06-07 20:44:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":158105,"visible":true,"origin":"","legend":"\u003cp\u003eTumor epithelium expresses TNFSF9 to enhance antigen presentation and promote anti-tumor response in MSI at the single cell level. (A) Violin plot illustrating heightened TNFSF9 expression in MSI tumor epithelial cells compared to nonMSI at the single cell level. (B-C) Violin plots depicting increased TNFSF9 expression in MSI across TCGA and GEO databases. (D) Comparison of TMB levels between MSI and nonMSI. (E) Correlation analysis of TNFSF9 expression with M1 cells. (F) IHC staining of TNFSF9 showed high TNFSF9 expression and expressed by epithelial cells in MSI. a, IHC staining of TNFSF9 in MSI (magnification, ×200); b, IHC staining of TNFSF9 in MSI (magnification, ×200); c, IHC staining of TNFSF9 in nonMSI (magnification, ×200); d, IHC staining of TNFSF9 in nonMSI (magnification, ×400). (G) The mean IOD of TNFSF9 between MSI (n=13) and nonMSI (n=10). (H) qPCR analysis of TNFSF9 expression between SNU-1 (MSI) and AGS (nonMSI) cell lines. (I) WB analysis of TNFSF9 expression between SNU-1 (MSI) and AGS (nonMSI) cell lines. The original blot is in Supplementary Figure 2. *, P \u0026lt; 0.05; **, P \u0026lt; 0.01; ***, P \u0026lt; 0.001; ****, P \u0026lt; 0.0001; ns = not significant.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/044741e83baf103d690762fa.png"},{"id":79604715,"identity":"6a171483-4f44-4cba-a01e-18ec49121c94","added_by":"auto","created_at":"2025-03-31 16:01:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1754965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/5c7b153d-fb8f-4182-8a09-9ef7be249538.pdf"},{"id":57949536,"identity":"1db9e1cb-e595-4c24-8c75-ded2b15e1056","added_by":"auto","created_at":"2024-06-07 20:44:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":946782,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4455639/v1/3804f0c52dfc4a24e0b45f47.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Bulk-seq and Single-cell-seq Reveals TNFSF9 as a Key Regulator in Microsatellite Instability- Positive Stomach Adenocarcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStomach adenocarcinoma (STAD) is divided into four subtypes: MSI (microsatellite unstable tumors), EBV (tumors positive for Epstein\u0026ndash;Barr virus), GS (genomically stable tumors) and CIN (chromosomal instability tumors) basing on gene phenotype in TCGA database(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), and among them MSI has better prognosis compared with others. Microsatellite instability is defined as the lack of DNA mismatch repair (MMR), which is mainly detected by immunostaining or PCR amplification(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Clinical trials showed that stomach adenocarcinoma with MSI has better prognosis combining with inhibitor immunotherapy, such as PD1 or PD-L1(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Moreover, recent studies have indicated that MSI, with or without adjuvant therapy, leads to a more favorable prognosis in stomach adenocarcinoma (STAD)(\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In summary, MSI tends to be regarded as an independent protective factor in STAD.\u003c/p\u003e \u003cp\u003eMSI, with higher frequency of gene mutations and the proportion of neoantigen peptides, promotes high immunogenic environment in STAD(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). MSI is a better prognosis in STAD, through enhancing tumor immune response with abundant immune infiltration CD8\u003csup\u003e+\u003c/sup\u003eT cells (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These studies focused on analyzing the differences of tumor microenvironment (TME) based on bulk RNA-seq, while it could not reflect the interaction among various cell types. Besides, they did not analyze the potential molecular mechanisms of the different immune infiltration(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Recently single-cell RNA sequencing (scRNA-seq) has shown advantages in description of tumor complexity and heterogeneity(\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Integrating bulk RNA-seq and scRNA-seq, we can systematically explore the differences of TMB, molecular mechanisms and prognosis between MSI and nonMSI.\u003c/p\u003e \u003cp\u003eOur study, we aim at unravelling the differences of TME and mechanisms between MSI and nonMSI. The finding could facilitate clinical diagnosis and provide new therapeutic methods of STAD.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eThe study sourced 23 stomach adenocarcinoma pathological sections from the Guangdong Provincial People's Hospital (GDPH) in China, categorized into two groups based on their postoperative pathological immunohistochemistry results: 13 samples were identified as MSI, while the remaining 10 were recognized as nonMSI. The study was conducted with the necessary ethical approval obtained from the Institutional Review Board. Informed written consent was obtained from all participating patients, ensuring their voluntary participation and understanding of the study's objectives and procedures. We obtained the single-cell transcriptome files and clinical information of GSE183904 from the Gene Expression Omnibus (GEO) database. Additionally, we obtained the comprehensive transcriptome and clinical data for GSE62254 from the GEO database, as well as gastric adenocarcinoma data from The Cancer Genome Atlas (TCGA) database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Estimation of Immune Cell Infiltration in Bulk RNA-seq\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;CIBERSORT\u0026rdquo; algorithm(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) was applied to calculate the proportion of 22 immune infiltrating cells for each sample based on the LM22 signature for 100 permutations. The differences in immune cell subtypes between the MSI and nonMSI from the bulk RNA-seq data (GSE62254) were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Estimation of Immune Cell Infiltration in Single-Cell RNA Sequencing\u003c/h2\u003e \u003cp\u003eScRNA-seq data were transformed into Seurat objects using the \u0026ldquo;CreateSeuratObject\u0026rdquo; algorithm in the \u0026ldquo;Seurat\u0026rdquo; package(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Data quality control met the following criteria: UMI count less than 6000, gene count greater than or equal to 250, and mitochondrial gene ratio less than 0.20. We used package \u0026ldquo;Harmony\u0026rdquo; to remove batch effects between different patients(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Non-linear dimensional reduction was performed with the UMAP method. Cell clustering was performed using the \u0026ldquo;FindClusters\u0026rdquo; function in Seurat, and the clusters were annotated by the expression of canonical marker genes(\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The R package \u0026ldquo;CellChat\u0026rdquo; is a tool used to infer, analyze and visualize intercellular communication networks(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). \u0026ldquo;CellChat\u0026rdquo; can quantitatively characterize and compare intercellular communications through the major referred signaling inputs and outputs for cell populations based on the known structural composition of ligand\u0026ndash;receptor interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Differentially Expressed Gene (DEG) and Pathway Analysis\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;Limma\u0026rdquo; package was used to perform the DEG analysis. An empirical Bayesian method was applied to estimate the fold change between cluster one and cluster two identified by the consensus clustering method using moderated T tests(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The adjusted P value for multiple testing was calculated using the Benjamini\u0026ndash;Hochberg correction. The genes with an absolute log2 fold change greater than 0.25 were identified as DEGs between MSI and nonMSI. Pathway and functional enrichment analysis of Kyoko encyclopedia of genes and genomes (KEGG) and Gene ontology (GO) were conducted by using R packages: \u0026ldquo;ClusterProfler\u0026rdquo; (version 4.0.5), \u0026ldquo;org.Hs.eg.db\u0026rdquo; (version 3.13.0), \u0026ldquo;ggplot2\u0026rdquo; (version 3.3.5), \u0026ldquo;enrichplot\u0026rdquo; (version 1.12.3)(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Differentially expressed genes were further used to analyze the underlying molecular pathway mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Definition and Validation of Hub Genes and Key Genes\u003c/h2\u003e \u003cp\u003eWe defined hub genes as the intersection of cytokine-related pathways identified through GO, KEGG, and GSEA KEGG enrichment analyses, resulting in the identification of 8 hub genes. We then verified their expression patterns in single-cell analysis, identifying pivotal cells correlated with their expression. Notably, among these hub genes, TNFSF9 exhibited exclusive expression in MSI tumor epithelial cells, designating it as a key gene. Lastly, we substantiated the significance of TNFSF9 through validation in the public database and basic experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immunohistochemistry\u003c/h2\u003e \u003cp\u003eFor immunohistochemistry analysis, paraffin-embedded tissue sections were incubated with TNFSF9 antibody overnight at 4\u0026deg;C, treated with secondary antibodies, and visualized using the DAB staining protocol, followed by hematoxylin counterstaining for background clarity. The assessment of the area and density of stained regions, as well as the integrated optical density (IOD) of the immunohistochemistry (IHC) sections, was performed using Image-Pro Plus software version 6.0. The evaluation of density signals within five randomly selected fields of the tissue sections was carried out in a blinded manner, followed by a statistical analysis to determine their significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Cell culture and Quantitative Polymerase Chain Reaction\u003c/h2\u003e \u003cp\u003eThe SNU-1 cell line, defined as MSI, and the AGS cell line, defined as nonMSI (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), were cultured in 1640 medium supplemented with 10% fetal bovine serum under conditions of 5% CO2, 37\u0026deg;C, and humidified environment. To assess the expression of TNFSF9 in these cell lines, Quantitative Polymerase Chain Reaction (qPCR) was employed. Total RNA extraction from the cells was performed using a commercial RNA extraction kit. Subsequently, reverse transcription was conducted to convert RNA into cDNA. For qPCR analysis, specific primers for TNFSF9 were utilized, with the forward primer sequence as 5'-AAATGTTCTGATCGATGGG-3' and the reverse primer sequence as 5'-CCGCAGCTCTAGTTGAAAGAAGA-3' (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The expression levels were normalized, and the qPCR reaction was carried out in a real-time PCR instrument. The relative expression of TNFSF9 was determined using the 2^-ΔΔCt method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Western blot analysis\u003c/h2\u003e \u003cp\u003eCells were lysed utilizing the M-PER Mammalian Protein Extraction Reagent from Thermo Scientific, followed by the execution of sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The membranes were subsequently visualized on a Tanon 4600 chemiluminescence imaging system, based in Shanghai, China, employing the Immobilon Western Chemiluminescent HRP Substrate by Millipore. Quantification of a specific protein was achieved by calculating the ratio of its intensity bands to those of α-Tubulin, utilizing the Image J software for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Survival Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using R software (version 4.1.2). All gene expression data were log2 transformed and standardized. The Wilcoxon test was used to compare TMB and TNFSF9 expression between MSI and nonMSI. Cox proportional hazards model and Kaplan-Meier curve were used for survival analysis. Spearman's correlation was used to investigate the association between TNFSF9 expression and immune cell infiltration. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was applied. Data visualization was performed using R software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 MSI is associated with better survival\u003c/h2\u003e \u003cp\u003eOverview of the experiment design was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Survival analysis revealed that subgroup MSI was associated with better prognosis in STAD in bulk RNA-seq data of GSE62254 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Cox analysis of unadjusted covariates showed that MSI had better prognosis, after adjusting covariates it still had better prognosis (Table S1). It meant that MSI was an independent factor in STAD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Inter-tumor TME Heterogeneity between MSI and nonMSI in Bulk RNA-seq\u003c/h2\u003e \u003cp\u003eWe assessed the heterogeneity of TME between MSI and nonMSI in Bulk RNA-seq.\u0026nbsp;The results showed that immune stromal scores and tumor purity score between MSI and nonMSI were not significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E). We further characterized their immunologic landscape across the 22 immune-related cell types with \u0026ldquo;CIBERSORT\u0026rdquo; algorithm. The results demonstrated that MSI had higher abundance of CD4\u003csup\u003e+\u003c/sup\u003e memory activated T cells and M1 cells but lower abundance of CD4\u003csup\u003e+\u003c/sup\u003e memory resting T cells, Plasma cells and Eosinophils cells compared with nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Inter-tumor TME Heterogeneity between MSI and nonMSI in scRNA-seq\u003c/h2\u003e \u003cp\u003eWe analyzed the single-cell-level heterogeneity of the TME between MSI and nonMSI. Data quality control, PCA reduction, and the removal of patient batch effects are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C. Based on cell lineage-specific marker genes (Fig. S1A), these cells were categorized into eight types, including T\u0026amp;NK cells, B cells, epithelial cells, endothelial cells, fibroblast cells, mast cells, myeloid cells, and plasma cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). The results indicated that T\u0026amp;NK cells constituted a higher proportion in MSI (46% \u003cem\u003evs\u003c/em\u003e. 34%), while plasma cells and fibroblast cells were more prevalent in nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further analyzed lymphatic cells within MSI (N\u0026thinsp;=\u0026thinsp;7) and nonMSI (N\u0026thinsp;=\u0026thinsp;19). Distinct clusters of T\u0026amp;NK cells were identified by specific makers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), including CD4\u003csup\u003e+\u003c/sup\u003eT, CD8\u003csup\u003e+\u003c/sup\u003eT, T-pro, and NK cells (Fig. S1B). The results showed an increased abundance of NK cells (14% \u003cem\u003evs\u003c/em\u003e. 11%) in MSI, while CD4\u003csup\u003e+\u003c/sup\u003eT cells were more abundant in nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). No significant differences were observed in the abundance of Tpro cells and CD8\u003csup\u003e+\u003c/sup\u003eT cells. Different clusters of CD8\u003csup\u003e+\u003c/sup\u003eT cells were identified (Fig. S1C), including cytotoxic CD8\u003csup\u003e+\u003c/sup\u003eT cells, effector memory CD8\u0026thinsp;+\u0026thinsp;T cells, exhausted CD8\u003csup\u003e+\u003c/sup\u003eT cells, MAIT CD8\u003csup\u003e+\u003c/sup\u003eT cells, and naive CD8\u003csup\u003e+\u003c/sup\u003eT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The results indicated that exhausted CD8\u003csup\u003e+\u003c/sup\u003eT cells (23% \u003cem\u003evs\u003c/em\u003e. 13%) were predominantly represented in MSI, whereas effector memory CD8\u003csup\u003e+\u003c/sup\u003eT cells were more prominent in nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Within the CD4\u003csup\u003e+\u003c/sup\u003eT cell population, distinct clusters were identified using marker genes (Fig. S1D), including naive CD4\u003csup\u003e+\u003c/sup\u003eT cells, effector memory CD4\u003csup\u003e+\u003c/sup\u003eT cells, regulatory CD4\u003csup\u003e+\u003c/sup\u003eT cells, Th1-like CD4\u003csup\u003e+\u003c/sup\u003eT cells, and Th17 CD4\u003csup\u003e+\u003c/sup\u003eT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The results showcased that regulatory CD4\u003csup\u003e+\u003c/sup\u003eT cells (33% \u003cem\u003evs\u003c/em\u003e. 26%) and Th1-like CD4\u003csup\u003e+\u003c/sup\u003eT cells (15% \u003cem\u003evs\u003c/em\u003e. 11%) were particularly expressed in MSI, while naive CD4\u003csup\u003e+\u003c/sup\u003eT cells and effector memory CD4\u003csup\u003e+\u003c/sup\u003eT cells were enriched in nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we conducted a subset analysis of myeloid cells within the MSI and nonMSI groups. Unique clusters of myeloid cells were identified utilizing marker genes (Fig. S1E), encompassing monocyte cells, macrophage cells, and dendritic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The results revealed that dendritic cells (19% \u003cem\u003evs.\u003c/em\u003e 16%) and monocyte cells (53% \u003cem\u003evs\u003c/em\u003e. 34%) were primarily prevalent in MSI, while macrophage cells were predominantly enriched in nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, among the macrophage cells, diverse clusters were distinguished based on marker genes (Fig. S1F), including M0 cells, M1 cells, and M2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The findings demonstrated that M1 cells (40% \u003cem\u003evs\u003c/em\u003e. 28%) were predominantly present in MSI, whereas M0 cells and M2 cells were primarily found in nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Dendritic cells were also identified using marker genes (Fig. S1G), categorized as activated DC cells, cDC1 cells, cDC2 cells, and pDC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). It was observed that activated DC cells (22% \u003cem\u003evs\u003c/em\u003e. 10%) and cDC1 cells (13% \u003cem\u003evs\u003c/em\u003e. 6%) were particularly enriched in MSI, whereas cDC2 cells were more abundant in nonMSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, MSI displayed increased immune cell infiltration, particularly with a noticeable rise in antigen-presenting cell populations. Additionally, we observed signs of immune exhaustion within the MSI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cytokine-Related Pathways Play an Important Role in MSI Status\u003c/h2\u003e \u003cp\u003eTo further investigate the differences in the TME between MSI and nonMSI, we initially screened for DEGs between MSI and nonMSI using bulk RNA-seq data. A total of 338 DEGs were identified, with a selection criterion of Fold Change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;0.25 and an adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This set comprised 112 upregulated DEGs and 225 downregulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). GSEA analyses revealed significant enrichment of DEGs in the Cytokine-cytokine receptor interaction pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). GO analyses highlighted the enrichment of DEGs in the Cytokine activity pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Similarly, KEGG analyses demonstrated a marked enrichment of DEGs in the Cytokine-cytokine receptor interaction pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Based on these analyses, we hypothesized that MSI might exhibit enriched cytokine-related pathways. To validate this hypothesis, we identified hub genes by intersecting cytokine-related genes obtained from GO, KEGG, and GSEA pathway analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). A total of 8 genes were defined as hub genes (Table S2). Additionally, we explored intercellular communication patterns using the \"CellChat\" package. Our analyses, including RankSimilarity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), NetAnalysis SignalingRole heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), and RankNet analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH), consistently demonstrated an enrichment of the cytokine-related pathway CXCL within MSI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 TNFSF9 Expressed by Tumor Epithelial Cells Plays an Important Role in MSI Status\u003c/h2\u003e \u003cp\u003eThe 8 hub genes identified through the intersection of cytokine pathways underwent single-cell level verification. Notably, TNFSF9 was observed to be significantly overexpressed in the tumor epithelium of the MSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Furthermore, elevated expression of TNFSF9 was consistently observed in the MSI across both GEO and TCGA datasets (Figures B-C). Based on these findings, we hypothesize that TNFSF9 may play a crucial role in the MSI status and, as such, is designated as a key gene. We further verified the result. Our findings demonstrated a significant elevation of TMB in MSI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To elucidate the correlation between TNFSF9 expression and immune infiltration, we further confirmed the co-expression of TNFSF9 with 22 immune-related cell types. Our analysis indicated positive correlations between TNFSF9 and M1 cells, which was categorized as Antigen-Presenting Cells (APCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). IHC analysis revealed that TNFSF9 was expressed by tumor epithelial cells and exhibited enrichment in MSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). qPCR analysis demonstrated higher expression of TNFSF9 in SNU-1 (MSI) cell line compared to AGS (nonMSI) cell line (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). WB analysis also demonstrated higher expression of TNFSF9 in SNU-1 (MSI) cell line compared to AGS (nonMSI) cell line (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTCGA database classified STAD into four subtypes, including MSI, EBV, CIN and GS(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Recently, immune checkpoint inhibitors (ICIs) have demonstrated significant clinical effects on patients with MSI, while little effect on nonMSI in STAD. However, recently studies have found that even without immunotherapy, MSI have better prognosis (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Consistent with these observations, our study corroborates that MSI serves as an independent protective factor in STAD, prompting a deeper exploration into the mechanisms driving this phenomenon. Previous studies emphasized the correlation between MSI and high tumor mutational burden (TMB), fostering increased infiltration of CD4\u003csup\u003e+\u003c/sup\u003eT and CD8\u003csup\u003e+\u003c/sup\u003eT cells compared to nonMSI (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, these studies fell short of unraveling the intricate mechanisms underlying the abundance of immune cells. Thus, our approach integrates bulk RNA-seq and scRNA-seq to unveil a more comprehensive understanding of the disparities between MSI and nonMSI in STAD.\u003c/p\u003e \u003cp\u003eOur findings highlight a higher TMB and increased APCs in MSI. The connection between TMB and antitumor immunity, particularly in MSI-H cancers, stems from the propensity of MSI-H to accumulate frameshift mutations, rendering these cancers highly immunogenic (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The resulting neoantigens trigger APC activation, exemplified by the abundance of M1 cells and activated dendritic cells in MSI. Dendritic cells, as professional APCs, play a crucial role in shaping adaptive immune responses (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). M1 cells, induced by T-helper type-1 cytokines, assume a pivotal role in inhibiting cell proliferation and inducing tissue damage (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our results highlighted that the prevalence of tumor-associated antigens in MSI fosters the activation of APCs and stimulates antitumor immunity.\u003c/p\u003e \u003cp\u003eMoreover, we observed an abundance of Th1-like CD4\u003csup\u003e+\u003c/sup\u003eT cells in MSI. These cells are crucial in the antigen presentation process. Dendritic cells enhance antigen presentation with the assistance of CD4\u003csup\u003e+\u003c/sup\u003eT cells (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Th1 CD4\u003csup\u003e+\u003c/sup\u003eT cells, in turn, induce type 1 immune responses to combat intracellular pathogens by activating M1 cell (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Following activation by APCs, Th1 cells, which differentiate from Th0 cells, express CD40L and secrete cytokines such as IL-2, IFN-γ, and TNF to further enhance their immune function (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Activated Th1 cells play a critical role in the immune response, including the secretion of IL-2 and other cytokines to promote cytotoxic T cell activation and assist cellular immunity (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This collaboration between APCs and Th1 CD4\u0026thinsp;+\u0026thinsp;T cells suggests a mechanism that enhances the antigen presentation process in MSI, further promoting effective anti-tumor immunity.\u003c/p\u003e \u003cp\u003eNevertheless, we also observed instances of immunosuppression within the MSI, primarily due to the heightened abundance of regulatory T cells and \"exhausted\" CD8\u003csup\u003e+\u003c/sup\u003eT cells. Regulatory T cells are recognized for their role in curbing tumor immunity; however, they also serve to prevent autoimmunity by modulating excessive immune responses (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). We verified that MSI exhibited robust immune responses, postulating that the overexpression of Tregs might be attributed to an exaggerated immune reaction. Additionally, a substantial abundance of exhausted CD8\u003csup\u003e+\u003c/sup\u003eT cells was noted in MSI. Prolonged antigenic stimulation can lead to a state of dysfunction known as \"exhaustion\" in tumor-specific CD8\u003csup\u003e+\u003c/sup\u003eT cells (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Although immunosuppressive cells are elevated in MSI, we posit that the sustained and potent antigenic stimulation, coupled with efficient antigen presentation, ultimately tilts the balance towards anti-tumor immunity. Moreover, studies have indicated that exhausted CD8\u003csup\u003e+\u003c/sup\u003eT cells can regain their tumor-killing capabilities with the application of Immune Checkpoint Inhibitors (ICIs) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This phenomenon is one of the reasons underlying the benefits of immunotherapy in the MSI.\u003c/p\u003e \u003cp\u003eFurther analysis of differentially expressed genes (DEGs) and pathway enrichment revealed a significant association between MSI and cytokine-related pathways. Eight key hub genes were identified, with TNFSF9 standing out due to its exclusive expression in MSI tumor epithelial cells. TNFSF9, a ligand for CD137, is expressed in APCs and various tumor cells. TNFSF9, a member of the TNF superfamily, acts as a ligand for CD137 and is expressed in APCs (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). And, TNFSF9 is found to be expressed in various types of tumor cells as well(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). CD137, prevalent in activated leukocytes, especially T cells and dendritic cells, plays a pivotal role in immune activation(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Interaction with its ligand, TNFSF9, triggers key signaling pathways\u0026mdash;NF-κB, JNK/SAPK, and p38/MAPK\u0026mdash;activating both CD4\u0026thinsp;+\u0026thinsp;T and CD8\u0026thinsp;+\u0026thinsp;T cells (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Beyond T cells, this interaction bi-directionally activates antigen-presenting cells like monocytes and fosters the crucial initiation of cytotoxic T-cell responses by activating dendritic cells (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In summary, elevated expression of TNFSF9 in MSI tumor cells, coupled with its positive correlation with APCs, suggests a pivotal role in mediating MSI status and enhancing the effectiveness of immunotherapy.\u003c/p\u003e \u003cp\u003eWhile we combined data from both bulk RNA-seq and single-cell sequencing to comprehensively elucidate the disparities between MSI and nonMSI, it's important to acknowledge the inherent limitations of this study. Despite delving into potential mechanisms based on existing research, it's crucial to note that additional in-depth mechanistic studies are essential to solidify these findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, our finding underscored a more favorable prognosis for in MSI and offered insights into the distinct tumor microenvironment dynamics between MSI and nonMSI. Our analysis revealed an elevated presence of APCs in MSI. Notably, any observed immunosuppression in MSI could be attributed to the heightened strength of immune response. Mechanistic analysis highlighted the crucial role of tumor cells in utilizing TNFSF9 to enhance antigen presentation and bolster the anti-tumor immune response in MSI. These results provided new insights into the TME in MSI that TNFSF9 played an important role in mediating MSI status.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Jianlong Zhou, Yongfeng Liu, Yucheng Zhang; \u003cstrong\u003eMethodology\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Jianlong Zhou, Jiehui Li, Wenxing Zhang, Junjiang Wang;\u0026nbsp;\u003cstrong\u003eSoftware\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Jianlong Zhou; \u003cstrong\u003eInvestigation\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Jianlong Zhou; \u003cstrong\u003eData curation\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eJianlong Zhou; \u003cstrong\u003eWriting\u0026mdash;original draft preparation\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Jianlong Zhou; \u003cstrong\u003eWriting\u0026mdash;review and editing\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eJianlong Zhou; \u003cstrong\u003eVisualization\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eJianlong Zhou; \u003cstrong\u003eFunding acquisition\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eXueqing Yao, Yong Li; \u003cstrong\u003eSupervision\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Yong Li; \u003cstrong\u003eProject administration\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eHuolun Feng, Jiabin Zheng.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Ethics Committee of Guangdong Provincial People\u0026apos;s Hospital, Ethical conduct of research Approved by the Ethics Committee of Guangdong Provincial People\u0026apos;s Hospital (protocol code KY-Q-2021-263-01).\u0026nbsp;Informed written consent was obtained from all participating patients, ensuring their voluntary participation and understanding of the study\u0026apos;s objectives and procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Leading Innovation Specialist Support Program of Guangdong Province, the Science and Technology Planning Project of Ganzhou (No. 202101074816), National Key Clinical Specialty Construction Project (2021\u0026ndash;2024, No. 2023YW030009), Science and Technology Plan of Guangzhou, Guangdong Province, China (No. 202201011416), National Natural Science Foundation of China (No. 2370836) and Medical Scientific Research Foundation of Guangdong Province of China (B2022168).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used in this study can be found in TCGA and GEO database. All presented data in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank TCGA (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and GEO (https://www.ncbi.nlm.nih.gov/geo/) public datasets for gene expression and survival information collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eComprehensive molecular characterization of gastric adenocarcinoma. 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CD137L-stimulated dendritic cells are more potent than conventional dendritic cells at eliciting cytotoxic T-cell responses. Oncoimmunology. 2013;2(11):e26859.\u003c/li\u003e\n\u003cli\u003eQian Y, Pei D, Cheng T, Wu C, Pu X, Chen X, et al. CD137 ligand-mediated reverse signaling inhibits proliferation and induces apoptosis in non-small cell lung cancer. Med Oncol. 2015;32(3):44.\u003c/li\u003e\n\u003cli\u003eDimberg J, Hugander A, W\u0026aring;gs\u0026auml;ter D. Expression of CD137 and CD137 ligand in colorectal cancer patients. Oncol Rep. 2006;15(5):1197-200.\u003c/li\u003e\n\u003cli\u003eSica G, Chen L. Biochemical and immunological characteristics of 4-1BB (CD137) receptor and ligand and potential applications in cancer therapy. Arch Immunol Ther Exp (Warsz). 1999;47(5):275-9.\u003c/li\u003e\n\u003cli\u003eGeuijen C, Tacken P, Wang LC, Klooster R, van Loo PF, Zhou J, et al. A human CD137\u0026times;PD-L1 bispecific antibody promotes anti-tumor immunity via context-dependent T cell costimulation and checkpoint blockade. Nat Commun. 2021;12(1):4445.\u003c/li\u003e\n\u003cli\u003eLangstein J, Michel J, Fritsche J, Kreutz M, Andreesen R, Schwarz H. CD137 (ILA/4-1BB), a member of the TNF receptor family, induces monocyte activation via bidirectional signaling. J Immunol. 1998;160(5):2488-94.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microsatellite instability, Stomach adenocarcinoma, TME, TNFSF9, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4455639/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4455639/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eStomach adenocarcinoma (STAD) with microsatellite instability (MSI) have better prognosis compared with nonMSI. This study aims to elucidate the distinctions in the tumor microenvironment (TME) of MSI and explore its potential mechanisms in STAD.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe analyzed TME differences between MSI and non-MSI using integrated single-cell RNA sequencing (N\u0026thinsp;=\u0026thinsp;26) and bulk RNA sequencing (N\u0026thinsp;=\u0026thinsp;237). Differentially expressed genes unveiled key pathways and hub genes, and TNFSF9 expression was validated through immunohistochemistry (IHC) quantitative polymerase chain reaction (qPCR) and Western blot analysis (WB).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe results demonstrated a significant association between MSI and improved prognosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), along with a higher tumor mutation burden (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Our study revealed increased abundance of antigen-presenting cells (APCs) in MSI, including M1 cells (40.1% \u003cem\u003evs.\u003c/em\u003e 27.9%) and activated dendritic cells (22.1% \u003cem\u003evs\u003c/em\u003e. 10.5%). Signaling pathway and cell communication analyses indicated the enrichment of cytokine-related pathways in MSI. The findings further revealed an increased expression of TNFSF9 by tumor epithelial cells in MSI. Correlation analysis revealed a positive association between TNFSF9 expression and increased APC abundance. IHC, qPCR, and WB validation revealed increased TNFSF9 expression in MSI tumor epithelial cells.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThese results offer new insights into the TME in MSI, emphasizing the significant role of TNFSF9 in mediating MSI status, enhancing immunotherapy efficacy, and improving patient survival in STAD.\u003c/p\u003e","manuscriptTitle":"Integrating Bulk-seq and Single-cell-seq Reveals TNFSF9 as a Key Regulator in Microsatellite Instability- Positive Stomach Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 20:44:37","doi":"10.21203/rs.3.rs-4455639/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-29T07:55:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-27T14:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-12-18T09:11:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-05T04:12:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307347745534707763828246717847654866098","date":"2024-07-29T23:56:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97032465294002018439052582415059731500","date":"2024-06-25T20:32:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-23T19:56:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-25T15:23:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-22T13:18:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2024-05-21T14:47:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36643091-c60b-4903-b486-bbd479982174","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-31T15:58:18+00:00","versionOfRecord":{"articleIdentity":"rs-4455639","link":"https://doi.org/10.1186/s40001-025-02471-0","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2025-03-28 15:56:55","publishedOnDateReadable":"March 28th, 2025"},"versionCreatedAt":"2024-06-07 20:44:37","video":"","vorDoi":"10.1186/s40001-025-02471-0","vorDoiUrl":"https://doi.org/10.1186/s40001-025-02471-0","workflowStages":[]},"version":"v1","identity":"rs-4455639","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4455639","identity":"rs-4455639","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}