Comprehensive bioinformatics analysis and experimental validation revealed that high-expression ARHGAP9 affected the progression of gastric cancer

Comprehensive bioinformatics analysis and experimental validation revealed that high-expression ARHGAP9 affected the progression of gastric cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive bioinformatics analysis and experimental validation revealed that high-expression ARHGAP9 affected the progression of gastric cancer Lizhen Qiu, Haowen Wu, Yu Song, Weixuan Hong, Xinxiong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7311091/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background: Gastric cancer is characterized by poor prognosis due to late diagnosis and therapeutic resistance. ARHGAP9, a Rho GTPase-activating protein, regulates cytoskeletal dynamics and MAPK signaling, but its role in gastric cancer progression remains unclear. Methods: Multi-omics data from TCGA, GEO, and cBioPortal were integrated to analyze ARHGAP9 expression, genetic alterations, and immune correlations in gastric cancer. Enrichment analysis, ceRNA network construction, PPI network analysis, immune infiltration assessment (ESTIMATE, CIBERSORT, ssGSEA), and drug sensitivity evaluation (GDSC, CTRP) were performed to elucidate ARHGAP9's role in gastric cancer. In vitro experiments (qRT-PCR, CCK-8, Transwell) with ARHGAP9 knockdown were conducted in gastric adenocarcinoma cell lines (SGC-7901, MGC-803) for functional validation. Results: ARHGAP9 was significantly upregulated in Gastric cancer samples (P < 0.05), correlating with advanced T stage, histological grade, and poor prognosis. Differentially expressed genes between high and low ARHGAP9 groups were enriched in immune-related pathways (BCR signaling). High ARHGAP9 expression was associated with increased CD8 + T cell infiltration and positive correlation with immune checkpoints (PD-1, CTLA4; P < 0.001). Low ARHGAP9 expression enhanced sensitivity to PD-1 inhibitors and chemotherapeutic agents (docetaxel, ribociclib). In vitro knockdown of ARHGAP9 inhibited gastric adenocarcinoma cell proliferation, migration, and invasion (P < 0.05). Conclusion: ARHGAP9 drives gastric cancer progression through immune regulation and serves as a prognostic biomarker. Targeting ARHGAP9 may improve therapeutic response in gastric cancer, particularly in patients resistant to immunotherapy. Gastric cancer ARHGAP9 Bioinformatics Tumor markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Gastric cancer is among the most common malignant tumors worldwide, with an incidence and mortality rate both ranking fifth among all cancers, representing a significant public health challenge and imposing a substantial disease burden 1 . Due to the often nonspecific and subtle symptoms in early stages, the majority of patients are diagnosed at an advanced stage, thereby missing the optimal treatment window and leading to a poor prognosis, with a postoperative 5-year survival rate of less than 30% 2 . The progression of gastric cancer is a complex process that involves genetic mutations, epigenetic modifications, and dynamic alterations in the tumor microenvironment 3 , 4 . Currently, the mainstay of gastric cancer treatment remains surgical resection, often combined with multimodal approaches including chemotherapy, radiotherapy, and targeted therapy 5 . However, the inherent heterogeneity and complexity of gastric cancer impose significant limitations on current therapeutic strategies. Postoperative recurrence and metastasis are the leading causes of treatment failure, and chemotherapy resistance, along with the limited applicability of targeted therapies, present major clinical challenges. Therefore, a comprehensive understanding of the molecular mechanisms driving gastric cancer development and progression, as well as the identification of novel prognostic biomarkers and therapeutic targets, is essential to improve patient outcomes. ARHGAP9 is located on human chromosome 12q13.3 and demonstrates significant Rho GTPase-activating protein (GAP) activity toward multiple Rho family GTPases in vitro, facilitating the conversion of GTP to GDP, and plays a role in regulating the adhesion of hematopoietic cells to the extracellular matrix. ARHGAP9 has been identified as a novel scaffold protein for mitogen-activated protein kinases (MAPKs) 6 . Moreover, studies have confirmed that ARHGAP9 interacts with Erk2 and p38α, thereby inhibiting their activation. As a key MAPK, Erk2 activation and subsequent MAPK/ERK2 phosphorylation are essential for cancer cell proliferation and tumor development 7 , 8 . Activation of the ERK2 pathway contributes to tumor progression in various human cancers, including lung adenocarcinoma 9 , liver cancer 10 , and renal cancer 11 , and significantly influences patient prognosis. This study aims to characterize ARHGAP9 in gastric cancer through comprehensive bioinformatics analysis, multi-omics validation, and functional experimental verification, with the goal of elucidating its role in tumor progression and providing valuable insights for tumor immunotherapy and prognostic assessment. Materials and Methods Expression Analysis Pan-cancer expression analysis was performed using TPM-normalized RNA-seq data from 33 cancer types, downloaded and curated from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov ). Differential expression analysis was conducted on TCGA-STAD samples, excluding samples lacking clinical information or duplicates, resulting in a final cohort of 375 tumor samples and 32 adjacent normal controls. Two independent expression datasets (GSE54129 and GSE118916) were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) and used for external validation. ARHGAP9 expression levels across different tumor stages and histological grades were analyzed based on corresponding clinical annotations. Protein expression and promoter methylation levels of ARHGAP9 in STAD were further investigated using the UALCAN platform ( http://resource.path.uab.edu/ ). Enrichment Analysis TCGA-STAD tumor samples were stratified into high and low ARHGAP9 expression groups based on the median expression value. Differentially expressed genes (DEGs) were identified using thresholds of adjusted p-value 1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package in R. GO analysis included three categories: biological processes (BP), cellular components (CC), and molecular functions (MF). Gene Set Enrichment Analysis (GSEA) was conducted using the curated gene set c2.cp.all.v2022.1.Hs.symbols.gmt. Co-expression Analysis Genes significantly co-expressed with ARHGAP9 (adjusted p-value 0.5) were identified, and the top 100 positively and negatively correlated genes were selected for further analysis. In addition, a heatmap was generated to visualize the top 100 most significantly upregulated and downregulated DEGs. PPI Network Analysis Overlapping genes between DEGs and co-expressed gene sets were selected to investigate ARHGAP9-related protein interactions. An ARHGAP9-centered protein-protein interaction (PPI) network was constructed using the STRING database ( https://string-db.org/ ) and visualized using Cytoscape. The most significant network module was identified using MCODE analysis with the following parameters: degree cutoff = 2, maximum depth = 100, k-core = 2, and node score cutoff = 0.2. Hub genes were identified using the DMNC algorithm in CytoHubba, and the top 10 genes were selected for further analysis. CeRNA Network Construction Potential ARHGAP9-targeting miRNAs were predicted by integrating results from three databases: miRDB ( https://mirdb.org/ ), TargetScan ( http://www.targetscan.org/ ), and miRWalk ( http://mirwalk.umm.uni-heidelberg.de/ ). The starBase platform ( http://starbase.sysu.edu.cn/ ) was used to visualize miRNA-ARHGAP9 interactions based on TCGA data. Target lncRNAs of the predicted miRNAs were identified, and the ARHGAP9-miRNA-lncRNA network was visualized using Sankey diagrams and Cytoscape. Genetic Alteration Analysis Genetic alterations of ARHGAP9, including mutations, copy number variations, and structural variants, were analyzed across 1,955 samples from eight STAD sequencing studies using the cBioPortal platform ( www.cbioportal.org ) 12 . The TCGA Stomach Adenocarcinoma (GDC) dataset, comprising 375 samples, was utilized to assess alterations in ARHGAP9-associated genes, with results visualized using a heatmap. Kaplan-Meier analysis was performed to evaluate overall survival differences based on ARHGAP9 alteration status. Immune Infiltration Analysis Immune infiltration levels in ARHGAP9 high and low expression groups were assessed to clarify its role in the tumor immune microenvironment. The ESTIMATE algorithm was applied to quantify stromal and immune cell abundance within the tumor microenvironment 13 . CIBERSORT was employed to deconvolute immune cell fractions 14 . Single-sample gene set enrichment analysis (ssGSEA) was performed to evaluate the enrichment levels of various immune cell populations 15 . The correlation between ARHGAP9 expression and immune checkpoint genes was analyzed to predict potential responses to immunotherapy. Drug Sensitivity Analysis The Immunophenoscore (IPS) was employed to evaluate the association between ARHGAP9 expression and immunotherapy responsiveness. Differences in IPS between ARHGAP9 high and low expression groups were compared using the Wilcoxon rank-sum test. Additionally, drug sensitivity data from the GDSC ( https://www.cancerrxgene.org/ ) and CTRP ( http://portals.broadinstitute.org/ctrp/ ) databases were analyzed to identify associations with ARHGAP9 expression, and the results were visualized using Cytoscape. Furthermore, the R package 'pRRophetic' was used to estimate drug IC50 values and assess the predictive potential of ARHGAP9 for targeted therapy and chemotherapy response 16 . Single-cell RNA Sequencing Analysis ARHGAP9 expression across various immune cell types was analyzed using data obtained from the Human Protein Atlas (HPA) ( https://www.proteinatlas.org/ ). Furthermore, ARHGAP9 expression in single-cell populations of gastric tissue and its spatial localization were further investigated 17 . Cell Culture and shRNA Transfection The GES-1, SGC-7901, and MGC-803 cell lines were obtained and maintained in culture. GES-1 normal gastric mucosal epithelial cells were purchased from the Beijing Institute for Cancer Prevention. SGC-7901 cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai). MGC-803 human gastric adenocarcinoma (GAC) cells were purchased from the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. ARHGAP9-targeting siRNAs were designed and synthesized by Genomic Pharmaceuticals (Shanghai, China). The shRNA sequences targeting ARHGAP9 cDNA were as follows: Forward: 5'-GATCCGGCTACAATGCTATCCAGCCGTTCAAGAGACGGCTGGATAGCATTGTAGCCTTTTTTG-3', Reverse: 5'-AATTCAAAAAAGGCTACAATGCTATCCAGCCGTCTCTTGAACGGCTGGATAGCATTGTAGCCG-3'. shRNA was transfected into SGC-7901 and MGC-803 cells using lentiviral vectors at a final concentration of 200 nM. cDNA Synthesis Total RNA was extracted using the TransZol Up Plus RNA Kit. RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer. RNA integrity was evaluated by agarose gel electrophoresis. cDNA synthesis was performed using the PrimeScript™ RT Reagent Kit (Perfect Real Time, RR037A). Quantitative RT‒PCR Relative ARHGAP9 mRNA expression levels in GES-1, SGC-7901, and MGC-803 cells were quantified using quantitative real-time PCR (qRT-PCR). The primer sequences used were as follows: ARHGAP9 forward: 5ʹ-TGAGAATTCTGGCTACAATGCTATCCAGCCG-3ʹ, reverse: 5ʹ-CCACTCGAGATGACCGGAATGTTTTCTC-3ʹ; GAPDH forward: 5ʹ-TGAGAATTCTGGCTACAATGCTATCCAGCCG-3ʹ, reverse: 5ʹ-CCACTCGAGATGACCGGAAAATGTTTTCTC-3ʹ. Cell Proliferation Assay Forty-eight hours after transfection with ARHGAP9-targeting siRNA, 2,000 SGC-7901 or MGC-803 cells were seeded per well in 100 µL medium in 96-well plates. At 6 hours (day 0), and at 1, 3, and 5 days post-seeding, 10 µL of CCK-8 solution was added to each well and incubated at 37°C for 1 hour. Absorbance at 450 nm was measured using a SpectraMax 190 microplate reader (Molecular Devices). Blank controls contained medium only without cells. Cell proliferation was assessed by plotting absorbance at 450 nm over time for SGC-7901 and MGC-803 cells. Cell Migration and Invasion Assays Cell migration and invasion abilities of SGC-7901 and MGC-803 cells were evaluated using Transwell chambers. Matrigel was diluted at a ratio of 1:3 in serum-free medium. Thirty microliters of diluted Matrigel were evenly applied to the upper surface of the chamber membrane. Cells were resuspended in serum-free medium at a density of 5 × 10⁴ cells/mL, and 200 µL of the cell suspension was added to the upper chamber. The lower chambers were filled with 800 µL of medium supplemented with 10% fetal bovine serum (FBS). Following 48 hours of incubation, cells that had migrated through the membrane were counted under a microscope. Statistical differences were analyzed using an independent samples t-test to evaluate the functional impact of ARHGAP9 knockdown. Results Pan-Cancer Analysis and Expression Profiling of ARHGAP9 in Gastric Cancer Analysis of TCGA-GTEx data revealed that ARHGAP9 expression was significantly elevated in CHOL, ESCA, GBM, HNSC, KIRC, KIRP, and STAD compared to normal tissues (P < 0.05) (Fig. 1 A), whereas its expression was significantly reduced in COAD, LUSC, PAAD, and READ (P < 0.05) (Fig. 1 A). Paired tumor-adjacent tissue analysis showed significantly increased ARHGAP9 expression in KIRC, KIRP, and STAD (P < 0.05) (Fig. 1 B), and significantly reduced expression in BLCA, COAD, LUAD, and LUSC (P < 0.05) (Fig. 1 B). In STAD, ARHGAP9 expression was higher in tumor tissues compared to both normal and adjacent non-tumor tissues (Fig. 1 C-D). Differential expression analysis of the GEO datasets GSE54129 and GSE118916 further confirmed significant upregulation of ARHGAP9 in tumor tissues (Fig. 1 E-F). ARHGAP9 expression increased with tumor progression, with significant differences observed between T1 and T3/T4 stages (P < 0.05), and showed a positive correlation with T stage (Fig. 1 G). Although no significant differences were observed across N stages, Stage I showed a significant difference compared to Stage III (P < 0.05) (Fig. 1 H). Histological grading also revealed significant differences between G2 and G3 (P < 0.05) (Fig. 1 I-J). Taken together, these results indicate that ARHGAP9 expression increases with gastric cancer progression. UALCAN analysis revealed higher ARHGAP9 expression in microsatellite instability-low (MSI-L) tumors (Fig. 1 K), and primary tumors showed elevated promoter methylation levels (Fig. 1 L). Enrichment Analysis of ARHGAP9-Associated Differentially Expressed Genes Gastric cancer samples were stratified into high and low ARHGAP9 expression groups based on the median expression value. Differential expression analysis using thresholds of |log2 fold change| >1 and adjusted p-value < 0.05 identified 1,192 DEGs, including 812 upregulated and 380 downregulated genes in the high-expression group compared to the low-expression group (Supplementary Table 1). Gene Ontology (GO) enrichment analysis revealed that DEGs were primarily involved in immune response processes related to immune cell activation and regulation (Fig. 2 A). KEGG pathway analysis indicated significant enrichment in pathways such as Staphylococcus aureus infection, hematopoietic cell lineage, cytokine–cytokine receptor interaction, viral protein–cytokine interaction, and chemokine signaling (Fig. 2 B). GSEA further revealed significant enrichment in pathways related to keratinization, cornified envelope formation, and developmental biology (Fig. 2 C-D). Immune-related pathways, including CD22-mediated BCR regulation, antigen-activated BCR signaling, and BCR signaling, were also significantly enriched (Fig. 2 E-F). The top 10 significantly enriched pathways were visualized for further interpretation. Co-expression Analysis and PPI Network Construction The top 100 significantly upregulated and downregulated DEGs were selected for further analysis (Supplementary Table 2). A heatmap was generated to visualize the top five DEGs (Fig. 3 A). Spearman correlation analysis was performed to identify the top 100 positively and negatively co-expressed genes (Supplementary Table 3). The top five positively correlated genes were RASAL3, TRAF3IP3, SASH3, WAS, and ITGAL, while the top five negatively correlated genes were FAM166C, ERG28, CMTM8, EBP, and GGCT (Fig. 3 B). Venn diagram analysis revealed 29 genes that overlapped between the DEGs and co-expressed gene sets. The PPI network analysis yielded 26 nodes and 89 edges, which were visualized using Cytoscape (Fig. 3 C). MCODE analysis identified a key module with a score of 8.2, comprising 11 nodes and 41 edges (Fig. 3 D). The CytoHubba plugin, using MCC and DMNC algorithms, identified and visualized the top 10 hub genes (Fig. 3 E–F). Construction of ARHGAP9 ceRNA Network ARHGAP9-targeting miRNAs were predicted using miRDB (Target Score ≥ 50), TargetScan, and miRWalk (Supplementary Table 4). Intersection analysis using a Venn diagram identified three high-confidence miRNAs (Fig. 4 A). Analysis using starBase revealed a positive correlation between hsa-miR-133b and ARHGAP9 expression (Fig. 4 B), whereas hsa-miR-6884-5p and hsa-miR-485-5p showed negative correlations (Fig. 4 C–D). Target lncRNAs of the identified miRNAs were predicted using StarBase and LncACTdb, and consensus lncRNAs were selected for further analysis. Sankey diagrams and network plots were used to visualize the mRNA–miRNA–lncRNA interactions (Fig. 4 E–F). This ceRNA network provides insight into the potential regulatory mechanisms of ARHGAP9 in tumor progression. Genetic Alteration Analysis of ARHGAP9 Genetic alterations of ARHGAP9 in gastric cancer were analyzed using the cBioPortal platform. Among 1,955 samples across eight cohorts, 69 (4%) exhibited ARHGAP9 alterations, primarily amplification and missense mutations (Fig. 5 A). The frequencies and types of alterations across datasets were visualized (Fig. 5 B). In the TCGA STAD (GDC) dataset comprising 375 samples, mutation profiles of 29 ARHGAP9-associated genes were analyzed. ZNF831 exhibited the highest mutation frequency (18%), followed by GRB7, BMP7, MIEN1, and SIRPG (Fig. 5 C). GISTIC analysis revealed that copy number alterations were primarily concentrated in the shallow deletion and diploid groups (Fig. 5 D). Patients harboring ARHGAP9 alterations exhibited significantly worse overall survival compared to those without alterations (P < 0.05) (Fig. 5 E). Characterization of ARHGAP9 alterations provides critical insights into prognostic improvement. Immunocorrelation Analysis of ARHGAP9 Given the significant impact of differential ARHGAP9 expression on gastric cancer progression, we next investigated immune heterogeneity between expression groups to evaluate the influence of the immune microenvironment on tumor biology. First, ESTIMATE analysis revealed significantly elevated Stromal Score, Immune Score, and ESTIMATE Score in the high ARHGAP9 expression group (P < 0.001) (Fig. 6 A). These scores were significantly and positively correlated with ARHGAP9 expression levels (Fig. 6 B–D). Subsequently, CIBERSORT was used to estimate the global proportions of immune cell subsets (Fig. 6 E). The infiltration levels of CD8 + T cells and activated CD4 + memory T cells were significantly and positively correlated with ARHGAP9 expression (Fig. 6 G–H). Similarly, ssGSEA was applied to compare immune infiltration levels between the two groups. Significant differences were observed in the infiltration of aDCs, B cells, CD8 + T cells, and other immune cell types (P < 0.001), with higher infiltration levels in the high ARHGAP9 expression group (Fig. 6 I). Finally, we evaluated the expression levels of immune checkpoint molecules between the two groups. ARHGAP9 expression was significantly and positively correlated with multiple immune checkpoint molecules, including PDCD1 (PD-1), PDCD1LG2 (PD-L2), and CTLA4 (P < 0.001) (Fig. 6 J–K). These findings suggest that gastric cancer patients stratified by ARHGAP9 expression levels may potentially benefit from immune checkpoint blockade therapy. ARHGAP9 Evaluation of Response to Drug Therapy The IPS is a predictive biomarker for response to immune checkpoint inhibitors (ICIs). Therefore, we investigated the association between ARHGAP9 expression and IPS in gastric cancer patients. Significant differences in IPS values for PD-1 blockade were observed between ARHGAP9 high and low expression groups (P < 0.001) (Fig. 7 A). No significant difference was observed in IPS values for CTLA-4 blockade (Fig. 7 C). However, further analysis of GASTRIC CANCER patients treated with PD-1 or CTLA-4 inhibitors revealed that low ARHGAP9 expression was significantly associated with improved prognosis (P < 0.05) (Fig. 7 B, D). We further investigated the correlation between ARHGAP9 expression and drug sensitivity. In the CTRP dataset, the top three drugs that showed negative correlations with ARHGAP9 expression were 17-AAG (HSP90 inhibitor), docetaxel (microtubule depolymerization inhibitor), and I-BET-762 (BET inhibitor) (Fig. 7 E, Supplementary Table 5). Similarly, in the CTRP dataset, LY-2183240 (fatty acid amide hydrolase inhibitor), vincristine (tubulin-binding agent), and PX-12 (thioredoxin-1 inhibitor) showed the strongest negative correlations with ARHGAP9 expression (Fig. 7 G, Supplementary Table 6). Correlations between drug sensitivity and ARHGAP9 expression were visualized using network diagrams (Fig. 7 F, H). Furthermore, IC50 values for 198 drugs were estimated to assess sensitivity to chemotherapeutic and targeted agents across ARHGAP9 expression levels. Results indicated consistently higher drug sensitivity in the low ARHGAP9 expression group (Supplementary Table 7). The top 12 most sensitive agents were visualized, including ribociclib (CDK4/6 inhibitor), CZC24832 (PI3Kγ inhibitor), AZ960 (JAK2/STAT5 inhibitor), AMG-319 (PI3Kδ inhibitor), SB216763 (GSK-3β inhibitor), AZD8055 (mTOR inhibitor), entospletinib (SYK inhibitor), PRT062607 (SYK/JAK dual inhibitor), RVX-208 (BET inhibitor), mitoxantrone (DNA topoisomerase II inhibitor), PRIMA-1MET (p53 reactivator), and WZ4003 (EGFR mutation-selective inhibitor) (Fig. 7 I–T). These findings provide a novel therapeutic rationale for improving outcomes in gastric cancer patients. Single-cell Analysis of ARHGAP9 To characterize ARHGAP9 expression at the cellular level, analysis of the HPA dataset revealed elevated ARHGAP9 expression in immune cells, including neutrophils, eosinophils, and plasmacytoid dendritic cells (pDCs) (Fig. 8 A). Gastric tissue cell types were further clustered based on normalized transcripts per million (nTPM) expression levels. Cluster C-2, composed of T cells, exhibited higher ARHGAP9 expression (Fig. 8 B). Cell-type analysis of gastric tissue confirmed high ARHGAP9 enrichment in macrophages, T cells, and neutrophils. In contrast, gastric-associated cell types, including endothelial cells, enteroendocrine cells, chief cells, and parietal cells, showed low ARHGAP9 enrichment (Fig. 8 C). Additionally, heatmaps were generated to visualize ARHGAP9 and marker gene expression across gastric single-cell clusters using Mas-norm and Z-score normalization (Fig. 8 D, F). Similarly, a heatmap was used to illustrate ARHGAP9 and marker gene expression across gastric cell types (Fig. 8 E). Overexpression of ARHGAP9 in GAC cells ARHGAP9 expression levels in GAC cell lines (SGC-7901 and MGC-803) and normal gastric mucosal epithelial cells (GES-1) were quantified using qRT-PCR. The results showed that ARHGAP9 expression was significantly higher in SGC-7901 and MGC-803 cells compared to GES-1 cells (Fig. 9 A–B, P < 0.01). Knockdown of ARHGAP9 inhibits the biological function of GAC cells SGC-7901 and MGC-803 cells were transfected with lentiviral vectors expressing short hairpin RNA (shRNA) targeting ARHGAP9 (Fig. 9 C). Cell viability was assessed in the blank control, LV3-shNC (negative control), and LV3-ARHGAP9 (knockdown) groups using the CCK-8 assay. No significant difference in proliferation activity was observed between the blank control and LV3-shNC groups. In contrast, the LV3-ARHGAP9 group exhibited significantly reduced proliferation activity (P < 0.05) (Fig. 9 D–G). Transwell migration and invasion assays were performed to evaluate the effects of ARHGAP9 knockdown on the migratory and invasive capacities of MGC-803 cells. Results showed that the average number of LV3-ARHGAP9 cells that migrated to or invaded the lower chamber was significantly lower than that of the control groups (P < 0.05) (Fig. 9 H–K). Discussion Currently, surgical resection remains the primary treatment modality for gastric cancer, often combined with multimodal therapeutic strategies. Contemporary prognostic assessment primarily relies on clinical and pathological features; however, advances in molecular biology are opening new avenues for personalized therapeutic strategies. In this study, we conducted a comprehensive analysis of integrated expression profiles, molecular functions, and immune infiltration patterns of key biomarkers. We further investigated ARHGAP9-associated ceRNA network regulation, drug sensitivity, and single-cell multi-omics in gastric cancer, supported by molecular experimental validation, with the aim of providing novel insights into disease mechanisms and therapeutic strategies. ARHGAP9, which encodes Rho GAC 9, was initially discovered and reported by the Japanese scientist Yoichi Furukawa. It belongs to the Rho GTPase family 18 . This protein family has been established as critical regulators of cytoskeletal reorganization and cell polarity, and as key drivers of tumor cell proliferation and metastasis 19 , 20 . ARHGAP9, the focus of the present study, has been shown to exert regulatory functions in multiple cancer types 21 . Experimental studies by Sun et al. revealed that ARHGAP9 promotes gastric cancer proliferation, migration, and invasion, findings that are consistent with our own 22 . Our bioinformatics-based approach enables an in-depth analysis of ARHGAP9's molecular regulatory relationships, thereby offering new insights into its underlying mechanisms. Additionally, elevated expression of ARHGAP9 contributes to immunomodulation in acute myeloid leukemia and is associated with poor patient prognosis 23 . Notably, ARHGAP9 exhibits divergent functional roles across different tumor types. Overexpression of ARHGAP9 suppresses malignant progression in colorectal cancer by inhibiting the PI3K/AKT/mTOR signaling pathway 24 . Similarly, Song et al. reported that knockdown of ARHGAP9 promotes LUAD metastasis by activating the Wnt/β-catenin signaling pathway via DDK inhibition 9 . The pro-tumorigenic roles of Rho GTPases across various malignancies, along with their potential as anti-cancer targets, warrant rigorous future validation of causal relationships to elucidate context-specific mechanisms underlying oncogenesis or tumor suppression. Using multi-omics data, we investigated the competing endogenous RNA (ceRNA) network of ARHGAP9 to elucidate its regulatory mechanisms in gastric cancer, given that the impact of the lncRNA-miRNA-mRNA axis on gastric cancer is well established 25 , 26 . The binding interactions between miR-133b, miR-6884-5p, and miR-485-5p and various lncRNAs modulate ARHGAP9 expression levels; however, their regulatory roles in gastric cancer progression remain largely unexplored and require experimental validation. Similarly, the tumor immune microenvironment plays a critical role in tumor development, chemotherapy resistance, and patient prognosis. In this study, we found that DEGs between ARHGAP9-high and ARHGAP9-low groups were primarily enriched in immune-related processes. Moreover, samples with high ARHGAP9 expression exhibited significantly elevated immune scores, particularly for CD8 + T cells and memory-activated CD4 + T cells, whose infiltration levels were significantly correlated with ARHGAP9 expression 27 . Based on immune checkpoint expression profiles, tumors with high ARHGAP9 expression may be more responsive to immune checkpoint inhibitor therapy 28 , 29 . Analysis of treatment response indicated that gastric cancer patients with low ARHGAP9 expression who received PD-1 inhibitor therapy had a more favorable prognosis. Furthermore, drug sensitivity analysis demonstrated that samples with low ARHGAP9 expression exhibited enhanced drug responsiveness, which may improve prognostic management strategies for gastric cancer patients. However, this study has several limitations. First, although we validated the impact of ARHGAP9 on gastric cancer cell behaviors through in vitro experiments, the absence of in vivo studies precludes a comprehensive assessment of its roles in tumor growth and metastasis. Future studies should incorporate animal experiments to verify the functions and mechanisms of ARHGAP9 in physiological contexts. Moreover, this study primarily focused on the expression patterns and functional validation of ARHGAP9, without fully elucidating its molecular mechanisms in gastric cancer. Subsequent studies should investigate the interactions of ARHGAP9 with other signaling pathways and its regulatory mechanisms within the tumor microenvironment. Finally, although this study revealed the significance of ARHGAP9 in gastric cancer, its clinical translation faces substantial challenges. The translation of ARHGAP9 into a clinically applicable biomarker and the development of ARHGAP9-targeted therapeutics require further investigation. Future studies should integrate clinical specimens to analyze the correlations between ARHGAP9 expression and patients' clinical characteristics and treatment responses, thereby providing stronger evidence for personalized therapy in gastric cancer. Conclusion In summary, this multi-omics study establishes ARHGAP9 as a key driver of gastric cancer progression. Future development of ARHGAP9-targeted inhibitory strategies, targeted therapies, and immunotherapeutic approaches may contribute to improving the poor prognosis associated with gastric cancer. Abbreviations GAC Gastric adenocarcinoma GAP GTPase-activating protein MAPKs Mitogen-activated protein kinases TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus DEGs Differentially expressed genes GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes BP Biological processes CC Cellular components MF Molecular functions GSEA Gene Set Enrichment Analysis PPI Protein-protein interaction ssGSEA Single-sample gene set enrichment analysis IPS Immunophenoscore HPA Human Protein Atlas qRT-PCR quantitative real-time PCR FBS Fetal bovine serum ICIs Immune checkpoint inhibitors Declarations Acknowledgements We are grateful to the Joint Logistics Support Force 900th Hospital Basic Laboratory for the experimental study in this study. Author contributions LZQ: Conduct data management, investigations, methodologies, write original drafts, and create visualizations. WXH: Review and edit manuscripts and provide supervision. HWW: Investigated and verified. YS: Surveyed and provided resources. XXL: Manage the project, develop the methodology, and conceptualize the study. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability The data used in the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participat The data for this study were obtained from online public databases. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Supplementary Files SupplementaryTable4.xlsx SupplementaryTable6.xlsx SupplementaryTable7.xlsx SupplementaryTable5.xlsx SupplementaryTable3.xlsx SupplementaryTable2.xlsx SupplementaryTable1.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 07 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 06 Aug, 2025 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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09:28:38","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4266644,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/fe89d9671f02d47801d928ec.png"},{"id":97487676,"identity":"2e5cc440-730c-4c60-8127-944116871819","added_by":"auto","created_at":"2025-12-05 01:52:29","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13670282,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/1402ec778150a6adf02ef9eb.png"},{"id":97487669,"identity":"29ae0118-c44e-4ff0-8a5e-c9d931f248a3","added_by":"auto","created_at":"2025-12-05 01:52:29","extension":"xml","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99370,"visible":true,"origin":"","legend":"","description":"","filename":"f6a585cb29254d63a30714296cb71a8c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/1877e6b2b57950769b0bc516.xml"},{"id":97487673,"identity":"499378e7-1c1e-4b53-ac34-c2bbbbc58d26","added_by":"auto","created_at":"2025-12-05 01:52:29","extension":"html","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108855,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/c4d573c0efb6d8494ffb50cd.html"},{"id":97670834,"identity":"a1d40cd9-e15a-42d6-a7c7-75d9cafac308","added_by":"auto","created_at":"2025-12-08 09:31:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":695405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePan-cancer analysis of ARHGAP9 and expression analysis in gastric cancer.\u003c/strong\u003e (A) Pan-cancer analysis between tumor and normal samples. (B) Pan-cancer analysis between tumor and adjacent samples. (C-D) Expression analysis in TCGA-STAD. (E-F) Expression analysis in GSE54129 and GSE118916 datasets. (G-J) Expression analysis based on T stage, N stage, tumor stage, and histological grade. (K-L) Expression analysis of tumor protein and methylation levels.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/2f3dd3897b90e9872ac96cd3.png"},{"id":97487696,"identity":"812f74f9-d836-4daa-aad8-5322fe5e6770","added_by":"auto","created_at":"2025-12-05 01:52:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":846790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis of DEGs of ARHGAP9 between groups.\u003c/strong\u003e (A) GO analysis. (B) KEGG pathway analysis. (C-D) GSEA analysis of top 10 enriched pathways. (E-F) GSEA analysis of top 10 enriched pathways for immune-related pathways.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/3e236489b354116bb753d6c7.png"},{"id":97487630,"identity":"d94fa19b-ef5e-467b-a326-81937cffe607","added_by":"auto","created_at":"2025-12-05 01:52:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":901456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of ARHGAP9 and PPI network construction.\u003c/strong\u003e (A) Heatmap of top 5 significantly DEGs. (B) Heatmap of top 5 significantly correlated genes. (C) PPI network of overlapping genes. (D) Module network based on MCODE algorithm. (E-F) Module networks based on MCC and DMNC algorithms.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/e039d823daaaaf58f42cacc9.png"},{"id":97487631,"identity":"ae89641a-7863-46df-a5f1-488db3874b9b","added_by":"auto","created_at":"2025-12-05 01:52:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":920414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCeRNA network of ARHGAP9.\u003c/strong\u003e (A) Venn diagram of three databases for miRNA prediction. (B-D) Expression correlation analysis between ARHGAP9 and miRNAs. (E) Sankey diagram of mRNA-miRNA-lncRNA interactions. (F) ceRNA network diagram of mRNA-miRNA-lncRNA interactions.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/66e9a0aa51a7b719dcdb1e4c.png"},{"id":97670515,"identity":"366538e3-4d3f-4fc9-a879-b8ad5115859a","added_by":"auto","created_at":"2025-12-08 09:30:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":719095,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic alteration analysis of ARHGAP9.\u003c/strong\u003e (A) Genetic mutation analysis across all datasets. (B) Bar plot analysis of mutation frequency in 8 datasets. (C) Genetic mutation analysis of differentially correlated genes in Stomach Adenocarcinoma (TCGA, GDC) dataset. (D) Copy number variation analyzed by GISTIC algorithm. (E) Prognostic survival analysis of ARHGAP9-mutated samples.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/6c61c032bdca819a00fbc96d.png"},{"id":97487629,"identity":"c28970e9-4a04-4f0b-9868-fad5df8f41ca","added_by":"auto","created_at":"2025-12-05 01:52:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1211179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis of ARHGAP9. \u003c/strong\u003e(A) Box plot of ESTIMATE analysis between groups. (B-D) Scatter plots of ESTIMATE scores versus ARHGAP9 expression. (E) Heatmap of immune cell proportion distribution by CIBERSORT analysis. (F) Box plot of CIBERSORT analysis between groups. (G-H) Scatter plots of infiltration abundance of selected immune cells versus ARHGAP9 expression. (I) Box plot of ssGSEA analysis between groups. (J) Heatmap of ARHGAP9 and common immune checkpoint expressions. (K) Immune checkpoint analysis between groups. *, **, *** indicate statistical significance at P\u0026lt;0.05, P\u0026lt;0.01, and P\u0026lt;0.001 levels, respectively.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/1414c37d752f6241deac0f03.png"},{"id":97487666,"identity":"ed0b3dba-2a3b-4bed-ab7a-858c9cbcd5c9","added_by":"auto","created_at":"2025-12-05 01:52:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1320167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of ARHGAP9 in drug treatment response. \u003c/strong\u003e(A,C) Box plots of IPS scores for ARHGAP9 with IPS-CTLA4 and IPS-PD1 blockers between groups. (B,D) Prognostic differences in ARHGAP9 expression between groups receiving IPS-CTLA4 and IPS-PD1 blockers. (E,G) Drug sensitivity analysis of ARHGAP9 expression in GDSC and CTPR datasets. (F,H) Drug network diagrams associated with ARHGAP9 expression in GDSC and CTPR datasets. (I-T) Sensitivity analysis between ARHGAP9 expression and various drugs across groups. *, **, *** indicate statistical significance at P\u0026lt;0.05, P\u0026lt;0.01, and P\u0026lt;0.001 levels, respectively.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/d50f4de4330f037d610b7779.png"},{"id":97487650,"identity":"af09ceb5-e1de-4701-aa3a-d596e67a1266","added_by":"auto","created_at":"2025-12-05 01:52:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":779170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell analysis of ARHGAP9.\u003c/strong\u003e (A) Expression levels of ARHGAP9 in immune cells. (B) Expression levels of ARHGAP9 in different gastric cell clusters. (C) Expression levels of ARHGAP9 in different gastric tissue cell types. (D,F) Expression levels of ARHGAP9 and marker genes in gastric cell clusters using Mas-norm and Z-score methods. (E) Expression levels of ARHGAP9 and marker genes in different gastric tissue cell types. Expression thresholds: Enhanced (nTPM ≥4), Low specificity (nTPM ≥1), Undetected (nTPM \u0026lt;1).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/c53db83a0d1029c9ff228ecb.png"},{"id":97487634,"identity":"62b9262e-8b41-49f6-a0fe-fb9f168848ca","added_by":"auto","created_at":"2025-12-05 01:52:27","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1757398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of ARHGAP9 on biological functions of GAC cells.\u003c/strong\u003e (A) Relative expression of ARHGAP9 in GAC cells versus normal gastric mucosal cells. (B) ARHGAP9 expression in GAC cells after LV3-ARHGAP9 transduction. (C) CCK-8 proliferation assay in GAC cells. (D) ARHGAP9 promotes proliferation of GAC cells. (E-G) ARHGAP9 enhances migration of GAC cells. (H-J) ARHGAP9 facilitates invasion of GAC cells.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/4e6179d73e5b5c059ffbfa25.png"},{"id":97677797,"identity":"4cfbfd5c-1962-41f8-912c-ea506409f62a","added_by":"auto","created_at":"2025-12-08 09:54:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8984952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/a1d92125-c448-45d0-b2ce-6a5de4c6f6a8.pdf"},{"id":97670732,"identity":"ce89d2f1-097d-4aef-b04c-17d0eeb7e0b5","added_by":"auto","created_at":"2025-12-08 09:31:13","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35013,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/2b0ceac85bcd3df2e12c32c4.xlsx"},{"id":97487653,"identity":"5f175e28-985c-44b2-9f63-f69d4b632906","added_by":"auto","created_at":"2025-12-05 01:52:28","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21328,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/c1b63ba1a528ee73bd81353b.xlsx"},{"id":97487628,"identity":"c262234c-1eb8-49eb-87c5-d640a94eb8e9","added_by":"auto","created_at":"2025-12-05 01:52:26","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16234,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/8037e01243fffae70d5b8718.xlsx"},{"id":97487663,"identity":"6f4292d0-9d82-42b6-8825-a14f700c7e06","added_by":"auto","created_at":"2025-12-05 01:52:28","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":32869,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/b33f211aaa26ef46ac625c1d.xlsx"},{"id":97669096,"identity":"d9b5e9ae-e99c-4b87-b07f-44b1269a62e1","added_by":"auto","created_at":"2025-12-08 09:27:12","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":31982,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/082b72e76c0e8e300b752cee.xlsx"},{"id":97487664,"identity":"df23c8e6-2a57-44e9-9ba4-5a0b46969350","added_by":"auto","created_at":"2025-12-05 01:52:28","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":32856,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/9da2b6daca02120e0d78e99e.xlsx"},{"id":97669084,"identity":"a518c9fd-d5fc-423d-a663-ddb788cc9e9b","added_by":"auto","created_at":"2025-12-08 09:27:11","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2353891,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7311091/v1/7531d557c368b32b97b1bfaf.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive bioinformatics analysis and experimental validation revealed that high-expression ARHGAP9 affected the progression of gastric cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer is among the most common malignant tumors worldwide, with an incidence and mortality rate both ranking fifth among all cancers, representing a significant public health challenge and imposing a substantial disease burden \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Due to the often nonspecific and subtle symptoms in early stages, the majority of patients are diagnosed at an advanced stage, thereby missing the optimal treatment window and leading to a poor prognosis, with a postoperative 5-year survival rate of less than 30%\u003csup\u003e2\u003c/sup\u003e. The progression of gastric cancer is a complex process that involves genetic mutations, epigenetic modifications, and dynamic alterations in the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Currently, the mainstay of gastric cancer treatment remains surgical resection, often combined with multimodal approaches including chemotherapy, radiotherapy, and targeted therapy\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, the inherent heterogeneity and complexity of gastric cancer impose significant limitations on current therapeutic strategies. Postoperative recurrence and metastasis are the leading causes of treatment failure, and chemotherapy resistance, along with the limited applicability of targeted therapies, present major clinical challenges. Therefore, a comprehensive understanding of the molecular mechanisms driving gastric cancer development and progression, as well as the identification of novel prognostic biomarkers and therapeutic targets, is essential to improve patient outcomes.\u003c/p\u003e\u003cp\u003eARHGAP9 is located on human chromosome 12q13.3 and demonstrates significant Rho GTPase-activating protein (GAP) activity toward multiple Rho family GTPases in vitro, facilitating the conversion of GTP to GDP, and plays a role in regulating the adhesion of hematopoietic cells to the extracellular matrix. ARHGAP9 has been identified as a novel scaffold protein for mitogen-activated protein kinases (MAPKs)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Moreover, studies have confirmed that ARHGAP9 interacts with Erk2 and p38α, thereby inhibiting their activation. As a key MAPK, Erk2 activation and subsequent MAPK/ERK2 phosphorylation are essential for cancer cell proliferation and tumor development\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Activation of the ERK2 pathway contributes to tumor progression in various human cancers, including lung adenocarcinoma\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, liver cancer \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and renal cancer\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and significantly influences patient prognosis.\u003c/p\u003e\u003cp\u003eThis study aims to characterize ARHGAP9 in gastric cancer through comprehensive bioinformatics analysis, multi-omics validation, and functional experimental verification, with the goal of elucidating its role in tumor progression and providing valuable insights for tumor immunotherapy and prognostic assessment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eExpression Analysis\u003c/h2\u003e\u003cp\u003ePan-cancer expression analysis was performed using TPM-normalized RNA-seq data from 33 cancer types, downloaded and curated from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differential expression analysis was conducted on TCGA-STAD samples, excluding samples lacking clinical information or duplicates, resulting in a final cohort of 375 tumor samples and 32 adjacent normal controls. Two independent expression datasets (GSE54129 and GSE118916) were obtained from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and used for external validation. ARHGAP9 expression levels across different tumor stages and histological grades were analyzed based on corresponding clinical annotations. Protein expression and promoter methylation levels of ARHGAP9 in STAD were further investigated using the UALCAN platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://resource.path.uab.edu/\u003c/span\u003e\u003cspan address=\"http://resource.path.uab.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnrichment Analysis\u003c/h3\u003e\n\u003cp\u003eTCGA-STAD tumor samples were stratified into high and low ARHGAP9 expression groups based on the median expression value. Differentially expressed genes (DEGs) were identified using thresholds of adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2 fold change| \u0026gt;1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler package in R. GO analysis included three categories: biological processes (BP), cellular components (CC), and molecular functions (MF). Gene Set Enrichment Analysis (GSEA) was conducted using the curated gene set c2.cp.all.v2022.1.Hs.symbols.gmt.\u003c/p\u003e\n\u003ch3\u003eCo-expression Analysis\u003c/h3\u003e\n\u003cp\u003eGenes significantly co-expressed with ARHGAP9 (adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; |Spearman correlation coefficient| \u0026gt;0.5) were identified, and the top 100 positively and negatively correlated genes were selected for further analysis. In addition, a heatmap was generated to visualize the top 100 most significantly upregulated and downregulated DEGs.\u003c/p\u003e\n\u003ch3\u003ePPI Network Analysis\u003c/h3\u003e\n\u003cp\u003eOverlapping genes between DEGs and co-expressed gene sets were selected to investigate ARHGAP9-related protein interactions. An ARHGAP9-centered protein-protein interaction (PPI) network was constructed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and visualized using Cytoscape. The most significant network module was identified using MCODE analysis with the following parameters: degree cutoff\u0026thinsp;=\u0026thinsp;2, maximum depth\u0026thinsp;=\u0026thinsp;100, k-core\u0026thinsp;=\u0026thinsp;2, and node score cutoff\u0026thinsp;=\u0026thinsp;0.2. Hub genes were identified using the DMNC algorithm in CytoHubba, and the top 10 genes were selected for further analysis.\u003c/p\u003e\n\u003ch3\u003eCeRNA Network Construction\u003c/h3\u003e\n\u003cp\u003ePotential ARHGAP9-targeting miRNAs were predicted by integrating results from three databases: miRDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirdb.org/\u003c/span\u003e\u003cspan address=\"https://mirdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), TargetScan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.targetscan.org/\u003c/span\u003e\u003cspan address=\"http://www.targetscan.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and miRWalk (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The starBase platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/\u003c/span\u003e\u003cspan address=\"http://starbase.sysu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to visualize miRNA-ARHGAP9 interactions based on TCGA data. Target lncRNAs of the predicted miRNAs were identified, and the ARHGAP9-miRNA-lncRNA network was visualized using Sankey diagrams and Cytoscape.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGenetic Alteration Analysis\u003c/h2\u003e\u003cp\u003eGenetic alterations of ARHGAP9, including mutations, copy number variations, and structural variants, were analyzed across 1,955 samples from eight STAD sequencing studies using the cBioPortal platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://portal.gdc.cancer.gov\" target=\"_blank\"\u003ewww.cbioportal.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The TCGA Stomach Adenocarcinoma (GDC) dataset, comprising 375 samples, was utilized to assess alterations in ARHGAP9-associated genes, with results visualized using a heatmap. Kaplan-Meier analysis was performed to evaluate overall survival differences based on ARHGAP9 alteration status.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImmune Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eImmune infiltration levels in ARHGAP9 high and low expression groups were assessed to clarify its role in the tumor immune microenvironment. The ESTIMATE algorithm was applied to quantify stromal and immune cell abundance within the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. CIBERSORT was employed to deconvolute immune cell fractions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Single-sample gene set enrichment analysis (ssGSEA) was performed to evaluate the enrichment levels of various immune cell populations\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The correlation between ARHGAP9 expression and immune checkpoint genes was analyzed to predict potential responses to immunotherapy.\u003c/p\u003e\n\u003ch3\u003eDrug Sensitivity Analysis\u003c/h3\u003e\n\u003cp\u003eThe Immunophenoscore (IPS) was employed to evaluate the association between ARHGAP9 expression and immunotherapy responsiveness. Differences in IPS between ARHGAP9 high and low expression groups were compared using the Wilcoxon rank-sum test. Additionally, drug sensitivity data from the GDSC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CTRP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://portals.broadinstitute.org/ctrp/\u003c/span\u003e\u003cspan address=\"http://portals.broadinstitute.org/ctrp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were analyzed to identify associations with ARHGAP9 expression, and the results were visualized using Cytoscape. Furthermore, the R package 'pRRophetic' was used to estimate drug IC50 values and assess the predictive potential of ARHGAP9 for targeted therapy and chemotherapy response\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSingle-cell RNA Sequencing Analysis\u003c/h2\u003e\u003cp\u003eARHGAP9 expression across various immune cell types was analyzed using data obtained from the Human Protein Atlas (HPA) (\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 ). Furthermore, ARHGAP9 expression in single-cell populations of gastric tissue and its spatial localization were further investigated\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCell Culture and shRNA Transfection\u003c/h2\u003e\u003cp\u003eThe GES-1, SGC-7901, and MGC-803 cell lines were obtained and maintained in culture. GES-1 normal gastric mucosal epithelial cells were purchased from the Beijing Institute for Cancer Prevention. SGC-7901 cells were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai). MGC-803 human gastric adenocarcinoma (GAC) cells were purchased from the Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. ARHGAP9-targeting siRNAs were designed and synthesized by Genomic Pharmaceuticals (Shanghai, China). The shRNA sequences targeting ARHGAP9 cDNA were as follows: Forward: 5'-GATCCGGCTACAATGCTATCCAGCCGTTCAAGAGACGGCTGGATAGCATTGTAGCCTTTTTTG-3', Reverse: 5'-AATTCAAAAAAGGCTACAATGCTATCCAGCCGTCTCTTGAACGGCTGGATAGCATTGTAGCCG-3'. shRNA was transfected into SGC-7901 and MGC-803 cells using lentiviral vectors at a final concentration of 200 nM.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ecDNA Synthesis\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted using the TransZol Up Plus RNA Kit. RNA concentration and purity were assessed using a NanoDrop 2000 spectrophotometer. RNA integrity was evaluated by agarose gel electrophoresis. cDNA synthesis was performed using the PrimeScript\u0026trade; RT Reagent Kit (Perfect Real Time, RR037A).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative RT‒PCR\u003c/h2\u003e\u003cp\u003eRelative ARHGAP9 mRNA expression levels in GES-1, SGC-7901, and MGC-803 cells were quantified using quantitative real-time PCR (qRT-PCR). The primer sequences used were as follows: ARHGAP9 forward: 5ʹ-TGAGAATTCTGGCTACAATGCTATCCAGCCG-3ʹ, reverse: 5ʹ-CCACTCGAGATGACCGGAATGTTTTCTC-3ʹ; GAPDH forward: 5ʹ-TGAGAATTCTGGCTACAATGCTATCCAGCCG-3ʹ, reverse: 5ʹ-CCACTCGAGATGACCGGAAAATGTTTTCTC-3ʹ.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCell Proliferation Assay\u003c/h2\u003e\u003cp\u003eForty-eight hours after transfection with ARHGAP9-targeting siRNA, 2,000 SGC-7901 or MGC-803 cells were seeded per well in 100 \u0026micro;L medium in 96-well plates. At 6 hours (day 0), and at 1, 3, and 5 days post-seeding, 10 \u0026micro;L of CCK-8 solution was added to each well and incubated at 37\u0026deg;C for 1 hour. Absorbance at 450 nm was measured using a SpectraMax 190 microplate reader (Molecular Devices). Blank controls contained medium only without cells. Cell proliferation was assessed by plotting absorbance at 450 nm over time for SGC-7901 and MGC-803 cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCell Migration and Invasion Assays\u003c/h2\u003e\u003cp\u003eCell migration and invasion abilities of SGC-7901 and MGC-803 cells were evaluated using Transwell chambers. Matrigel was diluted at a ratio of 1:3 in serum-free medium. Thirty microliters of diluted Matrigel were evenly applied to the upper surface of the chamber membrane. Cells were resuspended in serum-free medium at a density of 5 \u0026times; 10⁴ cells/mL, and 200 \u0026micro;L of the cell suspension was added to the upper chamber. The lower chambers were filled with 800 \u0026micro;L of medium supplemented with 10% fetal bovine serum (FBS). Following 48 hours of incubation, cells that had migrated through the membrane were counted under a microscope. Statistical differences were analyzed using an independent samples t-test to evaluate the functional impact of ARHGAP9 knockdown.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003ePan-Cancer Analysis and Expression Profiling of ARHGAP9 in Gastric Cancer\u003c/h2\u003e\u003cp\u003eAnalysis of TCGA-GTEx data revealed that ARHGAP9 expression was significantly elevated in CHOL, ESCA, GBM, HNSC, KIRC, KIRP, and STAD compared to normal tissues (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), whereas its expression was significantly reduced in COAD, LUSC, PAAD, and READ (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Paired tumor-adjacent tissue analysis showed significantly increased ARHGAP9 expression in KIRC, KIRP, and STAD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and significantly reduced expression in BLCA, COAD, LUAD, and LUSC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In STAD, ARHGAP9 expression was higher in tumor tissues compared to both normal and adjacent non-tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D). Differential expression analysis of the GEO datasets GSE54129 and GSE118916 further confirmed significant upregulation of ARHGAP9 in tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F). ARHGAP9 expression increased with tumor progression, with significant differences observed between T1 and T3/T4 stages (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and showed a positive correlation with T stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Although no significant differences were observed across N stages, Stage I showed a significant difference compared to Stage III (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Histological grading also revealed significant differences between G2 and G3 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI-J). Taken together, these results indicate that ARHGAP9 expression increases with gastric cancer progression. UALCAN analysis revealed higher ARHGAP9 expression in microsatellite instability-low (MSI-L) tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK), and primary tumors showed elevated promoter methylation levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eL).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEnrichment Analysis of ARHGAP9-Associated Differentially Expressed Genes\u003c/h2\u003e\u003cp\u003eGastric cancer samples were stratified into high and low ARHGAP9 expression groups based on the median expression value. Differential expression analysis using thresholds of |log2 fold change| \u0026gt;1 and adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 identified 1,192 DEGs, including 812 upregulated and 380 downregulated genes in the high-expression group compared to the low-expression group (Supplementary Table\u0026nbsp;1). Gene Ontology (GO) enrichment analysis revealed that DEGs were primarily involved in immune response processes related to immune cell activation and regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). KEGG pathway analysis indicated significant enrichment in pathways such as \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infection, hematopoietic cell lineage, cytokine\u0026ndash;cytokine receptor interaction, viral protein\u0026ndash;cytokine interaction, and chemokine signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). GSEA further revealed significant enrichment in pathways related to keratinization, cornified envelope formation, and developmental biology (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Immune-related pathways, including CD22-mediated BCR regulation, antigen-activated BCR signaling, and BCR signaling, were also significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). The top 10 significantly enriched pathways were visualized for further interpretation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCo-expression Analysis and PPI Network Construction\u003c/h2\u003e\u003cp\u003eThe top 100 significantly upregulated and downregulated DEGs were selected for further analysis (Supplementary Table\u0026nbsp;2). A heatmap was generated to visualize the top five DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Spearman correlation analysis was performed to identify the top 100 positively and negatively co-expressed genes (Supplementary Table\u0026nbsp;3). The top five positively correlated genes were RASAL3, TRAF3IP3, SASH3, WAS, and ITGAL, while the top five negatively correlated genes were FAM166C, ERG28, CMTM8, EBP, and GGCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Venn diagram analysis revealed 29 genes that overlapped between the DEGs and co-expressed gene sets. The PPI network analysis yielded 26 nodes and 89 edges, which were visualized using Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). MCODE analysis identified a key module with a score of 8.2, comprising 11 nodes and 41 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The CytoHubba plugin, using MCC and DMNC algorithms, identified and visualized the top 10 hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u0026ndash;F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of ARHGAP9 ceRNA Network\u003c/h2\u003e\u003cp\u003eARHGAP9-targeting miRNAs were predicted using miRDB (Target Score\u0026thinsp;\u0026ge;\u0026thinsp;50), TargetScan, and miRWalk (Supplementary Table\u0026nbsp;4). Intersection analysis using a Venn diagram identified three high-confidence miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Analysis using starBase revealed a positive correlation between hsa-miR-133b and ARHGAP9 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), whereas hsa-miR-6884-5p and hsa-miR-485-5p showed negative correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u0026ndash;D). Target lncRNAs of the identified miRNAs were predicted using StarBase and LncACTdb, and consensus lncRNAs were selected for further analysis. Sankey diagrams and network plots were used to visualize the mRNA\u0026ndash;miRNA\u0026ndash;lncRNA interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u0026ndash;F). This ceRNA network provides insight into the potential regulatory mechanisms of ARHGAP9 in tumor progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eGenetic Alteration Analysis of ARHGAP9\u003c/h2\u003e\u003cp\u003eGenetic alterations of ARHGAP9 in gastric cancer were analyzed using the cBioPortal platform. Among 1,955 samples across eight cohorts, 69 (4%) exhibited ARHGAP9 alterations, primarily amplification and missense mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The frequencies and types of alterations across datasets were visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In the TCGA STAD (GDC) dataset comprising 375 samples, mutation profiles of 29 ARHGAP9-associated genes were analyzed. ZNF831 exhibited the highest mutation frequency (18%), followed by GRB7, BMP7, MIEN1, and SIRPG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). GISTIC analysis revealed that copy number alterations were primarily concentrated in the shallow deletion and diploid groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Patients harboring ARHGAP9 alterations exhibited significantly worse overall survival compared to those without alterations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Characterization of ARHGAP9 alterations provides critical insights into prognostic improvement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eImmunocorrelation Analysis of ARHGAP9\u003c/h2\u003e\u003cp\u003eGiven the significant impact of differential ARHGAP9 expression on gastric cancer progression, we next investigated immune heterogeneity between expression groups to evaluate the influence of the immune microenvironment on tumor biology. First, ESTIMATE analysis revealed significantly elevated Stromal Score, Immune Score, and ESTIMATE Score in the high ARHGAP9 expression group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). These scores were significantly and positively correlated with ARHGAP9 expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u0026ndash;D). Subsequently, CIBERSORT was used to estimate the global proportions of immune cell subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). The infiltration levels of CD8\u0026thinsp;+\u0026thinsp;T cells and activated CD4\u0026thinsp;+\u0026thinsp;memory T cells were significantly and positively correlated with ARHGAP9 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG\u0026ndash;H). Similarly, ssGSEA was applied to compare immune infiltration levels between the two groups. Significant differences were observed in the infiltration of aDCs, B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and other immune cell types (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher infiltration levels in the high ARHGAP9 expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). Finally, we evaluated the expression levels of immune checkpoint molecules between the two groups. ARHGAP9 expression was significantly and positively correlated with multiple immune checkpoint molecules, including PDCD1 (PD-1), PDCD1LG2 (PD-L2), and CTLA4 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ\u0026ndash;K). These findings suggest that gastric cancer patients stratified by ARHGAP9 expression levels may potentially benefit from immune checkpoint blockade therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eARHGAP9 Evaluation of Response to Drug Therapy\u003c/h2\u003e\u003cp\u003eThe IPS is a predictive biomarker for response to immune checkpoint inhibitors (ICIs). Therefore, we investigated the association between ARHGAP9 expression and IPS in gastric cancer patients. Significant differences in IPS values for PD-1 blockade were observed between ARHGAP9 high and low expression groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). No significant difference was observed in IPS values for CTLA-4 blockade (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). However, further analysis of GASTRIC CANCER patients treated with PD-1 or CTLA-4 inhibitors revealed that low ARHGAP9 expression was significantly associated with improved prognosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, D). We further investigated the correlation between ARHGAP9 expression and drug sensitivity. In the CTRP dataset, the top three drugs that showed negative correlations with ARHGAP9 expression were 17-AAG (HSP90 inhibitor), docetaxel (microtubule depolymerization inhibitor), and I-BET-762 (BET inhibitor) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, Supplementary Table\u0026nbsp;5). Similarly, in the CTRP dataset, LY-2183240 (fatty acid amide hydrolase inhibitor), vincristine (tubulin-binding agent), and PX-12 (thioredoxin-1 inhibitor) showed the strongest negative correlations with ARHGAP9 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG, Supplementary Table\u0026nbsp;6). Correlations between drug sensitivity and ARHGAP9 expression were visualized using network diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, H). Furthermore, IC50 values for 198 drugs were estimated to assess sensitivity to chemotherapeutic and targeted agents across ARHGAP9 expression levels. Results indicated consistently higher drug sensitivity in the low ARHGAP9 expression group (Supplementary Table\u0026nbsp;7). The top 12 most sensitive agents were visualized, including ribociclib (CDK4/6 inhibitor), CZC24832 (PI3Kγ inhibitor), AZ960 (JAK2/STAT5 inhibitor), AMG-319 (PI3Kδ inhibitor), SB216763 (GSK-3β inhibitor), AZD8055 (mTOR inhibitor), entospletinib (SYK inhibitor), PRT062607 (SYK/JAK dual inhibitor), RVX-208 (BET inhibitor), mitoxantrone (DNA topoisomerase II inhibitor), PRIMA-1MET (p53 reactivator), and WZ4003 (EGFR mutation-selective inhibitor) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI\u0026ndash;T). These findings provide a novel therapeutic rationale for improving outcomes in gastric cancer patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eSingle-cell Analysis of ARHGAP9\u003c/h2\u003e\u003cp\u003eTo characterize ARHGAP9 expression at the cellular level, analysis of the HPA dataset revealed elevated ARHGAP9 expression in immune cells, including neutrophils, eosinophils, and plasmacytoid dendritic cells (pDCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Gastric tissue cell types were further clustered based on normalized transcripts per million (nTPM) expression levels. Cluster C-2, composed of T cells, exhibited higher ARHGAP9 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Cell-type analysis of gastric tissue confirmed high ARHGAP9 enrichment in macrophages, T cells, and neutrophils. In contrast, gastric-associated cell types, including endothelial cells, enteroendocrine cells, chief cells, and parietal cells, showed low ARHGAP9 enrichment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Additionally, heatmaps were generated to visualize ARHGAP9 and marker gene expression across gastric single-cell clusters using Mas-norm and Z-score normalization (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, F). Similarly, a heatmap was used to illustrate ARHGAP9 and marker gene expression across gastric cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eOverexpression of ARHGAP9 in GAC cells\u003c/h2\u003e\u003cp\u003eARHGAP9 expression levels in GAC cell lines (SGC-7901 and MGC-803) and normal gastric mucosal epithelial cells (GES-1) were quantified using qRT-PCR. The results showed that ARHGAP9 expression was significantly higher in SGC-7901 and MGC-803 cells compared to GES-1 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA\u0026ndash;B, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eKnockdown of ARHGAP9 inhibits the biological function of GAC cells\u003c/h2\u003e\u003cp\u003eSGC-7901 and MGC-803 cells were transfected with lentiviral vectors expressing short hairpin RNA (shRNA) targeting ARHGAP9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Cell viability was assessed in the blank control, LV3-shNC (negative control), and LV3-ARHGAP9 (knockdown) groups using the CCK-8 assay. No significant difference in proliferation activity was observed between the blank control and LV3-shNC groups. In contrast, the LV3-ARHGAP9 group exhibited significantly reduced proliferation activity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD\u0026ndash;G). Transwell migration and invasion assays were performed to evaluate the effects of ARHGAP9 knockdown on the migratory and invasive capacities of MGC-803 cells. Results showed that the average number of LV3-ARHGAP9 cells that migrated to or invaded the lower chamber was significantly lower than that of the control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH\u0026ndash;K).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, surgical resection remains the primary treatment modality for gastric cancer, often combined with multimodal therapeutic strategies. Contemporary prognostic assessment primarily relies on clinical and pathological features; however, advances in molecular biology are opening new avenues for personalized therapeutic strategies. In this study, we conducted a comprehensive analysis of integrated expression profiles, molecular functions, and immune infiltration patterns of key biomarkers. We further investigated ARHGAP9-associated ceRNA network regulation, drug sensitivity, and single-cell multi-omics in gastric cancer, supported by molecular experimental validation, with the aim of providing novel insights into disease mechanisms and therapeutic strategies.\u003c/p\u003e\u003cp\u003eARHGAP9, which encodes Rho GAC 9, was initially discovered and reported by the Japanese scientist Yoichi Furukawa. It belongs to the Rho GTPase family\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This protein family has been established as critical regulators of cytoskeletal reorganization and cell polarity, and as key drivers of tumor cell proliferation and metastasis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. ARHGAP9, the focus of the present study, has been shown to exert regulatory functions in multiple cancer types\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Experimental studies by Sun et al. revealed that ARHGAP9 promotes gastric cancer proliferation, migration, and invasion, findings that are consistent with our own\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Our bioinformatics-based approach enables an in-depth analysis of ARHGAP9's molecular regulatory relationships, thereby offering new insights into its underlying mechanisms. Additionally, elevated expression of ARHGAP9 contributes to immunomodulation in acute myeloid leukemia and is associated with poor patient prognosis\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Notably, ARHGAP9 exhibits divergent functional roles across different tumor types. Overexpression of ARHGAP9 suppresses malignant progression in colorectal cancer by inhibiting the PI3K/AKT/mTOR signaling pathway\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Similarly, Song et al. reported that knockdown of ARHGAP9 promotes LUAD metastasis by activating the Wnt/β-catenin signaling pathway via DDK inhibition\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The pro-tumorigenic roles of Rho GTPases across various malignancies, along with their potential as anti-cancer targets, warrant rigorous future validation of causal relationships to elucidate context-specific mechanisms underlying oncogenesis or tumor suppression.\u003c/p\u003e\u003cp\u003eUsing multi-omics data, we investigated the competing endogenous RNA (ceRNA) network of ARHGAP9 to elucidate its regulatory mechanisms in gastric cancer, given that the impact of the lncRNA-miRNA-mRNA axis on gastric cancer is well established\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The binding interactions between miR-133b, miR-6884-5p, and miR-485-5p and various lncRNAs modulate ARHGAP9 expression levels; however, their regulatory roles in gastric cancer progression remain largely unexplored and require experimental validation. Similarly, the tumor immune microenvironment plays a critical role in tumor development, chemotherapy resistance, and patient prognosis. In this study, we found that DEGs between ARHGAP9-high and ARHGAP9-low groups were primarily enriched in immune-related processes. Moreover, samples with high ARHGAP9 expression exhibited significantly elevated immune scores, particularly for CD8\u0026thinsp;+\u0026thinsp;T cells and memory-activated CD4\u0026thinsp;+\u0026thinsp;T cells, whose infiltration levels were significantly correlated with ARHGAP9 expression\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Based on immune checkpoint expression profiles, tumors with high ARHGAP9 expression may be more responsive to immune checkpoint inhibitor therapy\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Analysis of treatment response indicated that gastric cancer patients with low ARHGAP9 expression who received PD-1 inhibitor therapy had a more favorable prognosis. Furthermore, drug sensitivity analysis demonstrated that samples with low ARHGAP9 expression exhibited enhanced drug responsiveness, which may improve prognostic management strategies for gastric cancer patients.\u003c/p\u003e\u003cp\u003eHowever, this study has several limitations. First, although we validated the impact of ARHGAP9 on gastric cancer cell behaviors through in vitro experiments, the absence of in vivo studies precludes a comprehensive assessment of its roles in tumor growth and metastasis. Future studies should incorporate animal experiments to verify the functions and mechanisms of ARHGAP9 in physiological contexts. Moreover, this study primarily focused on the expression patterns and functional validation of ARHGAP9, without fully elucidating its molecular mechanisms in gastric cancer. Subsequent studies should investigate the interactions of ARHGAP9 with other signaling pathways and its regulatory mechanisms within the tumor microenvironment. Finally, although this study revealed the significance of ARHGAP9 in gastric cancer, its clinical translation faces substantial challenges. The translation of ARHGAP9 into a clinically applicable biomarker and the development of ARHGAP9-targeted therapeutics require further investigation. Future studies should integrate clinical specimens to analyze the correlations between ARHGAP9 expression and patients' clinical characteristics and treatment responses, thereby providing stronger evidence for personalized therapy in gastric cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this multi-omics study establishes ARHGAP9 as a key driver of gastric cancer progression. Future development of ARHGAP9-targeted inhibitory strategies, targeted therapies, and immunotherapeutic approaches may contribute to improving the poor prognosis associated with gastric cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGAC\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gastric adenocarcinoma\u003c/p\u003e\n\u003cp\u003eGAP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; GTPase-activating protein\u003c/p\u003e\n\u003cp\u003eMAPKs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; Mitogen-activated protein kinases\u003c/p\u003e\n\u003cp\u003eTCGA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; Biological processes\u003c/p\u003e\n\u003cp\u003eCC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Cellular components\u003c/p\u003e\n\u003cp\u003eMF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Molecular functions\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003ePPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Protein-protein interaction\u003c/p\u003e\n\u003cp\u003essGSEA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Single-sample gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eIPS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Immunophenoscore\u003c/p\u003e\n\u003cp\u003eHPA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; Human Protein Atlas\u003c/p\u003e\n\u003cp\u003eqRT-PCR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; quantitative real-time PCR\u003c/p\u003e\n\u003cp\u003eFBS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Fetal bovine serum\u003c/p\u003e\n\u003cp\u003eICIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immune checkpoint inhibitors\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the Joint\u0026nbsp;Logistics\u0026nbsp;Support Force 900th Hospital Basic Laboratory for the experimental study in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;LZQ: Conduct data management, investigations, methodologies, write original drafts, and create visualizations. WXH: Review and edit manuscripts and provide supervision. HWW: Investigated and verified. YS: Surveyed and provided resources. XXL: Manage the project, develop the methodology, and conceptualize the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participat\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from online public databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, et al. 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Curr Oncol Mar. 2022;2(3):1559\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/curroncol29030131\u003c/span\u003e\u003cspan address=\"10.3390/curroncol29030131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastric cancer, ARHGAP9, Bioinformatics, Tumor markers","lastPublishedDoi":"10.21203/rs.3.rs-7311091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7311091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Gastric cancer is characterized by poor prognosis due to late diagnosis and therapeutic resistance. ARHGAP9, a Rho GTPase-activating protein, regulates cytoskeletal dynamics and MAPK signaling, but its role in gastric cancer progression remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: Multi-omics data from TCGA, GEO, and cBioPortal were integrated to analyze ARHGAP9 expression, genetic alterations, and immune correlations in gastric cancer. Enrichment analysis, ceRNA network construction, PPI network analysis, immune infiltration assessment (ESTIMATE, CIBERSORT, ssGSEA), and drug sensitivity evaluation (GDSC, CTRP) were performed to elucidate ARHGAP9's role in gastric cancer. In vitro experiments (qRT-PCR, CCK-8, Transwell) with ARHGAP9 knockdown were conducted in gastric adenocarcinoma cell lines (SGC-7901, MGC-803) for functional validation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: ARHGAP9 was significantly upregulated in Gastric cancer samples (P \u0026lt; 0.05), correlating with advanced T stage, histological grade, and poor prognosis. Differentially expressed genes between high and low ARHGAP9 groups were enriched in immune-related pathways (BCR signaling). High ARHGAP9 expression was associated with increased CD8 + T cell infiltration and positive correlation with immune checkpoints (PD-1, CTLA4; P \u0026lt; 0.001). Low ARHGAP9 expression enhanced sensitivity to PD-1 inhibitors and chemotherapeutic agents (docetaxel, ribociclib). In vitro knockdown of ARHGAP9 inhibited gastric adenocarcinoma cell proliferation, migration, and invasion (P \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: ARHGAP9 drives gastric cancer progression through immune regulation and serves as a prognostic biomarker. Targeting ARHGAP9 may improve therapeutic response in gastric cancer, particularly in patients resistant to immunotherapy.\u003c/p\u003e","manuscriptTitle":"Comprehensive bioinformatics analysis and experimental validation revealed that high-expression ARHGAP9 affected the progression of gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 01:52:18","doi":"10.21203/rs.3.rs-7311091/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-12-01T11:49:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T10:03:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T10:01:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Cell International","date":"2025-08-06T14:53:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-cell-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ccin","sideBox":"Learn more about [Cancer Cell International](http://cancerci.biomedcentral.com/)","snPcode":"12935","submissionUrl":"https://submission.nature.com/new-submission/12935/3","title":"Cancer Cell International","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e96a7308-7af7-4ec8-9dee-988749e5d023","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-05T01:52:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 01:52:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7311091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7311091","identity":"rs-7311091","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}