Table_14_Bioinformatics Analysis Reveals an Association Between Cancer Cell Stemness, Gene Mutations, and the Immune Microenvironment in Stomach Adenocarcinoma.xlsx

Cancer stem cells (CSCs), characterized by infinite proliferation and self-renewal, greatly challenge tumor therapy. Research into their plasticity, dynamic instability, and immune microenvironment interactions may help overcome this obstacle. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena Browser. The infiltrating immune cells in stomach adenocarcinoma (STAD) tissues were predicted using the CIBERSORT method. Differentially expressed genes (DEGs) between the normal and tumor tissues were used to construct prognostic models with weighted gene co-expression network analysis (WGCNA) and Lasso regression. The association between cancer stemness, gene mutations, and immune responses was evaluated in STAD. A total of 6,739 DEGs were identified between the normal and tumor tissues. DEGs in the brown (containing 19 genes) and blue (containing 209 genes) co-expression modules were used to perform survival analysis based on Cox regression. A nine-gene signature prognostic model (ARHGEF38-IT1, CCDC15, CPZ, DNASE1L2, NUDT10, PASK, PLCL1, PRR5-ARHGAP8, and SYCE2) was constructed from 178 survival-related DEGs that were significantly related to overall survival, clinical characteristics, tumor microenvironment immune cells, TMB, and cancer-related pathways in STAD. Gene correlation was significant across the prognostic model, CNVs, and drug sensitivity. Our findings provide a prognostic model and highlight potential mechanisms and associated factors (immune microenvironment and mutation status) useful for targeting CSCs.

癌症干细胞（Cancer stem cells, CSCs）以无限增殖与自我更新为核心特征，对肿瘤治疗构成极大挑战。针对其可塑性、动态不稳定性以及免疫微环境互作开展研究，或有助于攻克这一治疗难题。本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）及UCSC Xena浏览器中获取了干细胞指数（mRNA干细胞指数, mRNAsi）、基因突变、拷贝数变异（copy number variations, CNV）、肿瘤突变负荷（tumor mutation burden, TMB）以及对应临床特征的相关数据。采用CIBERSORT算法对胃腺癌（stomach adenocarcinoma, STAD）组织中的浸润免疫细胞进行浸润丰度预测。以正常组织与肿瘤组织间的差异表达基因（differentially expressed genes, DEGs）为基础，通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）与Lasso回归构建预后模型。在胃腺癌样本中评估了癌症干细胞特性、基因突变与免疫应答之间的关联。最终在正常组织与肿瘤组织间共鉴定出6739个差异表达基因。选取棕色共表达模块（含19个基因）与蓝色共表达模块（含209个基因）中的差异表达基因，基于Cox回归开展生存分析。从178个与胃腺癌总生存期、临床特征、肿瘤微环境免疫细胞、肿瘤突变负荷及癌症相关通路均显著相关的生存相关差异表达基因中，构建了九基因标签预后模型，涉及基因包括ARHGEF38-IT1、CCDC15、CPZ、DNASE1L2、NUDT10、PASK、PLCL1、PRR5-ARHGAP8及SYCE2。该预后模型所涉及的基因相关性、拷贝数变异与药物敏感性之间存在显著关联。本研究构建的预后模型为胃癌相关研究提供了新的参考，并揭示了靶向癌症干细胞的潜在机制与相关影响因素（免疫微环境与突变状态）。