Table_3_Identification of Stemness Characteristics Associated With the Immune Microenvironment and Prognosis in Gastric Cancer.xls

BackgroundGastric cancer (GC) is a highly heterogeneous disease. In recent years, the prognostic value of the mRNA expression-based stemness index (mRNAsi) across cancers has been reported. We intended to identify stemness index-associated genes (SI-genes) for clinical characteristic, gene mutation status, immune response, and tumor microenvironment evaluation as well as risk stratification and survival prediction.
MethodsThe correlations between the mRNAsi and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were evaluated. Weighted gene correlation network analysis (WGCNA) was performed to identify SI-genes from differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was employed to calculate the sample SI-gene-based ssGSEA score according to the SI-genes. Then, the correlations between the ssGSEA score and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were analyzed. Finally, the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to construct a prognostic signature with prognostic SI-genes. The ssGSEA score and prognostic signature were validated using the Gene Expression Omnibus (GEO) database.
ResultsThe mRNAsi could predict overall survival (OS), clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Fourteen positive SI-genes and 178 negative SI-genes were screened out using WGCNA. The ssGSEA score, similar to the mRNAsi, was found to be closely related to OS, clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Finally, a prognostic signature based on 18 prognostic SI-genes was verified to more accurately predict GC 1-year, 3-year, and 5-year OS than traditional clinical prediction models.
ConclusionThe ssGSEA score and prognostic signature based on 18 prognostic SI-genes are of great value for immune response evaluation, risk stratification and survival prediction in GC and suggest that stemness features are crucial drivers of GC progression.

背景：胃癌（Gastric cancer, GC）是一种高度异质性的疾病。近年来，已有研究报道了基于mRNA表达的干细胞指数（mRNA expression-based stemness index, mRNAsi）在多种癌症中的预后价值。本研究旨在筛选与干细胞指数相关的基因（stemness index-associated genes, SI-genes），以用于临床特征、基因突变状态、免疫应答、肿瘤微环境评估，以及风险分层与生存预测。
方法：本研究首先评估了mRNAsi与GC预后、临床特征、基因突变状态、免疫细胞浸润及肿瘤微环境之间的相关性。随后，从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中的差异表达基因（differentially expressed genes, DEGs）中，通过加权基因共表达网络分析（Weighted gene correlation network analysis, WGCNA）筛选得到SI-genes。基于筛选出的SI-genes，采用单样本基因集富集分析（Single-sample gene set enrichment analysis, ssGSEA）计算样本的SI-genes相关ssGSEA评分。继而分析该ssGSEA评分与GC预后、临床特征、基因突变状态、免疫细胞浸润及肿瘤微环境之间的相关性。最后，利用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）Cox回归算法，基于预后相关SI-genes构建预后风险模型。本研究通过基因表达综合数据库（Gene Expression Omnibus, GEO）对ssGSEA评分与构建的预后风险模型进行验证。
结果：mRNAsi可有效预测胃癌患者的总生存期（overall survival, OS）、临床特征、基因突变状态、免疫细胞浸润情况及肿瘤微环境组成。通过WGCNA共筛选得到14个正相关SI-genes与178个负相关SI-genes。研究发现，ssGSEA评分与mRNAsi类似，同样与OS、临床特征、基因突变状态、免疫细胞浸润及肿瘤微环境组成密切相关。最终，基于18个预后相关SI-genes构建的预后风险模型，相较传统临床预测模型，可更精准地预测胃癌患者1年、3年及5年OS。
结论：基于18个预后相关SI-genes的ssGSEA评分与预后风险模型，在胃癌的免疫应答评估、风险分层及生存预测中具有重要应用价值，同时提示干细胞特征是胃癌进展的关键驱动因素。