Table_3_Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer.XLSX

BackgroundWith characteristic self-renewal and multipotent differentiation, cancer stem cells (CSCs) have a crucial influence on the metastasis, relapse and drug resistance of gastric cancer (GC). However, the genes that participates in the stemness of GC stem cells have not been identified.
MethodsThe mRNA expression-based stemness index (mRNAsi) was analyzed with differential expressions in GC. The weighted gene co-expression network analysis (WGCNA) was utilized to build a co-expression network targeting differentially expressed genes (DEG) and discover mRNAsi-related modules and genes. We assessed the association between the key genes at both the transcription and protein level. Gene Expression Omnibus (GEO) database was used to validate the expression levels of the key genes. The risk model was established according to the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Furthermore, we determined the prognostic value of the model by employing Kaplan-Meier (KM) plus multivariate Cox analysis.
ResultsGC tissues exhibited a substantially higher mRNAsi relative to the healthy non-tumor tissues. Based on WGCNA, 17 key genes (ARHGAP11A, BUB1, BUB1B, C1orf112, CENPF, KIF14, KIF15, KIF18B, KIF4A, NCAPH, PLK4, RACGAP1, RAD54L, SGO2, TPX2, TTK, and XRCC2) were identified. These key genes were clearly overexpressed in GC and validated in the GEO database. The protein-protein interaction (PPI) network as assessed by STRING indicated that the key genes were tightly connected. After LASSO analysis, a nine-gene risk model (BUB1B, NCAPH, KIF15, RAD54L, KIF18B, KIF4A, TTK, SGO2, C1orf112) was constructed. The overall survival in the high-risk group was relatively poor. The area under curve (AUC) of risk score was higher compared to that of clinicopathological characteristics. According to the multivariate Cox analysis, the nine-gene risk model was a predictor of disease outcomes in GC patients (HR, 7.606; 95% CI, 3.037–19.051; P < 0.001). We constructed a prognostic nomogram with well−fitted calibration curve based on risk score and clinical data.
ConclusionThe 17 mRNAsi-related key genes identified in this study could be potential treatment targets in GC treatment, considering that they can inhibit the stemness properties. The nine-gene risk model can be employed to predict the disease outcomes of the patients.

背景 具备特征性自我更新与多向分化潜能的癌症干细胞（Cancer Stem Cells, CSCs），对胃癌（Gastric Cancer, GC）的转移、复发及耐药性具有至关重要的调控作用。然而，参与胃癌干细胞干性维持的基因尚未被明确鉴定。
方法 本研究基于mRNA表达量分析胃癌组织的干细胞性指数（mRNA expression-based stemness index, mRNAsi）及其差异表达情况。采用加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）针对差异表达基因（Differentially Expressed Genes, DEG）构建共表达网络，筛选与mRNAsi相关的模块及基因。我们分别从转录水平与蛋白水平对关键基因的关联关系进行了验证分析。通过基因表达综合数据库（Gene Expression Omnibus, GEO）验证关键基因的表达水平。基于最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）Cox回归分析构建风险预测模型。此外，结合Kaplan-Meier（KM）法与多变量Cox回归分析，评估该模型的预后价值。
结果 胃癌组织的mRNAsi水平显著高于正常非肿瘤组织。通过WGCNA分析，共筛选得到17个关键基因（ARHGAP11A、BUB1、BUB1B、C1orf112、CENPF、KIF14、KIF15、KIF18B、KIF4A、NCAPH、PLK4、RACGAP1、RAD54L、SGO2、TPX2、TTK及XRCC2）。上述关键基因在胃癌组织中呈显著高表达，且该结果在GEO数据库中得到验证。通过STRING数据库构建的蛋白-蛋白相互作用（Protein-Protein Interaction, PPI）网络显示，这些关键基因之间存在紧密的相互关联。经LASSO分析后，本研究构建了包含9个基因的风险预测模型（BUB1B、NCAPH、KIF15、RAD54L、KIF18B、KIF4A、TTK、SGO2、C1orf112）。高风险组患者的总生存期显著更差。风险评分的曲线下面积（Area Under Curve, AUC）高于临床病理特征的曲线下面积。多变量Cox回归分析显示，该9基因风险模型可作为胃癌患者疾病预后的独立预测因子（风险比HR=7.606；95%置信区间CI=3.037~19.051；P<0.001）。本研究基于风险评分与临床数据，构建了拟合度良好的预后列线图，并配有校准曲线。
结论 本研究鉴定得到的17个与mRNAsi相关的关键基因，可通过抑制胃癌干细胞的干性特征成为胃癌治疗的潜在靶点。该9基因风险模型可用于预测胃癌患者的疾病预后结局。