Background. With 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. Methods. The 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. Results. GC 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 PPI (protein-protein interaction) 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 curves based on risk score and clinical data. Conclusions. The 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。癌症干细胞（CSCs）具有特征性的自我更新和多能分化，对胃癌（GC）的转移、复发和耐药性具有至关重要的影响。然而，参与GC干细胞干性的基因尚未确定。方法。用 GC 中的差异表达分析基于 mRNA 表达的干性指数 (mRNAsi)。加权基因共表达网络分析（WGCNA）用于构建针对差异表达基因（DEG）的共表达网络，并发现mRNAsi相关模块和基因。我们在转录和蛋白质水平上评估了关键基因之间的关联。基因表达综合（GEO）数据库用于验证关键基因的表达水平。根据最小绝对收缩和选择算子（LASSO）Cox回归分析建立风险模型。此外，我们通过采用 Kaplan-Meier (KM) 加多变量 Cox 分析确定了模型的预后价值。结果。相对于健康的非肿瘤组织，GC 组织表现出显着更高的 mRNAsi。基于WGCNA，鉴定出17个关键基因（ARHGAP11A、BUB1、BUB1B、C1orf112、CENPF、KIF14、KIF15、KIF18B、KIF4A、NCAPH、PLK4、RACGAP1、RAD54L、SGO2、TPX2、TTK和XRCC2）。这些关键基因在 GC 中明显过表达，并在 GEO 数据库中得到验证。STRING评估的PPI（蛋白质-蛋白质相互作用）网络表明关键基因紧密相连。LASSO 分析后，九基因风险模型（BUB1B、NCAPH、KIF15、RAD54L、KIF18B、KIF4A、TTK、SGO2、C1orf112) 被构建。高危组的总生存率相对较差。与临床病理学特征相比，风险评分的曲线下面积（AUC）更高。根据多变量 Cox 分析，九基因风险模型是 GC 患者疾病结局的预测因子（HR，7.606；95% CI，3.037-19.051；P < 0.001）。我们根据风险评分和临床数据构建了一个具有良好拟合校准曲线的预后列线图。结论。本研究中鉴定的 17 个 mRNAsi 相关关键基因可能是 GC 治疗中的潜在治疗靶点，因为它们可以抑制干性特性。九基因风险模型可用于预测患者的疾病结局。与临床病理学特征相比，风险评分的曲线下面积（AUC）更高。根据多变量 Cox 分析，九基因风险模型是 GC 患者疾病结局的预测因子（HR，7.606；95% CI，3.037-19.051；P < 0.001）。我们根据风险评分和临床数据构建了一个具有良好拟合校准曲线的预后列线图。结论。本研究中鉴定的 17 个 mRNAsi 相关关键基因可能是 GC 治疗中的潜在治疗靶点，因为它们可以抑制干性特性。九基因风险模型可用于预测患者的疾病结局。与临床病理学特征相比，风险评分的曲线下面积（AUC）更高。根据多变量 Cox 分析，九基因风险模型是 GC 患者疾病结局的预测因子（HR，7.606；95% CI，3.037-19.051；P < 0.001）。我们根据风险评分和临床数据构建了一个具有良好拟合校准曲线的预后列线图。结论。本研究中鉴定的 17 个 mRNAsi 相关关键基因可能是 GC 治疗中的潜在治疗靶点，因为它们可以抑制干性特性。九基因风险模型可用于预测患者的疾病结局。九基因风险模型是 GC 患者疾病结局的预测因子（HR，7.606；95% CI，3.037-19.051；P < 0.001）。我们根据风险评分和临床数据构建了一个具有良好拟合校准曲线的预后列线图。结论。本研究中鉴定的 17 个 mRNAsi 相关关键基因可能是 GC 治疗中的潜在治疗靶点，因为它们可以抑制干性特性。九基因风险模型可用于预测患者的疾病结局。九基因风险模型是 GC 患者疾病结局的预测因子（HR，7.606；95% CI，3.037-19.051；P < 0.001）。我们根据风险评分和临床数据构建了一个具有良好拟合校准曲线的预后列线图。结论。本研究中鉴定的 17 个 mRNAsi 相关关键基因可能是 GC 治疗中的潜在治疗靶点，因为它们可以抑制干性特性。九基因风险模型可用于预测患者的疾病结局。