Gastric cancer (GC) is a heterogeneous tumour with numerous differences of epidemiologic and clinicopathologic features between cardia cancer and non‐cardia cancer. However, few studies were performed to construct site‐specific GC prognostic models. In this study, we identified site‐specific GC transcriptomic prognostic biomarkers using genetic algorithm (GA)‐based support vector machine (GA‐SVM) and GA‐based Cox regression method (GA‐Cox) in the Cancer Genome Atlas (TCGA) database. The area under time‐dependent receive operating characteristic (ROC) curve (AUC) regarding 5‐year survival and concordance index (C‐index) was used to evaluate the predictive ability of Cox regression models. Finally, we identified 10 and 13 prognostic biomarkers for cardia cancer and non‐cardia cancer, respectively. Compared to traditional models, the addition of these site‐specific biomarkers could notably improve the model preference (cardia: AUC_traditional vs AUC_combined = 0.720 vs 0.899, P  = 8.75E‐08; non‐cardia: AUC_traditional vs AUC_combined = 0.798 vs 0.994, P  = 7.11E‐16). The combined nomograms exhibited superior performance in cardia and non‐cardia GC survival prediction (C‐index_cardia = 0.816; C‐index_noncardia = 0.812). We also constructed a user‐friendly GC site‐specific molecular system (GC‐SMS, https://njmu‐zhanglab.shinyapps.io/gc_sms/), which is freely available for users. In conclusion, we developed site‐specific GC prognostic models for predicting cardia cancer and non‐cardia cancer survival, providing more support for the individualized therapy of GC patients.

中文翻译：

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使用基于遗传算法的方法识别贲门和非贲门特异性胃癌预后因素的转录组学研究。

胃癌（GC）是一种异质性肿瘤，贲门癌和非贲门癌在流行病学和临床病理学特征上存在许多差异。然而，很少有研究用于构建位点特异性 GC 预后模型。在这项研究中，我们使用基于遗传算法 (GA) 的支持向量机 (GA-SVM) 和基于 GA 的 Cox 回归方法 (GA-Cox) 在癌症基因组图谱 (TCGA) 数据库中确定了位点特异性 GC 转录组预后生物标志物. 使用关于 5 年生存率和一致性指数 (C-index) 的时间相关接收操作特征 (ROC) 曲线 (AUC) 下的面积来评估 Cox 回归模型的预测能力。最后，我们分别确定了贲门癌和非贲门癌的 10 个和 13 个预后生物标志物。与传统模式相比，_传统vs AUC_组合 = 0.720 vs 0.899，P  = 8.75E-08；非 心脏：_传统AUC与_合并AUC  = 0.798 与 0.994，P = 7.11E-16）。组合列线图在贲门和非贲门 GC 生存预测方面表现出优异的性能（C 指数_贲门 = 0.816；C 指数_非贲门 = 0.812）。我们还构建了一个用户友好的 GC 位点特异性分子系统（GC-SMS，https://njmu-zhanglab.shinyapps.io/gc_sms/），可供用户免费使用。总之，我们开发了位点特异性 GC 预后模型，用于预测贲门癌和非贲门癌的存活率，为 GC 患者的个体化治疗提供更多支持。