Background. Intestinal type of gastric cancer (IGC) is the largest subtype of gastric cancer (GC) by Lauren classification. The purpose of this present study was to construct a prognostic signature for IGC patients, based on the high-grade dysplasia (HGD) and IGC tissues, to improve and enhance the prognostic accuracy. Methods. The microarray datasets and associated clinical characteristics of HGD and IGC were obtained from the Gene Expression Omnibus (GEO) database. Based on the differential expression analysis between HGD and IGC, the prognostic-related differential expression genes (DEGs) were identified in a training set by univariate COX regression analysis. The least absolute shrinkage and selection operator (LASSO) regression was used to construct an optimal prognostic signature. The enrichment analysis was performed by using Gene Set Enrichment Analysis (GSEA). The performance of the nomogram was assessed by the calibration curve and concordance index (C-index). The results were validated by using a testing set. Results. We identified 35 prognostic-related DGEs in the training set. The nine-gene signature was established by LASSO analysis. The nine-gene signature was an independent risk factor in both the training and testing sets. The areas under the curve (AUC) values of receiver operating characteristic (ROC) analysis were 0.733 and 0.700 for the training and testing sets, respectively. In GSEA analysis, the gene expression in high-risk group was enriched in hedgehog signaling, epithelial mesenchymal transition, and angiogenesis. The nomogram for IGC showed good performance with C-index of 0.81 (95% CI: 0.76-0.86) and 0.70 (95% CI: 0.63-0.77) in the training and testing sets, respectively. Conclusion. We identified and verified a nine-gene signature for the prognostic prediction of IGC patients, which might identify subgroups of IGC patients and select more suitable therapeutic options.

中文翻译：

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肠型胃癌新预后特征的构建和验证

背景。胃癌肠型 (IGC) 是 Lauren 分类的胃癌 (GC) 的最大亚型。本研究的目的是基于高度不典型增生 (HGD) 和 IGC 组织构建 IGC 患者的预后特征，以改善和提高预后准确性。方法. HGD 和 IGC 的微阵列数据集和相关的临床特征是从基因表达综合 (GEO) 数据库中获得的。基于 HGD 和 IGC 之间的差异表达分析，通过单变量 COX 回归分析在训练集中鉴定预后相关的差异表达基因 (DEG)。使用最小绝对收缩和选择算子 (LASSO) 回归来构建最佳预后特征。通过使用基因集富集分析（GSEA）进行富集分析。通过校准曲线和一致性指数（C-index）评估列线图的性能。使用测试集对结果进行了验证。结果. 我们在训练集中确定了 35 个与预后相关的 DGE。通过 LASSO 分析建立了九个基因特征。九基因特征是训练和测试集中的独立风险因素。训练集和测试集的受试者工作特征 (ROC) 分析的曲线下面积 (AUC) 值分别为 0.733 和 0.700。在GSEA分析中，高危组的基因表达富集于hedgehog信号、上皮间质转化和血管生成。IGC 的列线图表现出良好的性能，在训练和测试集中的 C 指数分别为 0.81（95% CI：0.76-0.86）和 0.70（95% CI：0.63-0.77）。结论. 我们确定并验证了用于 IGC 患者预后预测的九基因特征，这可能识别 IGC 患者的亚组并选择更合适的治疗方案。