DataSheet_1_A novel signature based on CeRNA and immune status predicts prognostic risk and drug sensitivity in gastric cancer patients.docx

BackgroundAt present, there is increasing evidence that both competitive endogenous RNAs (ceRNAs) and immune status in the tumor microenvironment (TME) can affect the progression of gastric cancer (GC), and are closely related to the prognosis of patients. However, few studies have linked the two to jointly determine the prognosis of patients with GC. This study aimed to develop a combined prognostic model based on ceRNAs and immune biomarkers.MethodsFirst, the gene expression profiles and clinical information were downloaded from TCGA and GEO databases. Then two ceRNA networks were constructed on the basis of circRNA. Afterwards, the key genes were screened by univariate Cox regression analysis and Lasso regression analysis, and the ceRNA-related prognostic model was constructed by multivariate Cox regression analysis. Next, CIBERSORT and ESTIMATE algorithms were utilized to obtain the immune cell infiltration abundance and stromal/immune score in TME. Furthermore, the correlation between ceRNAs and immunity was found out through co-expression analysis, and another immune-related prognosis model was established. Finally, combining these two models, a comprehensive prognostic model was built and visualized with a nomogram.ResultsThe (circRNA, lncRNA)-miRNA-mRNA regulatory network of GC was constructed. The predictive power of ceRNA-related and immune-related prognosis models was moderate. Co-expression analysis showed that the ceRNA network was correlated with immunity. The integrated model of combined ceRNAs and immunity in the TCGA training set, the AUC values of 1, 3, and 5-year survival rates were 0.78, 0.76, and 0.78, respectively; in the independent external validation set GSE62254, they were 0.81, 0.79, and 0.78 respectively; in GSE15459, they were 0.84, 0.88 and 0.89 respectively. Besides, the prognostic score of the comprehensive model can predict chemotherapeutic drug resistance. Moreover, we found that plasma variant translocation 1 (PVT1) and infiltrating immune cells (mast cells) are worthy of further investigation as independent prognostic factors.ConclusionsTwo ceRNA regulatory networks were constructed based on circRNA. At the same time, a comprehensive prognosis model was established, which has a high clinical significance for prognosis prediction and chemotherapy drug selection of GC patients.

当前研究表明，竞争性内源RNA（ceRNA）和肿瘤微环境（TME）中的免疫状态均可影响胃癌（GC）的进展，并与患者的预后密切相关。然而，鲜有研究将二者联合以共同决定GC患者的预后。本研究旨在构建基于ceRNA和免疫生物标志物的联合预后模型。方法上，首先从TCGA和GEO数据库中下载基因表达谱和临床信息。随后，基于环状RNA构建了两个ceRNA网络。接着，通过单变量Cox回归分析和Lasso回归分析筛选关键基因，并通过多变量Cox回归分析构建ceRNA相关预后模型。随后，利用CIBERSORT和ESTIMATE算法获取TME中的免疫细胞浸润丰度和间质/免疫评分。进一步通过共表达分析发现ceRNA与免疫之间的相关性，并建立了另一个免疫相关预后模型。最后，结合这两个模型，构建了综合预后模型，并以列线图进行可视化。结果显示，构建了GC的(circRNA, lncRNA)-miRNA-mRNA调控网络。ceRNA相关和免疫相关预后模型的预测能力适中。共表达分析显示ceRNA网络与免疫相关。在TCGA训练集中，结合ceRNAs和免疫的集成模型，1年、3年和5年生存率的AUC值分别为0.78、0.76和0.78；在独立的外部验证集GSE62254中，分别为0.81、0.79和0.78；在GSE15459中，分别为0.84、0.88和0.89。此外，综合模型的预后评分可以预测化疗药物耐药性。我们还发现，血浆变异易位1（PVT1）和浸润性免疫细胞（肥大细胞）作为独立的预后因素值得进一步研究。结论上，基于环状RNA构建了两个ceRNA调控网络。同时，建立的综合预后模型对于GC患者的预后预测和化疗药物选择具有重要的临床意义。