胃癌免疫细胞浸润相关预后基因的共表达网络分析鉴定

背景与目的：肿瘤免疫细胞浸润在胃癌（GC）的进展和预后中起到关键作用，然而，目前影响GC预后的免疫细胞浸润调控基因还不清楚。本研究联合共表达网络分析研究和反卷积分法鉴定了GC免疫相关预后基因。  方法：从TCGA数据库中下载GC的mRNA表达数据并使用CIBERSORT反卷积算法来确定每个样品中免疫细胞的比例，并构建共表达网络，结合Kaplan-Meier法确定免疫相关预后基因。 结果：在纳入分析的22种免疫细胞中，有11种免疫细胞在肿瘤组织中显著高于正常组织（均P<0.05）。对上述11种免疫细胞进行生存分析，发现静止记忆性CD4 T细胞和调节型T细胞的浸润情况与GC患者生存率呈明显相关（均P<0.05）。基于上结果论进行共表达网络分析，得绿松石模块中的基因与上述两种类型的细胞相关性最显著，并鉴定得到了3个与记忆性CD4 T细胞相关（CGB5、LINC00106、LINC00392）和1个与调节型T细胞相关（UPK1B）的预后基因（均P<0.05)。 结论：本研究鉴定的4个胃癌免疫细胞浸润相关预后基因可能在GC的进展和预后中起到重要作用，这些基因可能会通过影响肿瘤免疫过程而作为潜在的胃癌免疫治疗靶点。 

Co-expression network analysis and identification of prognostic genes associated with immune cell infiltration in gastric cancer

Background and Aims: Tumor immune cell infiltration plays a key role in the progression and prognosis of gastric cancer (GC). However, the regulatory genes of immune cell infiltration that affect the prognosis of GC are still unclear at present. This study was conducted to identified the immune genes associated with the prognosis of GC by co-expression network analysis and deconvolution analysis.   Methods: GC mRNA expression data were downloaded from TCGA database and the proportions of immune cells in each sample were determined using the CIBERSORT deconvolution algorithm. A co-expression network was also constructed, and then, the immune-related prognostic genes were identified by combining the Kaplan-Meier method. Results: Of the 22 types of immune cells included in the analysis, 11 were significantly higher in tumor tissue than those in normal tissue (all P<0.05). Survival analysis for the above 11 types of immune cells showed that infiltration of T cells CD4 memory resting and T cells regulatory was significantly correlated with survival rate in GC patients (all P<0.05). Based on the above results, the co-expression network analysis revealed that the genes in the turquoise module were most significantly correlated with the above two types of cells, and then, 3 prognostic genes associated with memory CD4 T cells (CGB5, LINC00106, LINC00392) and one associated with regulated T cells (UPK1B) were identified (all P<0.05).  Conclusion: The 4 prognostic genes identified in this study may play important roles in the progression and prognosis of GC, and these genes may serve as potential targets for immunotherapy of GC by influencing the tumor immune process.