Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, PR China.
Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai, PR China.
Genet Test Mol Biomarkers. 2021 Mar;25(3):163-178. doi: 10.1089/gtmb.2020.0141.
Colon cancer (CC) is an immunogenic tumor and immune-targeting disease. In this study, we analyzed differentially expressed genes (DEGs) from the expression profile data in CC of The Cancer Genome Atlas. Using univariate and multivariate Cox regression analysis, an immune gene-risk model containing 14 immune genes was established. Four hundred seventeen CC samples were divided into high-risk and low-risk groups, and Kaplan-Meier analysis revealed that high-risk score predicted poor survival. Meanwhile, we found the model was an independent prognostic factor for CC. Weighted gene coexpression network analysis was used to identify key gene modules between high- and low-risk groups. The methods of CIBERSORT and single-sample Gene Set Enrichment Analysis were used to evaluate the correlation between immune cells and our model. Taken together, our study suggested that the immune gene-related risk model may be developed as a potential tool in the prognostic assessment of CC.
结直肠癌(CC)是一种免疫原性肿瘤和免疫靶向疾病。本研究分析了癌症基因组图谱中 CC 的表达谱数据中的差异表达基因(DEGs)。使用单变量和多变量 Cox 回归分析,建立了包含 14 个免疫基因的免疫基因风险模型。将 417 例 CC 样本分为高风险组和低风险组,Kaplan-Meier 分析表明高风险评分预示着不良预后。同时,我们发现该模型是 CC 的独立预后因素。加权基因共表达网络分析用于识别高低风险组之间的关键基因模块。CIBERSORT 和单样本基因集富集分析的方法用于评估免疫细胞与我们模型之间的相关性。综上所述,我们的研究表明,免疫基因相关风险模型可能成为 CC 预后评估的潜在工具。