The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, P.R. China.
Second People's Hospital of Gansu Province, Lanzhou City, Gansu Province, P.R. China.
Biosci Rep. 2020 Nov 27;40(11). doi: 10.1042/BSR20201734.
Colon adenocarcinoma (COAD) is one of the most prevalent malignant tumors worldwide. Immune genes (IGs) have a considerable correlation with tumor initiation and prognosis. The present paper aims to identify the prognosis value of IGs in COAD and conduct a prognosis model for clinical utility. Gene expression data of COAD were downloaded from The Cancer Genome Atlas (TCGA), screening and analyzing differentially expressed IGs by bioinformatics. Core genes were screened by univariate and multivariate Cox regression analyses. Survival analysis was appraised by the Kaplan-Meier method and the log-rank test. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis (GSEA) were used to identify IGs' relevant signal pathways. We predicted the overall survival (OS) by nomogram. Finally, a prognosis model was conducted based on 12 IGs (SLC10A2, CXCL3, NOX4, FABP4, ADIPOQ, IGKV1-33, IGLV6-57, INHBA, UCN, VIP, NGFR, and TRDC). The risk score was an independent prognostic factor, and a nomogram could accurately predict the OS of individual COAD patients. These results were validated in GSE39582, GSE12945, and GSE103479 cohorts. Functional enrichment analysis demonstrated that these IGs are mainly enriched in hormone secretion, hormone transport, lipid transport, cytokine-cytokine receptor interaction, and peroxisome proliferators-activated receptor signaling pathway. In summary, the risk score is an independent prognostic biomarker. We also excavated several IGs related to COAD's survival and maybe potential biomarkers for COAD diagnosis and treatment.
结直肠癌(COAD)是全球最常见的恶性肿瘤之一。免疫基因(IGs)与肿瘤的发生和预后有很大的相关性。本研究旨在鉴定 COAD 中 IGs 的预后价值,并构建用于临床应用的预后模型。通过生物信息学方法从癌症基因组图谱(TCGA)下载 COAD 的基因表达数据,筛选和分析差异表达的 IGs。通过单因素和多因素 Cox 回归分析筛选核心基因。采用 Kaplan-Meier 法和对数秩检验进行生存分析。基因本体论(GO)、京都基因与基因组百科全书(KEGG)和基因集富集分析(GSEA)用于鉴定 IGs 的相关信号通路。通过列线图预测总生存期(OS)。最后,基于 12 个 IGs(SLC10A2、CXCL3、NOX4、FABP4、ADIPOQ、IGKV1-33、IGLV6-57、INHBA、UCN、VIP、NGFR 和 TRDC)构建预后模型。风险评分是独立的预后因素,列线图可以准确预测个体 COAD 患者的 OS。这些结果在 GSE39582、GSE12945 和 GSE103479 队列中得到验证。功能富集分析表明,这些 IGs 主要富集在激素分泌、激素转运、脂质转运、细胞因子-细胞因子受体相互作用和过氧化物酶体增殖物激活受体信号通路。总之,风险评分是独立的预后生物标志物。我们还挖掘了一些与 COAD 生存相关的 IGs,它们可能是 COAD 诊断和治疗的潜在生物标志物。