Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
Biomed Res Int. 2020 Nov 17;2020:4845360. doi: 10.1155/2020/4845360. eCollection 2020.
A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.
代谢紊乱被认为是癌症的标志之一。在结肠癌（CC）中已经鉴定出多种差异表达的代谢基因，并且它们的生物学功能和预后价值已经得到了很好的探索。本研究的目的是建立一个代谢特征，以优化 CC 的预后预测。相关数据从癌症基因组图谱（TCGA）、基因-组织表达（GTEx）数据库和基因表达综合数据库（GEO）中下载，与 GSE39582 集、GSE17538 集、GSE33113 集和 GSE37892 集相结合。使用 TCGA 和 GTEx 数据集，对差异表达的代谢基因进行单变量 Cox 回归和套索 Cox 回归分析。最后，开发了一个由十七个基因组成的代谢特征，将患者分为高风险组和低风险组。在 TCGA 和 GEO 数据集中，高风险组的患者预后均比低风险组差。此外，基因集富集分析表明，多个与代谢相关的途径显著富集。总之，我们的研究描述了一个新的十七个基因代谢特征，用于预测结肠癌的预后。
