Identification of a novel glycolysis-related gene signature for predicting the survival of patients with colon adenocarcinoma

生存分析 结直肠癌 基因签名 内科学 癌症研究 医学 基因表达谱 糖酵解 腺癌 生物 肿瘤科 癌症 基因表达 基因 比例危险模型 遗传学 内分泌学 新陈代谢
作者
Kezhen Yi,Jiangquan Wu,Xuan Tang,Qian Zhang,Bi‐Cheng Wang,Xinghuan Wang
出处
期刊:Scandinavian Journal of Gastroenterology [Taylor & Francis]
卷期号:57 (2): 214-221 被引量:3
标识
DOI:10.1080/00365521.2021.1989026
摘要

The most frequent histologic subtype of colon cancer is colon adenocarcinoma (COAD). A major problem in the diagnosis and treatment of COAD is that there is lack of new biomarkers to indicate the early stage of COAD. Compared with normally differentiated cells, the glycolytic pathways of tumor cells are more active, thus making them more adaptable to the hypoxic environment of solid tumors, which is known as the Warburg effect. Therefore, establishing a diagnostic and prognostic model based on glycolysis-related genes may provide guidance for the precise treatment of colon cancer.The Cancer Genome Atlas (TCGA) mRNA data were used to identify differentially expressed genes (DEGs). The glycolysis-related DEGs were identified using Gene Set Enrichment Analysis (GSEA) with HALLMARK gene sets. Combined with clinical data, we identified prognostic genes in glycolysis-related DEGs based on Cox regression analysis. Four glycolysis-related genes were identified and a predictive model was developed using univariate and multivariate Cox regression analysis. cBioPortal investigated the chromosomal variations of these genes. Following that, survival analysis and receiver operating characteristic (ROC) curve validation were carried out. The correlations between glycolysis-related gene signatures and molecular features and cancer subtypes were analyzed.We discovered five genes (SPAG4, P4HA1, STC2, ENO3, and GPC1) that are associated with COAD patients' prognosis. The risk score was more accurate in predicting prognosis when based on this gene signature in COAD patients. Furthermore, multivariate Cox regression analysis demonstrated that the glycolysis-related gene signature's predictive value was independent of clinical variables.We identified a glycolysis-related five-gene signature and developed a risk staging model potentially valuable for the clinical management of COAD patients. Our results suggest that prognostic markers based on glycolysis-related genes may be a reliable predictive tool for the prognosis of COAD patients.
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