Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer

• Published in: PeerJ (San Francisco, CA) Vol. 8; p. e9458
• Main Authors: Zhang, Xinxin, Xu, Jinyuan, Lan, Yujia, Guo, Fenghua, Xiao, Yun, Li, Yixue, Li, Xia
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ. Ltd 07.07.2020; PeerJ, Inc; PeerJ Inc
• Subjects: Analysis; Bioinformatics; Cancer therapies; Cell proliferation; Chemotherapy; Colon cancer; Colorectal cancer; Computational Biology; Computational Science; Datasets; Energy; Energy metabolism; Gene expression; Genes; Genetic aspects; Health aspects; Medical prognosis; Metabolism; Metastasis; Molecular Biology; Oncology; Overall survival; Patients; Physiological aspects; Prognosis; Regression analysis; Reprogramming energy metabolism; Risk groups; Signature; Survival analysis; Tumorigenesis
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.9458

Although much progress has been made to improve treatment, colon cancer remains a leading cause of cancer death worldwide. Metabolic reprogramming is a significant ability of cancer cells to ensure the necessary energy supply in uncontrolled proliferation. Since reprogramming energy metabolism has emerged as a new hallmark of cancer cells, accumulating evidences have suggested that metabolism-related genes may serve as key regulators of tumorigenesis and potential biomarkers. In this study, we analyzed a set of reprogramming energy metabolism-related genes by transcriptome analysis in colon cancer and revealed a five-gene signature that could significantly predict the overall survival. The reprogramming energy metabolism-related signature could distinguish patients into high-risk and low-risk groups with significantly different survival times (P = 0.0011; HR = 1.92; 95% CI [1.29-2.87]). Its prognostic value was confirmed in another two independent colon cancer cohorts (P = 5.2e-04; HR = 2.09, 95%; CI [1.37-3.2] for GSE17538 and P = 3.8e-04; HR = 2.08, 95% CI [1.37-3.16] for GSE41258). By multivariable analysis, we found that the signature was independent of clinicopathological features. Its power in promoting risk stratification of the current clinical stage was then evaluated by stratified analysis. Moreover, the signature could improve the power of the TNM stage for the prediction of overall survival and could be used in patients who received adjuvant chemotherapy. Overall, our results demonstrated the important role of the reprogramming energy metabolism-related signature in promoting stratification of high-risk patients, which could be diagnostic of adjuvant therapy benefit.