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

PeerJ. 2020 Jul 7:8:e9458. doi: 10.7717/peerj.9458. eCollection 2020.

Abstract

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.

Keywords: Colon cancer; Metabolism; Overall survival; Reprogramming energy metabolism; Signature.

Grants and funding

This work was supported in part by the National Key Research and Development Program of China (2018YFC2000100); the National Natural Science Foundation of China (Grant numbers 31871336, 61873075, 61573122); National Science Foundation of Heilongjiang Province (Grant number YQ2019C012); Heilongjiang Postdoctoral Foundation (Grant number LBH-Q18099); University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province (Grant number UNPYSCT-2016049); Heilongjiang Touyan Innovation Team Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.