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

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 e...

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Autores principales: Zhang, Xinxin, Xu, Jinyuan, Lan, Yujia, Guo, Fenghua, Xiao, Yun, Li, Yixue, Li, Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350917/
https://www.ncbi.nlm.nih.gov/pubmed/32704448
http://dx.doi.org/10.7717/peerj.9458
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author Zhang, Xinxin
Xu, Jinyuan
Lan, Yujia
Guo, Fenghua
Xiao, Yun
Li, Yixue
Li, Xia
author_facet Zhang, Xinxin
Xu, Jinyuan
Lan, Yujia
Guo, Fenghua
Xiao, Yun
Li, Yixue
Li, Xia
author_sort Zhang, Xinxin
collection PubMed
description 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.
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spelling pubmed-73509172020-07-22 Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer Zhang, Xinxin Xu, Jinyuan Lan, Yujia Guo, Fenghua Xiao, Yun Li, Yixue Li, Xia PeerJ Bioinformatics 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. PeerJ Inc. 2020-07-07 /pmc/articles/PMC7350917/ /pubmed/32704448 http://dx.doi.org/10.7717/peerj.9458 Text en ©2020 Zhang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Zhang, Xinxin
Xu, Jinyuan
Lan, Yujia
Guo, Fenghua
Xiao, Yun
Li, Yixue
Li, Xia
Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer
title Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer
title_full Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer
title_fullStr Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer
title_full_unstemmed Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer
title_short Transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer
title_sort transcriptome analysis reveals a reprogramming energy metabolism-related signature to improve prognosis in colon cancer
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7350917/
https://www.ncbi.nlm.nih.gov/pubmed/32704448
http://dx.doi.org/10.7717/peerj.9458
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