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

Colon cancer Metabolism Overall survival Reprogramming energy metabolism Signature

Journal

PeerJ
ISSN: 2167-8359
Titre abrégé: PeerJ
Pays: United States
ID NLM: 101603425

Informations de publication

Date de publication:
2020
Historique:
received: 17 02 2020
accepted: 09 06 2020
entrez: 25 7 2020
pubmed: 25 7 2020
medline: 25 7 2020
Statut: epublish

Résumé

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 (

Identifiants

pubmed: 32704448
doi: 10.7717/peerj.9458
pii: 9458
pmc: PMC7350917
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e9458

Informations de copyright

©2020 Zhang et al.

Déclaration de conflit d'intérêts

The authors declare there are no competing interests.

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Auteurs

Xinxin Zhang (X)

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.

Jinyuan Xu (J)

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.

Yujia Lan (Y)

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.

Fenghua Guo (F)

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.

Yun Xiao (Y)

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Harbin, China.

Yixue Li (Y)

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.

Xia Li (X)

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Harbin, China.

Classifications MeSH