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Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer

Metabolic changes, especially in glucose metabolism, are widely established during the occurrence and development of tumors and regarded as biological markers of pan-cancer. The well-known ‘Warburg effect’ demonstrates that cancer cells prefer aerobic glycolysis even if there is sufficient ambient o...

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Autores principales: Zhu, Jun, Wang, Shuai, Bai, Han, Wang, Ke, Hao, Jun, Zhang, Jian, Li, Jipeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969881/
https://www.ncbi.nlm.nih.gov/pubmed/33747908
http://dx.doi.org/10.3389/fonc.2021.588811
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author Zhu, Jun
Wang, Shuai
Bai, Han
Wang, Ke
Hao, Jun
Zhang, Jian
Li, Jipeng
author_facet Zhu, Jun
Wang, Shuai
Bai, Han
Wang, Ke
Hao, Jun
Zhang, Jian
Li, Jipeng
author_sort Zhu, Jun
collection PubMed
description Metabolic changes, especially in glucose metabolism, are widely established during the occurrence and development of tumors and regarded as biological markers of pan-cancer. The well-known ‘Warburg effect’ demonstrates that cancer cells prefer aerobic glycolysis even if there is sufficient ambient oxygen. Accumulating evidence suggests that aerobic glycolysis plays a pivotal role in colorectal cancer (CRC) development. However, few studies have examined the relationship of glycolytic gene clusters with prognosis of CRC patients. Here, our aim is to build a glycolysis-associated gene signature as a biomarker for colorectal cancer. The mRNA sequencing and corresponding clinical data were downloaded from TCGA and GEO databases. Gene set enrichment analysis (GSEA) was performed, indicating that four gene clusters were significantly enriched, which revealed the inextricable relationship of CRC with glycolysis. By comparing gene expression of cancer and adjacent samples, 236 genes were identified. Univariate, multivariate, and LASSO Cox regression analyses screened out five prognostic-related genes (ENO3, GPC1, P4HA1, SPAG4, and STC2). Kaplan–Meier curves and receiver operating characteristic curves (ROC, AUC = 0.766) showed that the risk model could become an effective prognostic indicator (P < 0.001). Multivariate Cox analysis also revealed that this risk model is independent of age and TNM stages. We further validated this risk model in external cohorts (GES38832 and GSE39582), showing these five glycolytic genes could emerge as reliable predictors for CRC patients’ outcomes. Lastly, based on five genes and risk score, we construct a nomogram model assessed by C-index (0.7905) and calibration plot. In conclusion, we highlighted the clinical significance of glycolysis in CRC and constructed a glycolysis-related prognostic model, providing a promising target for glycolysis regulation in CRC.
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spelling pubmed-79698812021-03-19 Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer Zhu, Jun Wang, Shuai Bai, Han Wang, Ke Hao, Jun Zhang, Jian Li, Jipeng Front Oncol Oncology Metabolic changes, especially in glucose metabolism, are widely established during the occurrence and development of tumors and regarded as biological markers of pan-cancer. The well-known ‘Warburg effect’ demonstrates that cancer cells prefer aerobic glycolysis even if there is sufficient ambient oxygen. Accumulating evidence suggests that aerobic glycolysis plays a pivotal role in colorectal cancer (CRC) development. However, few studies have examined the relationship of glycolytic gene clusters with prognosis of CRC patients. Here, our aim is to build a glycolysis-associated gene signature as a biomarker for colorectal cancer. The mRNA sequencing and corresponding clinical data were downloaded from TCGA and GEO databases. Gene set enrichment analysis (GSEA) was performed, indicating that four gene clusters were significantly enriched, which revealed the inextricable relationship of CRC with glycolysis. By comparing gene expression of cancer and adjacent samples, 236 genes were identified. Univariate, multivariate, and LASSO Cox regression analyses screened out five prognostic-related genes (ENO3, GPC1, P4HA1, SPAG4, and STC2). Kaplan–Meier curves and receiver operating characteristic curves (ROC, AUC = 0.766) showed that the risk model could become an effective prognostic indicator (P < 0.001). Multivariate Cox analysis also revealed that this risk model is independent of age and TNM stages. We further validated this risk model in external cohorts (GES38832 and GSE39582), showing these five glycolytic genes could emerge as reliable predictors for CRC patients’ outcomes. Lastly, based on five genes and risk score, we construct a nomogram model assessed by C-index (0.7905) and calibration plot. In conclusion, we highlighted the clinical significance of glycolysis in CRC and constructed a glycolysis-related prognostic model, providing a promising target for glycolysis regulation in CRC. Frontiers Media S.A. 2021-03-04 /pmc/articles/PMC7969881/ /pubmed/33747908 http://dx.doi.org/10.3389/fonc.2021.588811 Text en Copyright © 2021 Zhu, Wang, Bai, Wang, Hao, Zhang and Li http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zhu, Jun
Wang, Shuai
Bai, Han
Wang, Ke
Hao, Jun
Zhang, Jian
Li, Jipeng
Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer
title Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer
title_full Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer
title_fullStr Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer
title_full_unstemmed Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer
title_short Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer
title_sort identification of five glycolysis-related gene signature and risk score model for colorectal cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969881/
https://www.ncbi.nlm.nih.gov/pubmed/33747908
http://dx.doi.org/10.3389/fonc.2021.588811
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