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Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer

BACKGROUND: Metabolic reprograming have been associated with cancer occurrence and progression within the tumor immune microenvironment. However, the prognostic potential of metabolism-related genes in colorectal cancer (CRC) has not been comprehensively studied. Here, we investigated metabolic tran...

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Autores principales: Lin, Dagui, Fan, Wenhua, Zhang, Rongxin, Zhao, Enen, Li, Pansong, Zhou, Wenhao, Peng, Jianhong, Li, Liren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244251/
https://www.ncbi.nlm.nih.gov/pubmed/34193202
http://dx.doi.org/10.1186/s12967-021-02952-w
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author Lin, Dagui
Fan, Wenhua
Zhang, Rongxin
Zhao, Enen
Li, Pansong
Zhou, Wenhao
Peng, Jianhong
Li, Liren
author_facet Lin, Dagui
Fan, Wenhua
Zhang, Rongxin
Zhao, Enen
Li, Pansong
Zhou, Wenhao
Peng, Jianhong
Li, Liren
author_sort Lin, Dagui
collection PubMed
description BACKGROUND: Metabolic reprograming have been associated with cancer occurrence and progression within the tumor immune microenvironment. However, the prognostic potential of metabolism-related genes in colorectal cancer (CRC) has not been comprehensively studied. Here, we investigated metabolic transcript-related CRC subtypes and relevant immune landscapes, and developed a metabolic risk score (MRS) for survival prediction. METHODS: Metabolism-related genes were collected from the Molecular Signatures Database and metabolic subtypes were identified using an unsupervised clustering algorithm based on the expression profiles of survival-related metabolic genes in GSE39582. The ssGSEA and ESTIMATE methods were applied to estimate the immune infiltration among subtypes. The MRS model was developed using LASSO Cox regression in the GSE39582 dataset and independently validated in the TCGA CRC and GSE17537 datasets. RESULTS: We identified two metabolism-related subtypes (cluster-A and cluster-B) of CRC based on the expression profiles of 539 survival-related metabolic genes with distinct immune profiles and notably different prognoses. The cluster-B subtype had a shorter OS and RFS than the cluster-A subtype. Eighteen metabolism-related genes that were mostly involved in lipid metabolism pathways were used to build the MRS in GSE39582. Patients with higher MRS had worse prognosis than those with lower MRS (HR 3.45, P < 0.001). The prognostic role of MRS was validated in the TCGA CRC (HR 2.12, P = 0.00017) and GSE17537 datasets (HR 2.67, P = 0.039). Time-dependent receiver operating characteristic curve and stratified analyses revealed the robust predictive ability of the MRS in each dataset. Multivariate Cox regression analysis indicted that the MRS could predict OS independent of TNM stage and age. CONCLUSIONS: Our study provides novel insight into metabolic heterogeneity and its relationship with immune landscape in CRC. The MRS was identified as a robust prognostic marker and may facilitate individualized therapy for CRC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-021-02952-w.
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spelling pubmed-82442512021-06-30 Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer Lin, Dagui Fan, Wenhua Zhang, Rongxin Zhao, Enen Li, Pansong Zhou, Wenhao Peng, Jianhong Li, Liren J Transl Med Research BACKGROUND: Metabolic reprograming have been associated with cancer occurrence and progression within the tumor immune microenvironment. However, the prognostic potential of metabolism-related genes in colorectal cancer (CRC) has not been comprehensively studied. Here, we investigated metabolic transcript-related CRC subtypes and relevant immune landscapes, and developed a metabolic risk score (MRS) for survival prediction. METHODS: Metabolism-related genes were collected from the Molecular Signatures Database and metabolic subtypes were identified using an unsupervised clustering algorithm based on the expression profiles of survival-related metabolic genes in GSE39582. The ssGSEA and ESTIMATE methods were applied to estimate the immune infiltration among subtypes. The MRS model was developed using LASSO Cox regression in the GSE39582 dataset and independently validated in the TCGA CRC and GSE17537 datasets. RESULTS: We identified two metabolism-related subtypes (cluster-A and cluster-B) of CRC based on the expression profiles of 539 survival-related metabolic genes with distinct immune profiles and notably different prognoses. The cluster-B subtype had a shorter OS and RFS than the cluster-A subtype. Eighteen metabolism-related genes that were mostly involved in lipid metabolism pathways were used to build the MRS in GSE39582. Patients with higher MRS had worse prognosis than those with lower MRS (HR 3.45, P < 0.001). The prognostic role of MRS was validated in the TCGA CRC (HR 2.12, P = 0.00017) and GSE17537 datasets (HR 2.67, P = 0.039). Time-dependent receiver operating characteristic curve and stratified analyses revealed the robust predictive ability of the MRS in each dataset. Multivariate Cox regression analysis indicted that the MRS could predict OS independent of TNM stage and age. CONCLUSIONS: Our study provides novel insight into metabolic heterogeneity and its relationship with immune landscape in CRC. The MRS was identified as a robust prognostic marker and may facilitate individualized therapy for CRC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-021-02952-w. BioMed Central 2021-06-30 /pmc/articles/PMC8244251/ /pubmed/34193202 http://dx.doi.org/10.1186/s12967-021-02952-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lin, Dagui
Fan, Wenhua
Zhang, Rongxin
Zhao, Enen
Li, Pansong
Zhou, Wenhao
Peng, Jianhong
Li, Liren
Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer
title Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer
title_full Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer
title_fullStr Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer
title_full_unstemmed Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer
title_short Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer
title_sort molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8244251/
https://www.ncbi.nlm.nih.gov/pubmed/34193202
http://dx.doi.org/10.1186/s12967-021-02952-w
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