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Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival
Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to esta...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9485806/ https://www.ncbi.nlm.nih.gov/pubmed/36147494 http://dx.doi.org/10.3389/fgene.2022.989327 |
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author | Huang, Yanpeng Zhou, Jinming Zhong, Haibin Xie, Ning Zhang, Fei-Ran Zhang, Zhanmin |
author_facet | Huang, Yanpeng Zhou, Jinming Zhong, Haibin Xie, Ning Zhang, Fei-Ran Zhang, Zhanmin |
author_sort | Huang, Yanpeng |
collection | PubMed |
description | Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using “ConsensusClusterPlus” based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients. |
format | Online Article Text |
id | pubmed-9485806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94858062022-09-21 Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival Huang, Yanpeng Zhou, Jinming Zhong, Haibin Xie, Ning Zhang, Fei-Ran Zhang, Zhanmin Front Genet Genetics Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using “ConsensusClusterPlus” based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients. Frontiers Media S.A. 2022-09-06 /pmc/articles/PMC9485806/ /pubmed/36147494 http://dx.doi.org/10.3389/fgene.2022.989327 Text en Copyright © 2022 Huang, Zhou, Zhong, Xie, Zhang and Zhang. https://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 | Genetics Huang, Yanpeng Zhou, Jinming Zhong, Haibin Xie, Ning Zhang, Fei-Ran Zhang, Zhanmin Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival |
title | Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival |
title_full | Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival |
title_fullStr | Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival |
title_full_unstemmed | Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival |
title_short | Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival |
title_sort | identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9485806/ https://www.ncbi.nlm.nih.gov/pubmed/36147494 http://dx.doi.org/10.3389/fgene.2022.989327 |
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