Cargando…

Identification of a ferroptosis-related gene signature predictive model in colon cancer

BACKGROUND: The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. MET...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Ye, Xia, Heng-bo, Chen, Zhang-ming, Meng, Lei, Xu, A-man
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086290/
https://www.ncbi.nlm.nih.gov/pubmed/33926457
http://dx.doi.org/10.1186/s12957-021-02244-z
_version_ 1783686494066573312
author Wang, Ye
Xia, Heng-bo
Chen, Zhang-ming
Meng, Lei
Xu, A-man
author_facet Wang, Ye
Xia, Heng-bo
Chen, Zhang-ming
Meng, Lei
Xu, A-man
author_sort Wang, Ye
collection PubMed
description BACKGROUND: The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. METHODS: The expression profiles of FRGs and relevant clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression model were performed to build a prognostic model in TCGA cohort. RESULTS: Ten FRGs, five of which had mutation rates ≥ 3%, were found to be related to the overall survival (OS) of patients with CC. Patients were divided into high- and low-risk groups based on the results of Cox regression and LASSO analysis. Patients in the low-risk group had a significantly longer survival time than patients in the high-risk group (P < 0.001). Enrichment analyses in different risk groups showed that the altered genes were associated with the extracellular matrix, fatty acid metabolism, and peroxisome. Age, risk score, T stage, N stage, and M stage were independent predictors of patient OS based on the results of Cox analysis. Finally, a nomogram was constructed to predict 1-, 3-, and 5-year OS of patients with CC based on the above five independent factors. CONCLUSION: A novel FRG model can be used for prognostic prediction in CC and may be helpful for individualized treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-021-02244-z.
format Online
Article
Text
id pubmed-8086290
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-80862902021-04-30 Identification of a ferroptosis-related gene signature predictive model in colon cancer Wang, Ye Xia, Heng-bo Chen, Zhang-ming Meng, Lei Xu, A-man World J Surg Oncol Research BACKGROUND: The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. METHODS: The expression profiles of FRGs and relevant clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression model were performed to build a prognostic model in TCGA cohort. RESULTS: Ten FRGs, five of which had mutation rates ≥ 3%, were found to be related to the overall survival (OS) of patients with CC. Patients were divided into high- and low-risk groups based on the results of Cox regression and LASSO analysis. Patients in the low-risk group had a significantly longer survival time than patients in the high-risk group (P < 0.001). Enrichment analyses in different risk groups showed that the altered genes were associated with the extracellular matrix, fatty acid metabolism, and peroxisome. Age, risk score, T stage, N stage, and M stage were independent predictors of patient OS based on the results of Cox analysis. Finally, a nomogram was constructed to predict 1-, 3-, and 5-year OS of patients with CC based on the above five independent factors. CONCLUSION: A novel FRG model can be used for prognostic prediction in CC and may be helpful for individualized treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-021-02244-z. BioMed Central 2021-04-29 /pmc/articles/PMC8086290/ /pubmed/33926457 http://dx.doi.org/10.1186/s12957-021-02244-z 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
Wang, Ye
Xia, Heng-bo
Chen, Zhang-ming
Meng, Lei
Xu, A-man
Identification of a ferroptosis-related gene signature predictive model in colon cancer
title Identification of a ferroptosis-related gene signature predictive model in colon cancer
title_full Identification of a ferroptosis-related gene signature predictive model in colon cancer
title_fullStr Identification of a ferroptosis-related gene signature predictive model in colon cancer
title_full_unstemmed Identification of a ferroptosis-related gene signature predictive model in colon cancer
title_short Identification of a ferroptosis-related gene signature predictive model in colon cancer
title_sort identification of a ferroptosis-related gene signature predictive model in colon cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8086290/
https://www.ncbi.nlm.nih.gov/pubmed/33926457
http://dx.doi.org/10.1186/s12957-021-02244-z
work_keys_str_mv AT wangye identificationofaferroptosisrelatedgenesignaturepredictivemodelincoloncancer
AT xiahengbo identificationofaferroptosisrelatedgenesignaturepredictivemodelincoloncancer
AT chenzhangming identificationofaferroptosisrelatedgenesignaturepredictivemodelincoloncancer
AT menglei identificationofaferroptosisrelatedgenesignaturepredictivemodelincoloncancer
AT xuaman identificationofaferroptosisrelatedgenesignaturepredictivemodelincoloncancer