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An age stratified analysis of the biomarkers in patients with colorectal cancer

Colorectal cancer (CRC), a common malignant tumor of the digestive tract, has a high incidence and mortality rate. Several recent studies have found that aging is associated with the increasing risk of cancer. Nevertheless, the expression status and function of age-related genes in CRC is still not...

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Autores principales: Yao, Hui, Li, Chengjie, Tan, Xiaodong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8599678/
https://www.ncbi.nlm.nih.gov/pubmed/34789836
http://dx.doi.org/10.1038/s41598-021-01850-x
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author Yao, Hui
Li, Chengjie
Tan, Xiaodong
author_facet Yao, Hui
Li, Chengjie
Tan, Xiaodong
author_sort Yao, Hui
collection PubMed
description Colorectal cancer (CRC), a common malignant tumor of the digestive tract, has a high incidence and mortality rate. Several recent studies have found that aging is associated with the increasing risk of cancer. Nevertheless, the expression status and function of age-related genes in CRC is still not well understood. In the study, we comprehensively analyzed the gene expression data of CRC patients from The Cancer Genome Atlas (TCGA) database. Age-related differential expression genes (age-related DEGs) in tumor tissues compared with normal tissues of CRC were further identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of age-related DEGs were performed by clusterProfiler of R. Afterwards, we used the STRING database to map the protein–protein interaction network of DEGs. We constructed prognostic model through univariate and multivariate COX regression analyses, and further evaluated their predictive power. The prognostic gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA). The weighted gene co-expression network analysis (WGCNA) was used to identify key module associated with two prognostic gene signatures. Finally, we used the Metascape to perform functional enrichment analysis of genes in the key module. A total of 279 age-related DEGs were identified from the TCGA database. GO and KEGG enrichment analysis showed that the age-related DEGs were enriched in the Modulation of chemical synaptic transmission and Neuroactive ligand–receptor interaction. Moreover, we established a novel age-related gene signature (DLX2 and PCOLCE2) for overall survival in CRC, which was further predicted in both the training and validation sets. The results of GSEA demonstrated that numerous disease-related pathways were enriched in the high-risk group. We identified 43 genes related to the DLX2 and PCOLCE2 by the WGCNA co-expression network. We also found that these 43 genes were enriched in the cancer-related pathways. To sum up, the study identified an age-related gene signature for predicting the prognosis of CRC patients, which is conducive to the identification of novel prognostic molecular markers.
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spelling pubmed-85996782021-11-19 An age stratified analysis of the biomarkers in patients with colorectal cancer Yao, Hui Li, Chengjie Tan, Xiaodong Sci Rep Article Colorectal cancer (CRC), a common malignant tumor of the digestive tract, has a high incidence and mortality rate. Several recent studies have found that aging is associated with the increasing risk of cancer. Nevertheless, the expression status and function of age-related genes in CRC is still not well understood. In the study, we comprehensively analyzed the gene expression data of CRC patients from The Cancer Genome Atlas (TCGA) database. Age-related differential expression genes (age-related DEGs) in tumor tissues compared with normal tissues of CRC were further identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of age-related DEGs were performed by clusterProfiler of R. Afterwards, we used the STRING database to map the protein–protein interaction network of DEGs. We constructed prognostic model through univariate and multivariate COX regression analyses, and further evaluated their predictive power. The prognostic gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA). The weighted gene co-expression network analysis (WGCNA) was used to identify key module associated with two prognostic gene signatures. Finally, we used the Metascape to perform functional enrichment analysis of genes in the key module. A total of 279 age-related DEGs were identified from the TCGA database. GO and KEGG enrichment analysis showed that the age-related DEGs were enriched in the Modulation of chemical synaptic transmission and Neuroactive ligand–receptor interaction. Moreover, we established a novel age-related gene signature (DLX2 and PCOLCE2) for overall survival in CRC, which was further predicted in both the training and validation sets. The results of GSEA demonstrated that numerous disease-related pathways were enriched in the high-risk group. We identified 43 genes related to the DLX2 and PCOLCE2 by the WGCNA co-expression network. We also found that these 43 genes were enriched in the cancer-related pathways. To sum up, the study identified an age-related gene signature for predicting the prognosis of CRC patients, which is conducive to the identification of novel prognostic molecular markers. Nature Publishing Group UK 2021-11-17 /pmc/articles/PMC8599678/ /pubmed/34789836 http://dx.doi.org/10.1038/s41598-021-01850-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Article
Yao, Hui
Li, Chengjie
Tan, Xiaodong
An age stratified analysis of the biomarkers in patients with colorectal cancer
title An age stratified analysis of the biomarkers in patients with colorectal cancer
title_full An age stratified analysis of the biomarkers in patients with colorectal cancer
title_fullStr An age stratified analysis of the biomarkers in patients with colorectal cancer
title_full_unstemmed An age stratified analysis of the biomarkers in patients with colorectal cancer
title_short An age stratified analysis of the biomarkers in patients with colorectal cancer
title_sort age stratified analysis of the biomarkers in patients with colorectal cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8599678/
https://www.ncbi.nlm.nih.gov/pubmed/34789836
http://dx.doi.org/10.1038/s41598-021-01850-x
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