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Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients
BACKGROUND: Despite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to for...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746810/ https://www.ncbi.nlm.nih.gov/pubmed/33343640 http://dx.doi.org/10.3389/fgene.2020.607009 |
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author | Dai, Siqi Xu, Shuang Ye, Yao Ding, Kefeng |
author_facet | Dai, Siqi Xu, Shuang Ye, Yao Ding, Kefeng |
author_sort | Dai, Siqi |
collection | PubMed |
description | BACKGROUND: Despite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to forecast patient’s prognosis. METHODS: IRGs were collected from the ImmPort database. The CRC dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another two CRC datasets from the Gene Expression Omnibus (GEO). Gene function enrichment analysis was conducted. A prognostic nomogram was built incorporating the IRG signature with clinical risk factors. RESULTS: The three datasets had 487, 579, and 224 patients, respectively. A prognostic six-gene-signature (CCL22, LIMK1, MAPKAPK3, FLOT1, GPRC5B, and IL20RB) was developed through feature selection that showed good differentiation between the low- and high-risk groups in the training set (p < 0.001), which was later confirmed in the two validation groups (log-rank p < 0.05). The signature outperformed tumor TNM staging for survival prediction. GO and KEGG functional annotation analysis suggested that the signature was significantly enriched in metabolic processes and regulation of immunity (p < 0.05). When combined with clinical risk factors, the model showed robust prediction capability. CONCLUSION: The immune-related six-gene signature is a reliable prognostic indicator for CRC patients and could provide insight for personalized cancer management. |
format | Online Article Text |
id | pubmed-7746810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77468102020-12-19 Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients Dai, Siqi Xu, Shuang Ye, Yao Ding, Kefeng Front Genet Genetics BACKGROUND: Despite recent advance in immune therapy, great heterogeneity exists in the outcomes of colorectal cancer (CRC) patients. In this study, we aimed to analyze the immune-related gene (IRG) expression profiles from three independent public databases and develop an effective signature to forecast patient’s prognosis. METHODS: IRGs were collected from the ImmPort database. The CRC dataset from The Cancer Genome Atlas (TCGA) database was used to identify a prognostic gene signature, which was verified in another two CRC datasets from the Gene Expression Omnibus (GEO). Gene function enrichment analysis was conducted. A prognostic nomogram was built incorporating the IRG signature with clinical risk factors. RESULTS: The three datasets had 487, 579, and 224 patients, respectively. A prognostic six-gene-signature (CCL22, LIMK1, MAPKAPK3, FLOT1, GPRC5B, and IL20RB) was developed through feature selection that showed good differentiation between the low- and high-risk groups in the training set (p < 0.001), which was later confirmed in the two validation groups (log-rank p < 0.05). The signature outperformed tumor TNM staging for survival prediction. GO and KEGG functional annotation analysis suggested that the signature was significantly enriched in metabolic processes and regulation of immunity (p < 0.05). When combined with clinical risk factors, the model showed robust prediction capability. CONCLUSION: The immune-related six-gene signature is a reliable prognostic indicator for CRC patients and could provide insight for personalized cancer management. Frontiers Media S.A. 2020-12-04 /pmc/articles/PMC7746810/ /pubmed/33343640 http://dx.doi.org/10.3389/fgene.2020.607009 Text en Copyright © 2020 Dai, Xu, Ye and Ding. 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 | Genetics Dai, Siqi Xu, Shuang Ye, Yao Ding, Kefeng Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients |
title | Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients |
title_full | Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients |
title_fullStr | Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients |
title_full_unstemmed | Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients |
title_short | Identification of an Immune-Related Gene Signature to Improve Prognosis Prediction in Colorectal Cancer Patients |
title_sort | identification of an immune-related gene signature to improve prognosis prediction in colorectal cancer patients |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7746810/ https://www.ncbi.nlm.nih.gov/pubmed/33343640 http://dx.doi.org/10.3389/fgene.2020.607009 |
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