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Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment

BACKGROUND: Colon cancer is a disease with high malignancy and incidence in the world. Tumor immune microenvironment (TIM) and tumor mutational burden (TMB) have been proved to play crucial roles in predicting clinical outcomes and therapeutic efficacy, but the correlation between them and the under...

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Autores principales: Wang, Xinyi, Duanmu, Jinzhong, Fu, Xiaorui, Li, Taiyuan, Jiang, Qunguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456375/
https://www.ncbi.nlm.nih.gov/pubmed/32859214
http://dx.doi.org/10.1186/s12967-020-02491-w
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author Wang, Xinyi
Duanmu, Jinzhong
Fu, Xiaorui
Li, Taiyuan
Jiang, Qunguang
author_facet Wang, Xinyi
Duanmu, Jinzhong
Fu, Xiaorui
Li, Taiyuan
Jiang, Qunguang
author_sort Wang, Xinyi
collection PubMed
description BACKGROUND: Colon cancer is a disease with high malignancy and incidence in the world. Tumor immune microenvironment (TIM) and tumor mutational burden (TMB) have been proved to play crucial roles in predicting clinical outcomes and therapeutic efficacy, but the correlation between them and the underlying mechanism were not completely understood in colon cancer. METHODS: In this study, we used Single-Sample Gene Set Enrichment Analysis (ssGSEA) and unsupervised consensus clustering analysis to divide patients from the TCGA cohort into three immune subgroups. Then we validated their differences in immune cell infiltration, overall survival outcomes, clinical phenotypes and expression levels of HLA and checkpoint genes by Mann–Whitney tests. We performed weighted correlation network analysis (WGCNA) to obtain immunity-related module and hub genes. Then we explored the underlying mechanism of hub genes by gene set enrichment analysis (GSEA) and gene set evaluation analysis (GSVA). Finally, we gave an overall view of gene variants and verified the correlation between TIM and TMB by comparing microsatellite instability (MSI) and gene mutations among three immune subgroups. RESULTS: The colon cancer patients were clustered into low immunity, median immunity and high immunity groups. The median immunity group had a favorable survival probability compared with that of the low and high immunity groups. Three groups had significant differences in immune cell infiltration, tumor stage, living state and T classification. We got 8 hub genes (CCDC69, CLMP, FAM110B, FAM129A, GUCY1B3, PALLD, PLEKHO1 and STY11) and predicted that immunity may correlated with inflammatory response, KRAS signaling pathway and T cell infiltration. With higher immunity, the TMB was higher. The most frequent mutations in low and median immunity groups were APC, TP53 and KRAS, while TTN and MUC16 showed higher mutational frequency in high immunity group. CONCLUSIONS: We performed a comprehensive evaluation of the immune microenvironment landscape of colon cancer and demonstrated the positive correlation between immunity and TMB. The hub genes and frequently mutated genes were strongly related to immunity and may give suggestion for immunotherapy in the future.
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spelling pubmed-74563752020-08-31 Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment Wang, Xinyi Duanmu, Jinzhong Fu, Xiaorui Li, Taiyuan Jiang, Qunguang J Transl Med Research BACKGROUND: Colon cancer is a disease with high malignancy and incidence in the world. Tumor immune microenvironment (TIM) and tumor mutational burden (TMB) have been proved to play crucial roles in predicting clinical outcomes and therapeutic efficacy, but the correlation between them and the underlying mechanism were not completely understood in colon cancer. METHODS: In this study, we used Single-Sample Gene Set Enrichment Analysis (ssGSEA) and unsupervised consensus clustering analysis to divide patients from the TCGA cohort into three immune subgroups. Then we validated their differences in immune cell infiltration, overall survival outcomes, clinical phenotypes and expression levels of HLA and checkpoint genes by Mann–Whitney tests. We performed weighted correlation network analysis (WGCNA) to obtain immunity-related module and hub genes. Then we explored the underlying mechanism of hub genes by gene set enrichment analysis (GSEA) and gene set evaluation analysis (GSVA). Finally, we gave an overall view of gene variants and verified the correlation between TIM and TMB by comparing microsatellite instability (MSI) and gene mutations among three immune subgroups. RESULTS: The colon cancer patients were clustered into low immunity, median immunity and high immunity groups. The median immunity group had a favorable survival probability compared with that of the low and high immunity groups. Three groups had significant differences in immune cell infiltration, tumor stage, living state and T classification. We got 8 hub genes (CCDC69, CLMP, FAM110B, FAM129A, GUCY1B3, PALLD, PLEKHO1 and STY11) and predicted that immunity may correlated with inflammatory response, KRAS signaling pathway and T cell infiltration. With higher immunity, the TMB was higher. The most frequent mutations in low and median immunity groups were APC, TP53 and KRAS, while TTN and MUC16 showed higher mutational frequency in high immunity group. CONCLUSIONS: We performed a comprehensive evaluation of the immune microenvironment landscape of colon cancer and demonstrated the positive correlation between immunity and TMB. The hub genes and frequently mutated genes were strongly related to immunity and may give suggestion for immunotherapy in the future. BioMed Central 2020-08-28 /pmc/articles/PMC7456375/ /pubmed/32859214 http://dx.doi.org/10.1186/s12967-020-02491-w Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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, Xinyi
Duanmu, Jinzhong
Fu, Xiaorui
Li, Taiyuan
Jiang, Qunguang
Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment
title Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment
title_full Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment
title_fullStr Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment
title_full_unstemmed Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment
title_short Analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment
title_sort analyzing and validating the prognostic value and mechanism of colon cancer immune microenvironment
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7456375/
https://www.ncbi.nlm.nih.gov/pubmed/32859214
http://dx.doi.org/10.1186/s12967-020-02491-w
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