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Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer

BACKGROUND: The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. METHODS: We calculated immune an...

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Autores principales: Chen, Wenzheng, Huang, Jianfeng, Xiong, Jianbo, Fu, Pengcheng, Chen, Changyu, Liu, Yi, Li, Zhengrong, Jie, Zhigang, Cao, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420973/
https://www.ncbi.nlm.nih.gov/pubmed/34497681
http://dx.doi.org/10.1155/2021/6290261
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author Chen, Wenzheng
Huang, Jianfeng
Xiong, Jianbo
Fu, Pengcheng
Chen, Changyu
Liu, Yi
Li, Zhengrong
Jie, Zhigang
Cao, Yi
author_facet Chen, Wenzheng
Huang, Jianfeng
Xiong, Jianbo
Fu, Pengcheng
Chen, Changyu
Liu, Yi
Li, Zhengrong
Jie, Zhigang
Cao, Yi
author_sort Chen, Wenzheng
collection PubMed
description BACKGROUND: The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. METHODS: We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME-related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME-related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated. RESULTS: High immune and stromal scores are significantly associated with poor overall survival (p < 0.05). We identified 773 differential TME-related prognostic genes associated with survival; these genes were enriched in immune-related pathways. Nine key prognostic genes were identified and were used to construct a TME-related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high-risk group with worse survival outcomes, based on Kaplan-Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high-risk and low-risk groups. High-risk patients had higher expression of PD-1, PDL-1, and CTLA-4 and lower expression of LAG-3 and VSIR. Infiltration of dendritic cells was higher in the high-risk group. CONCLUSIONS: We identified a novel prognostic TME-related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients.
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spelling pubmed-84209732021-09-07 Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer Chen, Wenzheng Huang, Jianfeng Xiong, Jianbo Fu, Pengcheng Chen, Changyu Liu, Yi Li, Zhengrong Jie, Zhigang Cao, Yi Oxid Med Cell Longev Research Article BACKGROUND: The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. METHODS: We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME-related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME-related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated. RESULTS: High immune and stromal scores are significantly associated with poor overall survival (p < 0.05). We identified 773 differential TME-related prognostic genes associated with survival; these genes were enriched in immune-related pathways. Nine key prognostic genes were identified and were used to construct a TME-related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high-risk group with worse survival outcomes, based on Kaplan-Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high-risk and low-risk groups. High-risk patients had higher expression of PD-1, PDL-1, and CTLA-4 and lower expression of LAG-3 and VSIR. Infiltration of dendritic cells was higher in the high-risk group. CONCLUSIONS: We identified a novel prognostic TME-related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients. Hindawi 2021-08-14 /pmc/articles/PMC8420973/ /pubmed/34497681 http://dx.doi.org/10.1155/2021/6290261 Text en Copyright © 2021 Wenzheng Chen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chen, Wenzheng
Huang, Jianfeng
Xiong, Jianbo
Fu, Pengcheng
Chen, Changyu
Liu, Yi
Li, Zhengrong
Jie, Zhigang
Cao, Yi
Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer
title Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer
title_full Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer
title_fullStr Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer
title_full_unstemmed Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer
title_short Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer
title_sort identification of a tumor microenvironment-related gene signature indicative of disease prognosis and treatment response in colon cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420973/
https://www.ncbi.nlm.nih.gov/pubmed/34497681
http://dx.doi.org/10.1155/2021/6290261
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