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Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature
BACKGROUND: The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression. METHODS: The TME pa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436413/ https://www.ncbi.nlm.nih.gov/pubmed/37596528 http://dx.doi.org/10.1186/s12885-023-11277-4 |
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author | Guo, Xian-wen Lei, Rong-e Zhou, Qing-nan Zhang, Guo Hu, Bang-li Liang, Yun-xiao |
author_facet | Guo, Xian-wen Lei, Rong-e Zhou, Qing-nan Zhang, Guo Hu, Bang-li Liang, Yun-xiao |
author_sort | Guo, Xian-wen |
collection | PubMed |
description | BACKGROUND: The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression. METHODS: The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts. RESULTS: The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples. CONCLUSION: Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy. |
format | Online Article Text |
id | pubmed-10436413 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104364132023-08-19 Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature Guo, Xian-wen Lei, Rong-e Zhou, Qing-nan Zhang, Guo Hu, Bang-li Liang, Yun-xiao BMC Cancer Research BACKGROUND: The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression. METHODS: The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts. RESULTS: The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples. CONCLUSION: Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy. BioMed Central 2023-08-18 /pmc/articles/PMC10436413/ /pubmed/37596528 http://dx.doi.org/10.1186/s12885-023-11277-4 Text en © The Author(s) 2023 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/) . 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 Guo, Xian-wen Lei, Rong-e Zhou, Qing-nan Zhang, Guo Hu, Bang-li Liang, Yun-xiao Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature |
title | Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature |
title_full | Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature |
title_fullStr | Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature |
title_full_unstemmed | Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature |
title_short | Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature |
title_sort | tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10436413/ https://www.ncbi.nlm.nih.gov/pubmed/37596528 http://dx.doi.org/10.1186/s12885-023-11277-4 |
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