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Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer

BACKGROUND: Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the associatio...

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Autores principales: Wang, Yichen, Zhang, Jingkai, Zhou, Yijun, Li, Zhiguang, Lv, Dekang, Liu, Quentin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588713/
https://www.ncbi.nlm.nih.gov/pubmed/34763648
http://dx.doi.org/10.1186/s12885-021-08935-w
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author Wang, Yichen
Zhang, Jingkai
Zhou, Yijun
Li, Zhiguang
Lv, Dekang
Liu, Quentin
author_facet Wang, Yichen
Zhang, Jingkai
Zhou, Yijun
Li, Zhiguang
Lv, Dekang
Liu, Quentin
author_sort Wang, Yichen
collection PubMed
description BACKGROUND: Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC. METHODS: We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein–protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences. RESULTS: Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status. CONCLUSION: Our study provides new insights for novel biomarkers and immunotherapy targets in EC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08935-w.
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spelling pubmed-85887132021-11-15 Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer Wang, Yichen Zhang, Jingkai Zhou, Yijun Li, Zhiguang Lv, Dekang Liu, Quentin BMC Cancer Research BACKGROUND: Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC. METHODS: We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein–protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences. RESULTS: Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status. CONCLUSION: Our study provides new insights for novel biomarkers and immunotherapy targets in EC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08935-w. BioMed Central 2021-11-11 /pmc/articles/PMC8588713/ /pubmed/34763648 http://dx.doi.org/10.1186/s12885-021-08935-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (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
Wang, Yichen
Zhang, Jingkai
Zhou, Yijun
Li, Zhiguang
Lv, Dekang
Liu, Quentin
Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
title Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
title_full Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
title_fullStr Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
title_full_unstemmed Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
title_short Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
title_sort construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8588713/
https://www.ncbi.nlm.nih.gov/pubmed/34763648
http://dx.doi.org/10.1186/s12885-021-08935-w
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