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

Abstract 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 as...

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Autores principales:	Yichen Wang, Jingkai Zhang, Yijun Zhou, Zhiguang Li, Dekang Lv, Quentin Liu
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Endometrial cancer Immune microenvironment Prognosis Nomogram Immune status Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/279261fd74b24404aa31fc54689c72a1
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spelling	oai:doaj.org-article:279261fd74b24404aa31fc54689c72a12021-11-14T12:29:55ZConstruction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer10.1186/s12885-021-08935-w1471-2407https://doaj.org/article/279261fd74b24404aa31fc54689c72a12021-11-01T00:00:00Zhttps://doi.org/10.1186/s12885-021-08935-whttps://doaj.org/toc/1471-2407Abstract 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.Yichen WangJingkai ZhangYijun ZhouZhiguang LiDekang LvQuentin LiuBMCarticleEndometrial cancerImmune microenvironmentPrognosisNomogramImmune statusNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENBMC Cancer, Vol 21, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Endometrial cancer Immune microenvironment Prognosis Nomogram Immune status Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Endometrial cancer Immune microenvironment Prognosis Nomogram Immune status Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Yichen Wang Jingkai Zhang Yijun Zhou Zhiguang Li Dekang Lv Quentin Liu Construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
description	Abstract 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.
format	article
author	Yichen Wang Jingkai Zhang Yijun Zhou Zhiguang Li Dekang Lv Quentin Liu
author_facet	Yichen Wang Jingkai Zhang Yijun Zhou Zhiguang Li Dekang Lv Quentin Liu
author_sort	Yichen Wang
title	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_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_sort	construction of a microenvironment immune gene model for predicting the prognosis of endometrial cancer
publisher	BMC
publishDate	2021
url	https://doaj.org/article/279261fd74b24404aa31fc54689c72a1
work_keys_str_mv	AT yichenwang constructionofamicroenvironmentimmunegenemodelforpredictingtheprognosisofendometrialcancer AT jingkaizhang constructionofamicroenvironmentimmunegenemodelforpredictingtheprognosisofendometrialcancer AT yijunzhou constructionofamicroenvironmentimmunegenemodelforpredictingtheprognosisofendometrialcancer AT zhiguangli constructionofamicroenvironmentimmunegenemodelforpredictingtheprognosisofendometrialcancer AT dekanglv constructionofamicroenvironmentimmunegenemodelforpredictingtheprognosisofendometrialcancer AT quentinliu constructionofamicroenvironmentimmunegenemodelforpredictingtheprognosisofendometrialcancer
_version_	1718429155375185920

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

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