Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma

Abstract Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenviro...

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Autores principales:	Pu Cheng, Jiong Ma, Xia Zheng, Chunxia Zhou, Xuejun Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d9285665ecfc45b080d29af6f5e1e6ef
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spelling	oai:doaj.org-article:d9285665ecfc45b080d29af6f5e1e6ef2021-12-02T16:04:30ZBioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma10.1038/s41598-021-92091-52045-2322https://doaj.org/article/d9285665ecfc45b080d29af6f5e1e6ef2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92091-5https://doaj.org/toc/2045-2322Abstract Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.Pu ChengJiong MaXia ZhengChunxia ZhouXuejun ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pu Cheng Jiong Ma Xia Zheng Chunxia Zhou Xuejun Chen Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
description	Abstract Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.
format	article
author	Pu Cheng Jiong Ma Xia Zheng Chunxia Zhou Xuejun Chen
author_facet	Pu Cheng Jiong Ma Xia Zheng Chunxia Zhou Xuejun Chen
author_sort	Pu Cheng
title	Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
title_short	Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
title_full	Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
title_fullStr	Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
title_full_unstemmed	Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
title_sort	bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d9285665ecfc45b080d29af6f5e1e6ef
work_keys_str_mv	AT pucheng bioinformaticprofilingidentifiesprognosisrelatedgenesintheimmunemicroenvironmentofendometrialcarcinoma AT jiongma bioinformaticprofilingidentifiesprognosisrelatedgenesintheimmunemicroenvironmentofendometrialcarcinoma AT xiazheng bioinformaticprofilingidentifiesprognosisrelatedgenesintheimmunemicroenvironmentofendometrialcarcinoma AT chunxiazhou bioinformaticprofilingidentifiesprognosisrelatedgenesintheimmunemicroenvironmentofendometrialcarcinoma AT xuejunchen bioinformaticprofilingidentifiesprognosisrelatedgenesintheimmunemicroenvironmentofendometrialcarcinoma
_version_	1718385237414641664

Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma

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