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

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-rel...

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Autores principales: Cheng, Pu, Ma, Jiong, Zheng, Xia, Zhou, Chunxia, Chen, Xuejun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206132/
https://www.ncbi.nlm.nih.gov/pubmed/34131259
http://dx.doi.org/10.1038/s41598-021-92091-5
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author Cheng, Pu
Ma, Jiong
Zheng, Xia
Zhou, Chunxia
Chen, Xuejun
author_facet Cheng, Pu
Ma, Jiong
Zheng, Xia
Zhou, Chunxia
Chen, Xuejun
author_sort Cheng, Pu
collection PubMed
description 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.
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spelling pubmed-82061322021-06-16 Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma Cheng, Pu Ma, Jiong Zheng, Xia Zhou, Chunxia Chen, Xuejun Sci Rep Article 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. Nature Publishing Group UK 2021-06-15 /pmc/articles/PMC8206132/ /pubmed/34131259 http://dx.doi.org/10.1038/s41598-021-92091-5 Text en © The Author(s) 2021 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/) .
spellingShingle Article
Cheng, Pu
Ma, Jiong
Zheng, Xia
Zhou, Chunxia
Chen, Xuejun
Bioinformatic profiling identifies prognosis-related genes in the immune microenvironment of endometrial carcinoma
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206132/
https://www.ncbi.nlm.nih.gov/pubmed/34131259
http://dx.doi.org/10.1038/s41598-021-92091-5
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