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Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis
BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated. METHODS: To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequenc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929804/ https://www.ncbi.nlm.nih.gov/pubmed/36819577 http://dx.doi.org/10.21037/atm-22-6461 |
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author | Li, Haixia Zhou, Quan Wu, Zhangying Lu, Xiaoling |
author_facet | Li, Haixia Zhou, Quan Wu, Zhangying Lu, Xiaoling |
author_sort | Li, Haixia |
collection | PubMed |
description | BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated. METHODS: To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the “gdcDEAnalysis” package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles. RESULTS: There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts. CONCLUSIONS: Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy. |
format | Online Article Text |
id | pubmed-9929804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-99298042023-02-16 Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis Li, Haixia Zhou, Quan Wu, Zhangying Lu, Xiaoling Ann Transl Med Original Article BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated. METHODS: To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the “gdcDEAnalysis” package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles. RESULTS: There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts. CONCLUSIONS: Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy. AME Publishing Company 2023-01-31 2023-01-31 /pmc/articles/PMC9929804/ /pubmed/36819577 http://dx.doi.org/10.21037/atm-22-6461 Text en 2023 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Li, Haixia Zhou, Quan Wu, Zhangying Lu, Xiaoling Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis |
title | Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis |
title_full | Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis |
title_fullStr | Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis |
title_full_unstemmed | Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis |
title_short | Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis |
title_sort | identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9929804/ https://www.ncbi.nlm.nih.gov/pubmed/36819577 http://dx.doi.org/10.21037/atm-22-6461 |
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