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Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis
BACKGROUND: Ovarian cancer is one of the most lethal malignant gynecologic tumors worldwide. We aimed to identify critical hallmarks of the surface epithelium between normal ovaries and serous ovarian carcinomas and then obtain the hub genes associated with these critical hallmarks. METHODS: We chos...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8797326/ https://www.ncbi.nlm.nih.gov/pubmed/35116413 http://dx.doi.org/10.21037/tcr-20-2604 |
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author | Su, Rongjia Jin, Chengjuan Jin, Chengwen Kuang, Menghua Xiang, Jiangdong |
author_facet | Su, Rongjia Jin, Chengjuan Jin, Chengwen Kuang, Menghua Xiang, Jiangdong |
author_sort | Su, Rongjia |
collection | PubMed |
description | BACKGROUND: Ovarian cancer is one of the most lethal malignant gynecologic tumors worldwide. We aimed to identify critical hallmarks of the surface epithelium between normal ovaries and serous ovarian carcinomas and then obtain the hub genes associated with these critical hallmarks. METHODS: We chose GSE36668, GSE54388 and GSE69428 as data sources and then determined the common gene sets through gene set enrichment analysis (GSEA) to explore essential hallmarks and hub genes driving normal ovarian cells to evolve progressively into a neoplastic state. The differentially expressed genes (DEGs) extracted separately in each gene set were analyzed again through the Metascape website. Kaplan-Meier plotter was used to obtain significant prognostic information. The hub genes were obtained through protein-protein interaction (PPI) network by Cytoscape. Hub genes were confirmed to have higher mRNA and protein expression levels in ovarian cancer tissues than in normal tissues through public databases [Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas]. Correlation analysis of six hub genes showed a strong correlation via R. RESULTS: We obtained 11 common hallmarks from GSEA of the three mentioned datasets and 22 hub genes that showed a significant association with poor survival. CONCLUSIONS: Not only the gene sets enriched by GSEA but also the hub genes extracted by the PPI network indicate that the most fundamental trait of ovarian cancer cells involves their ability to sustain chronic proliferation. |
format | Online Article Text |
id | pubmed-8797326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-87973262022-02-02 Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis Su, Rongjia Jin, Chengjuan Jin, Chengwen Kuang, Menghua Xiang, Jiangdong Transl Cancer Res Original Article BACKGROUND: Ovarian cancer is one of the most lethal malignant gynecologic tumors worldwide. We aimed to identify critical hallmarks of the surface epithelium between normal ovaries and serous ovarian carcinomas and then obtain the hub genes associated with these critical hallmarks. METHODS: We chose GSE36668, GSE54388 and GSE69428 as data sources and then determined the common gene sets through gene set enrichment analysis (GSEA) to explore essential hallmarks and hub genes driving normal ovarian cells to evolve progressively into a neoplastic state. The differentially expressed genes (DEGs) extracted separately in each gene set were analyzed again through the Metascape website. Kaplan-Meier plotter was used to obtain significant prognostic information. The hub genes were obtained through protein-protein interaction (PPI) network by Cytoscape. Hub genes were confirmed to have higher mRNA and protein expression levels in ovarian cancer tissues than in normal tissues through public databases [Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas]. Correlation analysis of six hub genes showed a strong correlation via R. RESULTS: We obtained 11 common hallmarks from GSEA of the three mentioned datasets and 22 hub genes that showed a significant association with poor survival. CONCLUSIONS: Not only the gene sets enriched by GSEA but also the hub genes extracted by the PPI network indicate that the most fundamental trait of ovarian cancer cells involves their ability to sustain chronic proliferation. AME Publishing Company 2021-02 /pmc/articles/PMC8797326/ /pubmed/35116413 http://dx.doi.org/10.21037/tcr-20-2604 Text en 2021 Translational Cancer Research. 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/. |
spellingShingle | Original Article Su, Rongjia Jin, Chengjuan Jin, Chengwen Kuang, Menghua Xiang, Jiangdong Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis |
title | Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis |
title_full | Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis |
title_fullStr | Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis |
title_full_unstemmed | Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis |
title_short | Identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis |
title_sort | identification of hub genes in key hallmarks of ovarian cancer via bioinformatics analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8797326/ https://www.ncbi.nlm.nih.gov/pubmed/35116413 http://dx.doi.org/10.21037/tcr-20-2604 |
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