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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer
BACKGROUND: This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. METHODS: Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus d...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236200/ https://www.ncbi.nlm.nih.gov/pubmed/34174849 http://dx.doi.org/10.1186/s12885-021-08412-4 |
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author | Wu, Baojie Xi, Shuyi |
author_facet | Wu, Baojie Xi, Shuyi |
author_sort | Wu, Baojie |
collection | PubMed |
description | BACKGROUND: This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. METHODS: Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. RESULTS: A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. CONCLUSIONS: These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08412-4. |
format | Online Article Text |
id | pubmed-8236200 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82362002021-06-28 Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer Wu, Baojie Xi, Shuyi BMC Cancer Research BACKGROUND: This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. METHODS: Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. RESULTS: A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. CONCLUSIONS: These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08412-4. BioMed Central 2021-06-26 /pmc/articles/PMC8236200/ /pubmed/34174849 http://dx.doi.org/10.1186/s12885-021-08412-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Wu, Baojie Xi, Shuyi Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer |
title | Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer |
title_full | Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer |
title_fullStr | Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer |
title_full_unstemmed | Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer |
title_short | Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer |
title_sort | bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236200/ https://www.ncbi.nlm.nih.gov/pubmed/34174849 http://dx.doi.org/10.1186/s12885-021-08412-4 |
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