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Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment
BACKGROUND: Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently METHODS: Gene expression profile for OSC...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103916/ https://www.ncbi.nlm.nih.gov/pubmed/33987033 http://dx.doi.org/10.7717/peerj.11375 |
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author | Xu, Youzheng Xu, Yixin Wang, Chun Xia, Baoguo Mu, Qingling Luan, Shaohong Fan, Jun |
author_facet | Xu, Youzheng Xu, Yixin Wang, Chun Xia, Baoguo Mu, Qingling Luan, Shaohong Fan, Jun |
author_sort | Xu, Youzheng |
collection | PubMed |
description | BACKGROUND: Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently METHODS: Gene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival. RESULTS: Eighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival. CONCLUSION: Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment. |
format | Online Article Text |
id | pubmed-8103916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81039162021-05-12 Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment Xu, Youzheng Xu, Yixin Wang, Chun Xia, Baoguo Mu, Qingling Luan, Shaohong Fan, Jun PeerJ Bioinformatics BACKGROUND: Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently METHODS: Gene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival. RESULTS: Eighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival. CONCLUSION: Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment. PeerJ Inc. 2021-05-04 /pmc/articles/PMC8103916/ /pubmed/33987033 http://dx.doi.org/10.7717/peerj.11375 Text en ©2021 Xu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Xu, Youzheng Xu, Yixin Wang, Chun Xia, Baoguo Mu, Qingling Luan, Shaohong Fan, Jun Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment |
title | Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment |
title_full | Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment |
title_fullStr | Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment |
title_full_unstemmed | Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment |
title_short | Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment |
title_sort | mining tcga database for gene expression in ovarian serous cystadenocarcinoma microenvironment |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103916/ https://www.ncbi.nlm.nih.gov/pubmed/33987033 http://dx.doi.org/10.7717/peerj.11375 |
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