Cargando…

Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics

Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression p...

Descripción completa

Detalles Bibliográficos
Autores principales: Alur, Varun Chandra, Raju, Varshita, Vastrad, Basavaraj, Vastrad, Chanabasayya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628368/
https://www.ncbi.nlm.nih.gov/pubmed/30970615
http://dx.doi.org/10.3390/diagnostics9020039
_version_ 1783434943619137536
author Alur, Varun Chandra
Raju, Varshita
Vastrad, Basavaraj
Vastrad, Chanabasayya
author_facet Alur, Varun Chandra
Raju, Varshita
Vastrad, Basavaraj
Vastrad, Chanabasayya
author_sort Alur, Varun Chandra
collection PubMed
description Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C), VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC.
format Online
Article
Text
id pubmed-6628368
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-66283682019-07-23 Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics Alur, Varun Chandra Raju, Varshita Vastrad, Basavaraj Vastrad, Chanabasayya Diagnostics (Basel) Article Epithelial ovarian cancer (EOC) is the18th most common cancer worldwide and the 8th most common in women. The aim of this study was to diagnose the potential importance of, as well as novel genes linked with, EOC and to provide valid biological information for further research. The gene expression profiles of E-MTAB-3706 which contained four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples were downloaded from the ArrayExpress database. Pathway enrichment and Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) were performed, and protein-protein interaction (PPI) network, microRNA-target gene regulatory network and TFs (transcription factors) -target gene regulatory network for up- and down-regulated were analyzed using Cytoscape. In total, 552 DEGs were found, including 276 up-regulated and 276 down-regulated DEGs. Pathway enrichment analysis demonstrated that most DEGs were significantly enriched in chemical carcinogenesis, urea cycle, cell adhesion molecules and creatine biosynthesis. GO enrichment analysis showed that most DEGs were significantly enriched in translation, nucleosome, extracellular matrix organization and extracellular matrix. From protein-protein interaction network (PPI) analysis, modules, microRNA-target gene regulatory network and TFs-target gene regulatory network for up- and down-regulated, and the top hub genes such as E2F4, SRPK2, A2M, CDH1, MAP1LC3A, UCHL1, HLA-C (major histocompatibility complex, class I, C), VAT1, ECM1 and SNRPN (small nuclear ribonucleoprotein polypeptide N) were associated in pathogenesis of EOC. The high expression levels of the hub genes such as CEBPD (CCAAT enhancer binding protein delta) and MID2 in stages 3 and 4 were validated in the TCGA (The Cancer Genome Atlas) database. CEBPD andMID2 were associated with the worst overall survival rates in EOC. In conclusion, the current study diagnosed DEGs between normal and EOC samples, which could improve our understanding of the molecular mechanisms in the progression of EOC. These new key biomarkers might be used as therapeutic targets for EOC. MDPI 2019-04-09 /pmc/articles/PMC6628368/ /pubmed/30970615 http://dx.doi.org/10.3390/diagnostics9020039 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alur, Varun Chandra
Raju, Varshita
Vastrad, Basavaraj
Vastrad, Chanabasayya
Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics
title Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics
title_full Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics
title_fullStr Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics
title_full_unstemmed Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics
title_short Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics
title_sort mining featured biomarkers linked with epithelial ovarian cancerbased on bioinformatics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628368/
https://www.ncbi.nlm.nih.gov/pubmed/30970615
http://dx.doi.org/10.3390/diagnostics9020039
work_keys_str_mv AT alurvarunchandra miningfeaturedbiomarkerslinkedwithepithelialovariancancerbasedonbioinformatics
AT rajuvarshita miningfeaturedbiomarkerslinkedwithepithelialovariancancerbasedonbioinformatics
AT vastradbasavaraj miningfeaturedbiomarkerslinkedwithepithelialovariancancerbasedonbioinformatics
AT vastradchanabasayya miningfeaturedbiomarkerslinkedwithepithelialovariancancerbasedonbioinformatics