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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/9/2/39 |
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| author | Varun Chandra Alur Varshita Raju Basavaraj Vastrad Chanabasayya Vastrad |
| author_facet | Varun Chandra Alur Varshita Raju Basavaraj Vastrad Chanabasayya Vastrad |
| author_sort | Varun Chandra Alur |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T22:31:09Z |
| format | Article |
| id | doaj.art-6eea2ccb0eb54aafa8b0a25ce75c9901 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-04-11T22:31:09Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-6eea2ccb0eb54aafa8b0a25ce75c99012022-12-22T03:59:24ZengMDPI AGDiagnostics2075-44182019-04-01923910.3390/diagnostics9020039diagnostics9020039Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on BioinformaticsVarun Chandra Alur0Varshita Raju1Basavaraj Vastrad2Chanabasayya Vastrad3Department of Endocrinology, J.J. M Medical College, Davanagere, Karnataka 577004, IndiaDepartment of Obstetrics and Gynecology, J.J. M Medical College, Davanagere, Karnataka 577004, IndiaDepartment of Pharmaceutics, SET’S College of Pharmacy, Dharwad, Karnataka 580002, IndiaBiostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad, Karanataka 580001, IndiaEpithelial 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.https://www.mdpi.com/2075-4418/9/2/39epithelial ovarian cancerbioinformatics analysisdifferentially-expressed genesPPI networksurvival analysis |
| spellingShingle | Varun Chandra Alur Varshita Raju Basavaraj Vastrad Chanabasayya Vastrad Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics Diagnostics epithelial ovarian cancer bioinformatics analysis differentially-expressed genes PPI network survival analysis |
| 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 | epithelial ovarian cancer bioinformatics analysis differentially-expressed genes PPI network survival analysis |
| url | https://www.mdpi.com/2075-4418/9/2/39 |
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