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pmid:30970615

Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics.

Varun Chandra Alur · Varshita Raju · Basavaraj Vastrad · Chanabasayya Vastrad

Diagnostics (Basel, Switzerland) · 2019 doi:10.3390/diagnostics9020039

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Mining Featured Biomarkers Linked with Epithelial Ovarian CancerBased on Bioinformatics.
doi
10.3390/diagnostics9020039
pmid
30970615
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Varun Chandra Alur
[1]
Varshita Raju
[2]
Basavaraj Vastrad
[3]
Chanabasayya Vastrad
journal
Diagnostics (Basel, Switzerland)
year
2019
abstract
: 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.
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Genes with fold changes> 1 for up-regulated genes, fold changes >−1 for down-regulated genes and FDR (false discovery rate) < 0.0000015 were considered as highly significant.
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The microarray expression profile dataset E-MTAB-3706, which is based on the Illumina HumanHT-12_V4_0_R2_15002873_B Expression BeadChip (Illumina Way, San Diego, CA, USA), was downloaded from the European Bioinformatics Institute ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3706/). The dataset contained 12 samples, including four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples.
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The microarray expression profile dataset E-MTAB-3706, which is based on the Illumina HumanHT-12_V4_0_R2_15002873_B Expression BeadChip (Illumina Way, San Diego, CA, USA), was downloaded from the European Bioinformatics Institute ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3706/). The dataset contained 12 samples, including four high-grade ovarian epithelial cancer samples, four normal fallopian tube samples and four normal ovarian epithelium samples.
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