A bioinformatics approach to identify potential biomarkers of high-grade ovarian cancer.
Objective: High-grade ovarian cancer (HGOC) remains a significant therapeutic challenge due to its aggressive nature and poor prognosis. The aim was to elucidate the molecular drivers of HGOC through an integrated bioinformatics analysis. Material and Methods: The microarray datasets (GSE6008 and GS...
| Publicado en: | Journal of the Turkish-German Gynecological Association Vol. 27; no. 1; pp. 19 - 29 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Mar2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192017925&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192017925 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13090399 4U8C jtl: Journal of the Turkish-German Gynecological Association issn: 13090399 maglogo: N pubinfo: dt: Mar2026 vid: 27 iid: 1 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 192017925 192017925 192017925 10.4274/jtgga.galenos.2025.2025-1-1 192017925 ppf: 19 ppct: 10 formats: tig: atl: A bioinformatics approach to identify potential biomarkers of high-grade ovarian cancer. aug: au: Kahraman, Özlem Timirci Gültekin, Güldal İnal Billur, Deryanaz Ülker, Esin Bayralı İşbilen, Murat Durmuş, Saliha Çakır, Tunahan Yaylım, İlhan İsbir, Turgay affil: Department of Molecular Medicine, Aziz Sancar Institute of Experimental Medicine, İstanbul University, İstanbul, Türkiye sug: subj: Bioinformatics Tumor Markers, Biological Neoplasm Grading Ovarian Neoplasms Gene Expression Profiling Human Gene Expression Data Analysis Software Oxidative Stress Phosphorylation Cell Cycle Molecular Biology Oncogenes Microarray Analysis Proteomics Retrospective Design Funding Source ab: Objective: High-grade ovarian cancer (HGOC) remains a significant therapeutic challenge due to its aggressive nature and poor prognosis. The aim was to elucidate the molecular drivers of HGOC through an integrated bioinformatics analysis. Material and Methods: The microarray datasets (GSE6008 and GSE14764) served as the training set, while an independent microarray dataset (GSE23603) was used as the validation set. These datasets included low- and high-grade ovarian tumor samples and were downloaded from the ArrayExpress database. Selection criteria included clearly classified low-grade ovarian cancer and HGOC samples, as well as platform and sample processing methods compatibility. After normalization, differentially expressed genes (DEGs) were obtained using R software. Functional enrichment analysis [including gene ontology (GO) and pathway analysis] was performed using the DAVID database. A protein-protein interaction (PPI) network was constructed by STRING to identify hub genes associated with HGOC. Results: A total of 106 common DEGs were identified across all three datasets, including 66 up-regulated and 40 down-regulated genes. Given the study's focus on potential oncogenic drivers, subsequent analyses prioritized the 66 up-regulated genes. The DEGs were classified into three groups by GO terms (21 biological process, 10 molecular function and 12 cellular component). Kyoto Encyclopedia of Genes and Genomes pathway analysis showed enrichment in metabolic pathways, oxidative phosphorylation, drug metabolism, and cell cycle regulation. The top nine up-regulated hub genes in the PPI network were GMPS, RFC4, YWHAZ, CHEK1, CYC1, MRPL13, MRPL15, SDHA, and CLPB. Conclusion: The identification of these hub genes and pathways may represent an important step forward in our understanding of HGOC. While down-regulated genes may also hold biological significance, their analysis was beyond the scope of this study and warrants future investigation. Further experimental validation is needed to confirm the roles of the identified genes in disease pathogenesis and their potential as biomarkers and therapeutic targets. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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