A bioinformatics approach to identify potential biomarkers of high-grade ovarian cancer

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.

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.

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.

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.