همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Integrated bioinformatic analysis for the screening of hub genes & therapeutic drugs in high-grade serous ovarian cancer

نویسندگان :

Maryam Khalili¹ Behnaz Saffar²

1- دانشگاه شهرکرد 2- دانشگاه شهرکرد

کلمات کلیدی :

HGSOC،Differential Expression miRNAs،Survival analysis،Functional enrichment analysis،Protein-protein interaction

چکیده :

High-grade serous ovarian cancer (HGSOC) accounts for nearly 60% of total cases of epithelial ovarian cancer, the highest frequent malignant gynecologic tumor. It is the most aggressive subtype which shows poor prognosis and low patient survival (Topno and Singh, 2021). This study aims to identify hub genes and therapeutic drugs involved in HGSOC. The gene expression profile (GSE235525) was obtained from the Gene Expression Omnibus (GEO), which included miRNA expression data from 70 serum samples, comprising 36 HGSOC cases and 34 normal ovarian samples. Differentially expressed (DE) miRNAs between ovarian cancer tissues and normal tissues were identified using GEO2R analysis, with a P-value < 0.05 and -1 < |log fold change (FC)| < 1. A total of 76 hsa-miRNAs were identified and subsequently analyzed in the DIANA-miRPath database to validate miRNA interactions. Four hsa-miRNAs were highlighted for their extensive interactions: has-miR-125b-5p, has-miR-145-5p, has-miR-21-5p, and has-miR-155-5p. The MultiMiR package in R software was employed to determine gene targets, while the Interactive Venn Diagram was utilized to assess gene sharing among these miRNAs (Interactive Venn, 2024). Functional enrichment analysis of these genes was conducted through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments using the Enrichr online tool (Ma'ayan Laboratory, 2024). Then, the hub genes were identified by the cytoHubba plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis via STRING and survival analysis (STRING Database, 2024; Kaplan-Meier Plotter, 2024). Finally, the GEPIA (Gene Expression Profiling Analysis) and DGIdb (Drug-Gene Interaction database) databases were utilized to verify the expression levels of hub genes and to select the candidate drugs for HGSOC, respectively (Gene Expression Profiling Interactive Analysis, 2024; The Drug–Gene Interaction Database, 2024). A total of 49 differentially expressed genes (DEGs) were identified. The GO analysis indicated that the molecular functions of these DEGs predominantly pertained to the negative regulation of cell population proliferation. As for the KEGG pathways, the DEGs were primarily linked to human cytomegalovirus infection, pancreatic cancer, and the role of proteoglycans in cancer. Furthermore, ten hub genes (CTNNB1, STAT3, CDKN1A, EGFR, CD44, CDK6, THBS1, SP1, NF2, and MUC1) were identified, and survival analysis revealed that high expression levels of CDK6, EGFR, STAT3, and THBS1 in patients with HGSOC were statistically associated with poorer survival outcomes. Lastly, DGIdb database was used to identify 126 small molecules as the potentially targeted drugs for HGSOC treatment. In summary, the Hub genes and candidate drugs may improve individualized diagnosis and therapy for HGSOC in future.

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