Exploring critical hub genes and potential drug candidates for PCOS: A systems biology approach

Ali Ahmadizad Firouzjaei; Ali Mahmoudi; Natalia Elizova; Prashant Kesharwani; Amirhossein Sahebkar

doi:10.1016/j.repbio.2025.101078

Exploring critical hub genes and potential drug candidates for PCOS: A systems biology approach

Reprod Biol. 2025 Sep 27;25(4):101078. doi: 10.1016/j.repbio.2025.101078. Online ahead of print.

Authors

Ali Ahmadizad Firouzjaei¹, Ali Mahmoudi², Natalia Elizova³, Prashant Kesharwani⁴, Amirhossein Sahebkar⁵

Affiliations

¹ Bioinformatics Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
² Abadan University of Medical Sciences, Abadan, Iran.
³ Petrovsky National Research Centre of Surgery, Moscow, Russia; Institute of General Pathology and Pathophysiology, Moscow, Russia.
⁴ Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya (A Central University), Sagar, Madhya Pradesh, 470003, India; University Institute of Pharma Sciences, Chandigarh University, Mohali, Punjab, India. Electronic address: prashantdops@gmail.com.
⁵ Center for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India; Applied Biomedical Research Center, Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address: amir_saheb2000@yahoo.com.

PMID: 41016372
DOI: 10.1016/j.repbio.2025.101078

Abstract

Polycystic ovarian syndrome (PCOS) is widely recognized as the most common endocrine disorder in women of reproductive age. We aimed to identify PCOS-related genes by searching the Gene Expression Omnibus (GEO) database, and subsequently used the DESeq2 tool in the R program to identify differentially expressed genes. We constructed a protein-protein interaction (PPI) network for the upregulated genes in PCOS and applied the MCODE algorithm to identify significant clustering groups and hub candidates. Additionally, we performed an enrichment analysis using the ClueGO plugin to explore the biological processes and pathways associated with the upregulated genes. We successfully obtained the dataset GSE155489 from the GEO database and identified 198 elevated genes. The biological functions associated with the upregulated genes were predominantly related to steroid binding, chemokine activity, and pyridoxal phosphate binding. Results from the molecular docking study indicated that three drugs, namely Ponatinib, Dihydroergotamine, and Paliperidone, exhibited the lowest binding affinity energy and displayed the highest interaction with the most critical hub genes identified through the PPI network analysis (WNT5A, SERPINE1, and CXCR4). In summary, our findings highlight several crucial genes involved in PCOS and suggest that Ponatinib, Dihydroergotamine, and Paliperidone may have a significant impact on these genes. However, it is important to note that further research and well-designed clinical trials are necessary, as there is limited evidence from small-population clinical trials regarding the repurposing of drugs for PCOS. Advancements in PCOS knowledge will aid in the development of innovative medications for the disorder.

Keywords: Drug repository; Gene ontology; KEGG; PPI network; Polycystic ovarian syndrome.