Systems biology and in silico-based analysis of PCOS revealed the risk of metabolic disorders

Md Arju Hossain; Sheikh Abdullah Al Ashik; Moshiur Rahman Mahin; Md Al Amin; Md Habibur Rahman; Md Arif Khan; Abdullah Al Emran

doi:10.1016/j.heliyon.2022.e12480

Systems biology and in silico-based analysis of PCOS revealed the risk of metabolic disorders

Heliyon. 2022 Dec 22;8(12):e12480. doi: 10.1016/j.heliyon.2022.e12480. eCollection 2022 Dec.

Authors

Md Arju Hossain¹, Sheikh Abdullah Al Ashik¹, Moshiur Rahman Mahin¹, Md Al Amin¹, Md Habibur Rahman², Md Arif Khan^{1

3}, Abdullah Al Emran¹

Affiliations

¹ Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1092, Bangladesh.
² Department of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh.
³ Department of Biotechnology and Genetic Engineering, University of Development Alternative, 4/4B, Block A, Lalmatia, Dhaka, 1209, Bangladesh.

Abstract

Background: Polycystic ovarian syndrome (PCOS) is a common condition of hyperandrogenism, chronic ovulation, and polycystic ovaries in females during the reproduction and maturation of the ovum. Although PCOS has been associated with metabolic disorders, including type 2 diabetes (T2D), obesity (OBE), and cardiovascular disease (CVD), Causal connection and molecular features are still unknown.

Purpose: Therefore, we investigated the shared common differentially expressed genes (DEGs), pathways, and networks of associated proteins in PCOS and metabolic diseases with therapeutic intervention.

Methods: We have used a bioinformatics pipeline to analyze transcriptome data for the polycystic ovarian syndrome (PCOS), type 2 diabetes (T2D), obesity (OBE), and cardiovascular diseases (CVD) in female patients. Then we employed gene-disease association network, gene ontology (GO) and signaling pathway analysis, selection of hub genes from protein-protein interaction (PPI) network, molecular docking, and gold benchmarking approach to screen potential hub proteins.

Result: We discovered 2225 DEGs in PCOS patients relative to healthy controls and 34, 91, and 205 significant DEGs with T2D, Obesity, and CVD, respectively. Gene Ontology analysis revealed several significant shared and metabolic pathways from signaling pathway analysis. Furthermore, we identified ten potential hub proteins from PPI analysis that may serve as a therapeutic intervention in the future. Finally, we targeted one significant hub protein, IGF2R (PDB ID: 2V5O), out of ten hub proteins based on the Maximal clique centrality (MCC) algorithm and literature review for molecular docking study. Enzastaurin (-12.5), Kaempferol (-9.1), Quercetin (-9.0), and Coumestrol (-8.9) kcal/mol showed higher binding affinity in the molecular docking approach than 19 drug compounds. We have also found that the selected four compounds displayed favorable ADMET properties compared to the native ligand.

Conclusion: Our in-silico research findings identified a shared molecular etiology between PCOS and metabolic diseases that may suggest new therapeutic targets and warrants future experimental validation of the key targets.

Keywords: ADMET; Cardiovascular disease; Metabolic comorbidities; Obesity; Phytochemicals; Polycystic ovary syndrome; Type 2 diabetes.