Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome.
Balqis RamlyNor Afiqah-AlengZeti Azura Mohamed HusseinPublished in: International journal of molecular sciences (2019)
Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein-protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher's exact test and comorbidity data were used to identify PCOS-disease subnetworks. Pathway enrichment analysis was performed on the PCOS-disease subnetworks to identify significant pathways that are highly involved in the PCOS-disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.
Keyphrases
- polycystic ovary syndrome
- protein protein
- insulin resistance
- network analysis
- small molecule
- bipolar disorder
- blood pressure
- healthcare
- adipose tissue
- metabolic syndrome
- skeletal muscle
- machine learning
- molecular dynamics
- coronary artery disease
- health information
- social media
- artificial intelligence
- single molecule
- density functional theory