Text mining and data analysis identifies potential drugs and pathways for polycystic ovary syndrome treatment

Abstract

Objective: Polycystic ovarian syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. This study aimed to use text mining and microarray data analysis to identify drugs that target genes and potential pathways associated with PCOS. Methods: We extracted a common set of genes associated with PCOS using text mining and the microarray dataset GSE48301. Next, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of these genes, as well as protein-protein interaction (PPI) network analysis. Additionally, we used MCODE and cytoHubba to cluster significant common genes in the PPI network and performed gene-drug interaction analyses to identify potential drugs for further investigation. Finally, we annotated pathways associated with the genes identified. Results: Text mining and microarray analysis yielded 696 text mining genes (TMGs) and 2,804 differentially expressed genes (DEGs). Among these, a set of 77 genes was found in both TMGs and DEGs. Interestingly, 67 of these genes participated in constructing the PPI network. Seven common hub genes were selected using the MCODE and CytoHubba methods. Finally, five out of seven genes were targeted by 15 existing drugs. Conclusion: Four genes (FASLG, IL13, IL17A, and IL2RA), which are mainly related to the cytokine-cytokine receptor interaction pathway, could be prioritized as targets for PCOS.