Citation
Abstract
Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder affecting women of reproductive age globally. The importance of aberrant immune response dysregulation in PCOS has emerged as a prominent subject of discussion. Non-invasive approaches utilizing important blood biomarkers not only provide valuable information about a patient's biological profile but also present a promising opportunity for developing new diagnostic and prognostic tools. In the present study, PCOS data sets, including GSE34526 from granulosa cell (GC) samples and GSE54248 from blood samples, were investigated, and shared differentially expressed mRNA (DE-mRNAs) between the two data sets were identified. In the next step, DE-mRNA enrichment analysis of the shared mRNAs has been performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and the FunRich tool. Finally, protein-protein interaction (PPI) and miRNA-mRNA networks were constructed to identify the most significant DE-mRNAs and their corresponding targets. In total, hub miRNA-mRNA was retrieved from 20 nodes and 24 edges, including 9 mRNAs: CXCL8, IL1B, TLR4, PTPRC, AIF1, CXCR2, TLR6, CD86, and ILIRN, and 11 predicted miRNAs included; has-miR-155-5p, has-miR-126- 3p, has-miR-146a-5p, has-miR-21-5p, has-miR-19b-3p, has-miR-19a-3p, has-miR-106b-5p, has-miR212-3p, has-miR-93-5p, has-miR-20a-5p, and has-miR-17-5p. Top-up-regulated genes were enriched in the immune system, immune response, and top-down-regulated genes were enriched in carbohydrate metabolism and sphingolipid metabolism pathways. The findings of the current study might help researchers shine a spotlight on the role of immune biomarkers in the pathogenesis and development of PCOS.
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Medicine and Health Science Malaysian Research Institute on Ageing |
Publisher: | Wolters Kluwer - Medknow Publications and Media |
Keywords: | Polycystic ovary syndrome; Inflammatory immune response; Hub genes; Hub miRNAs; Bioinformatics analysis |
Depositing User: | Ms. Zaimah Saiful Yazan |
Date Deposited: | 14 Jun 2024 08:03 |
Last Modified: | 14 Jun 2024 08:03 |
URI: | http://psasir.upm.edu.my/id/eprint/108242 |
Statistic Details: | View Download Statistic |
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