Identification of the key pathways and genes related to polycystic ovary syndrome using bioinformatics analysis

Rok, strany: 2019, 205 - 214

Polycystic ovary syndrome (PCOS) is the most common hormonal and metabolic disorder among women of reproductive age, but the mechanisms underlying this unique pathogenesis remain unknown. This study was therefore designed to identify candidate genes involved in the pathogenesis of PCOS, using bioinformatics analysis. The gene expression profiles of GSE34526 from 7 PCOS patients and 3 controls were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using GCBI online tool. Expression levels of candidate genes were verified using quantitative RT-PCR (qRT-PCR) and Western blot. 426 DEGs were identified by GCBI, including 418 up-regulated and 8 down-regulated genes. Function and pathway enrichment analyses showed that these DEGs were significantly enriched in inflammation and immune-related pathways. Additionally, protein–protein interaction (PPI) network and module analyses showed that two modules involved the Toll-like receptor signaling pathway were ranked among the most upregulated modules, and the candidate genes involved in this signaling pathway consisted of TLR1, TLR2, TLR8, and CD14. Finally, expression levels of TLR2, TLR8 and CD14 were significantly increased in samples from PCOS patients. Collectively, the results suggested that the Toll-like receptor signaling pathway might play an important role in the pathogenesis of PCOS.

ISO 690:

Bi, X., Zhai, Z., Wang, S. 2019. Identification of the key pathways and genes related to polycystic ovary syndrome using bioinformatics analysis. In General Physiology and Biophysics, vol. 38, no.3, pp. 205-214. 0231-5882.

APA:

Bi, X., Zhai, Z., Wang, S. (2019). Identification of the key pathways and genes related to polycystic ovary syndrome using bioinformatics analysis. General Physiology and Biophysics, 38(3), 205-214. 0231-5882.