Prediction of shared gene signatures and biological mechanisms between polycystic ovary syndrome and asthma: Based on weighted gene coexpression network analysis.

Objective: Several clinical studies have shown an association between polycystic ovary syndrome (PCOS) and asthma; however, the molecular link between these conditions remains unclear. In this study, we conducted a reanalysis and repurposing of existing databases in order to depict the common key genes, related signaling pathways, and similarity of the immune microenvironment between PCOS and asthma.

Methods: PCOS and asthma data sets were downloaded, and common signal pathways were identified by using gene set enrichment analysis.

Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia.

Preeclampsia is a disease that affects both the mother and child, with serious consequences. Screening the characteristic genes of preeclampsia and studying the placental immune microenvironment are expected to explore specific methods for the treatment of preeclampsia and gain an in-depth understanding of the pathological mechanism of preeclampsia. We screened for differential genes in preeclampsia by using limma package.

Construction of a ceRNA network in polycystic ovary syndrome (PCOS) driven by exosomal lncRNA.

Polycystic ovary syndrome (PCOS), a common and frustrating syndrome in women of reproductive age, is characterized by symptoms including hyperandrogenemia, ovulation dysfunction, and polycystic ovaries. The role of competitive endogenous RNA (ceRNA) networks is receiving increasing attention and has been reported in multiple complicated diseases, such as various carcinomas, endometriosis, and tubal factor infertility. However, the association of ceRNA networks with the pathogenesis of PCOS remains unclear.