Polycystic Ovary Syndrome and Oxidative Stress-From Bench to Bedside

Oxidative stress (OS) is a condition that occurs as a result of an imbalance between the production of reactive oxygen species (ROS) and the body's ability to detoxify and neutralize them. It can play a role in a variety of reproductive system conditions, including polycystic ovary syndrome (PCOS), endometriosis, preeclampsia, and infertility. In this review, we briefly discuss the links between oxidative stress and PCOS. Mitochondrial mutations may lead to impaired oxidative phosphorylation (OXPHOS), decreased adenosine triphosphate (ATP) production, and an increased production of ROS. These functional consequences may contribute to the metabolic and hormonal dysregulation observed in PCOS. Studies have shown that OS negatively affects ovarian follicles and disrupts normal follicular development and maturation. Excessive ROS may damage oocytes and granulosa cells within the follicles, impairing their quality and compromising fertility. Impaired OXPHOS and mitochondrial dysfunction may contribute to insulin resistance (IR) by disrupting insulin signaling pathways and impairing glucose metabolism. Due to dysfunctional OXPHOS, reduced ATP production, may hinder insulin-stimulated glucose uptake, leading to IR. Hyperandrogenism promotes inflammation and IR, both of which can increase the production of ROS and lead to OS. A detrimental feedback loop ensues as IR escalates, causing elevated insulin levels that exacerbate OS. Exploring the relations between OS and PCOS is crucial to fully understand the role of OS in the pathophysiology of PCOS and to develop effective treatment strategies to improve the quality of life of women affected by this condition. The role of antioxidants as potential therapies is also discussed.

Analysis of characteristic genes and ceRNA regulation mechanism of endometriosis based on full transcriptional sequencing

Background: Endometriosis is a common gynecological disorder that usually causes infertility, pelvic pain, and ovarian masses. This study aimed to mine the characteristic genes of endometriosis, and explore the regulatory mechanism and potential therapeutic drugs based on whole transcriptome sequencing data and resources from public databases, providing a theoretical basis for the diagnosis and treatment of endometriosis.

Methods: The transcriptome data of the five eutopic (EU) and ectopic (EC) endometrium samples were obtained from Beijing Obstetrics and Gynecology Hospital, Beijing, China, and dinified as the own data set. The expression and clinical data of EC and EU samples in GSE25628 and GSE7305 datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds). Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify the endometriosis-related differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted by the “clusterProfiler” R package. Then, characteristic genes for endometriosis were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm. The expression of characteristic genes was verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western-blot. The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory ability of characteristic genes. We assessed the abundance of infiltrating immune cells in each sample using MCP-counter and ImmuCellAI algorithms. The competitive endogenous RNA (ceRNA) regulatory network of characteristic genes was created by Cytoscape and potential targeting drugs were obtained in the CTD database.

Results: 44 endometriosis-related differentially expressed genes were obtained from GSE25628 and the own dataset. Subsequently, LASSO and SVM-RFE algorithms identified four characteristic genes, namely ACLY, PTGFR, ADH1B, and MYOM1. The results of RT-PCR and western-blot were consistent with those of sequencing. The result of ROC curves indicated that the characteristic genes had powerful abilities in distinguishing EC samples from EU samples. Infiltrating immune cells analysis suggested that there was a certain difference in immune microenvironment between EC and EU samples. The characteristic genes were significantly correlated with specific differential immune cells between EC and EU samples. Then, a ceRNA regulatory network of characteristic genes was constructed and showed a total of 7, 11, 11, and 1 miRNA associated with ACLY, ADH1B, PTGFR, and MYOM1, respectively. Finally, we constructed a gene-compound network and mined 30 drugs targeting ACLY, 33 drugs targeting ADH1B, 13 drugs targeting MYOM1, and 12 drugs targeting PTGFR.

Conclusion: Comprehensive bioinformatic analysis was used to identify characteristic genes, and explore ceRNA regulatory network and potential therapeutic agents for endometriosis. Altogether, these findings provide new insights into the diagnosis and treatment of endometriosis.