Sperm epigenetics and male infertility: unraveling the molecular puzzle.

BACKGROUND: The prevalence of infertility among couples is estimated to range from 8 to 12%. A paradigm shift has occurred in understanding of infertility, challenging the notion that it predominantly affects women. It is now acknowledged that a significant proportion, if not the majority, of infertility cases can be attributed to male-related factors. Various elements contribute to male reproductive impairments, including aberrant sperm production caused by pituitary malfunction, testicular malignancies, aplastic germ cells, varicocele, and environmental factors. MAIN BODY: The epigenetic profile of mammalian sperm is distinctive and specialized. Various epigenetic factors regulate genes across different levels in sperm, thereby affecting its function. Changes in sperm epigenetics, potentially influenced by factors such as environmental exposures, could contribute to the development of male infertility. CONCLUSION: In conclusion, this review investigates the latest studies pertaining to the mechanisms of epigenetic changes that occur in sperm cells and their association with male reproductive issues.

Evaluation of the expression profile of mRNAs and lncRNAs in cumulus cells associated with polycystic ovary syndrome and pregnancy.

Objectives: Polycystic ovary syndrome (PCOS), the primary cause of anovulatory infertility in women, may change the gene expression profile of cumulus cells. In human ART (assisted reproductive technology), gene expression profiling in cumulus cells, a non-invasive method, may be used to identify the most competent oocytes. We aim to identify key genes according to the network-based data and assess the suitability of these genes as markers to predict oocyte competence and PCOS diagnosis. Materials and Methods: The GSE34526 microarray dataset was obtained from the Gene Expression Omnibus (GEO) database. The function and pathway enrichment analysis for DEGs were analyzed. A protein-protein interaction (PPI) network analysis and candidate gene screening were conducted. A two-layer network consisting of mRNA and lncRNA was constructed. Expression levels of hub genes were verified using quantitative RT-PCR (qRT-PCR). Results: A total of 2721 DEGs were retained. The PPI network of selected genes associated with the biological process of "cell communication" was analyzed, and the first 10 key genes were determined by degree. Additionally, 2 hub genes and 2 hub lncRNAs, including STAT3, RHOA, GAS5, and LINC01116, were selected from the lncRNA-mRNA network. Finally, expression levels of STAT3, RHOA, GAS5, and LINC01116 were significantly increased in the cumulus cells of PCOS patients compared to the control group (P<0.05). However, there was no significant difference in expression between the pregnant and non-pregnant groups. Conclusion: STAT3, RHOA, GAS5, and LINC01116 may serve as possible diagnostic markers for PCOS. However, further studies on a larger population are needed to validate this finding.

Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis.

PURPOSE: Endometriosis (EMs) is a major gynecological condition in women. Due to the absence of definitive symptoms, its early detection is very challenging; thus, it is crucial to find biomarkers to ease its diagnosis and therapy. Here, we aimed to identify potential diagnostic and therapeutic targets for EMs by constructing a regulatory network and using machine learning approaches. METHODS: Three Gene Expression Omnibus (GEO) datasets were merged, and differentially expressed genes (DEGS) were identified after preprocessing steps. Using the DEGs, a transcription factor (TF)-mRNA-miRNA regulatory network was constructed, and hub genes were detected based on four different algorithms in CytoHubba. The hub genes were used to build a GaussianNB diagnostic model and also in docking analysis that were performed using Discovery Studio and AutoDock Vina software. RESULTS: A total of 119 DEGs were identified between EMs and non-EMs samples. A regulatory network consisting of 52 mRNAs, 249 miRNAs, and 37 TFs was then constructed. The diagnostic model was introduced using the hub genes selected from the network (GATA6, HMOX1, HS3ST1, NFASC, and PTGIS) that its area under the curve (AUC) was 0.98 and 0.92 in the training and validation cohorts, respectively. Based on docking analysis, two chemical compounds, rofecoxib and retinoic acid, had potential therapeutic effects on EMs. CONCLUSION: In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.