Regulatory RNA Networks in Ovarian Follicular Cysts in Dairy Cows: Implications for Human Polycystic Ovary Syndrome

Background/Objectives: Ovarian follicular cysts (OFCs) in dairy cows represent a significant cause of infertility and share striking similarities with polycystic ovary syndrome (PCOS) in women. This study aimed to elucidate the molecular mechanisms underlying OFCs and their relevance to PCOS by profiling differentially expressed (DE) microRNAs (miRNAs) and constructing integrative RNA interaction networks. Methods: Expression analysis of 84 bovine miRNAs was conducted in antral follicular fluid from normal and cystic follicles using miScript PCR arrays. Bioinformatic tools including miRBase, miRNet, and STRING were employed to predict miRNA targets, construct protein-protein interaction networks, and perform gene ontology and KEGG pathway enrichment. Network analyses integrated miRNAs with coding (mRNAs) and non-coding RNAs (circRNAs, lncRNAs, snRNAs). Results: Seventeen miRNAs were significantly dysregulated in OFCs, including bta-miR-18a, bta-miR-30e-5p, and bta-miR-15b-5p, which were associated with follicular arrest, insulin resistance, and impaired steroidogenesis. Upregulated miRNAs such as bta-miR-132 and bta-miR-145 correlated with inflammation, oxidative stress, and intrafollicular androgen excess. Key regulatory lncRNAs such as Nuclear Enriched Abundant Transcript 1 (NEAT1), Potassium Voltage-Gated Channel Subfamily Q Member 1 Opposite Strand/Antisense Transcript 1 (KCNQ1OT1), Taurine-Upregulated 1 (TUG1), and X Inactive Specific Transcript (XIST), as well as circRNA/pseudogene hubs, were identified, targeting pathways involved in metabolism, inflammation, steroidogenesis, cell cycle, and apoptosis. Conclusions: The observed transcriptomic changes mirror core features of human PCOS, supporting the use of bovine OFCs as a comparative model. These findings provide novel insights into the regulatory RNA networks driving ovarian dysfunction and suggest potential biomarkers and therapeutic targets for reproductive disorders. This network-based approach enhances our understanding of the complex transcriptomic landscape associated with follicular pathologies in both cattle and women.