Construction of a competing endogenous RNA network to identify drug targets against polycystic ovary syndrome

Whispers of the polycystic ovary syndrome theater: Directing role of long noncoding RNAs

Polycystic Ovary Syndrome (PCOS) is a multifaceted endocrine disorder that implicates a spectrum of clinical manifestations, including hormonal imbalance, metabolic dysfunction, and even compromised ovarian granulosa cell (GC) activity. The underlying molecular mechanisms of PCOS remain elusive, presenting a significant barrier to effective diagnosis and treatment. This review delves into the emerging role of long non-coding RNAs (lncRNAs) in the pathophysiology of PCOS, articulating their intricate interactions with mRNAs, microRNAs, and other epigenetic regulators that collectively influence the hormonal and metabolic milieu of PCOS. We examine the dynamic regulatory networks orchestrated by lncRNAs that impact GC function, steroidogenesis, insulin resistance, and inflammatory pathways. By integrating findings from recent studies, we illuminate the potential of lncRNAs as biomarkers for PCOS and highlight their contribution to the disorder, offering a detailed perspective on the lncRNA-mediated modulation of gene expression and pathogenic pathways. Understanding targeted lncRNA interactions with PCOS proposes novel avenues for therapeutic intervention to ameliorate the reproductive and metabolic disturbances characteristic of the syndrome.

Identification and validation of senescence-related genes in polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is an exceedingly intractable issue affecting female endocrine and reproductive health. However, the etiology and intricate pathological mechanisms of PCOS remain unclear. Nowadays, aging was found to share multiple common pathological mechanisms with PCOS, which causes probing into the pathogenesis of PCOS from senescence. However, no bioinformatics analyses have specifically focused on connection between PCOS and ovarian aging.

METHODS

Differentially expressed aging-related genes in PCOS were identified and then analyzed using function enrichment method. Hub genes were determined based on multiple algorithms, and expression validation of hub genes was performed in both datasets and experiments (human granulosa-like tumor cell line, KGN; human Granulosa Cell, hGCs). Finally, a transcription factor-miRNA-gene network of hub genes was constructed.

RESULTS

Here, we identified 73 aging-related differential expression genes (ARDEGs) by intersecting DEGs in PCOS and senescence-related gene set. Furthermore, we performed biological functions and potential pathways of ARDEGs and potential hub genes were also screened by multiple algorithms. From the perspective of immune dysfunction, we analyzed the correlation between PCOS and immune cells. Finally, TF-miRNA-gene networks were constructed. Finally, TF-miRNA-gene networks were constructed.

CONCLUSIONS

Our work aimed to elucidate the relation between PCOS and cellular senescence based on bioinformatics strategy, deepening the understanding of mechanisms and to seek for novel therapy strategies for improving reproductive lifespan and female health. Exploring the potential molecular mechanism of cell aging in PCOS is expected to bring a new breakthrough for PCOS diagnosis and therapy strategies. And this, might deepen our understanding about intricate mechanisms of ovarian aging.