Screening biomarkers for premature ovarian insufficiency based on cellular senescence and endoplasmic reticulum stress with experimental validation

Abstract

BACKGROUND: Ovarian granulosa cell senescence and endoplasmic reticulum stress are closely related to the development and progression of premature ovarian insufficiency; however, the underlying regulatory mechanisms remain unelucidated. OBJECTIVE: To identify potential biomarkers associated with cellular senescence and endoplasmic reticulum stress in granulosa cells in premature ovarian insufficiency using bioinformatic analysis and machine learning algorithms, with subsequent validation in animal experiments. METHODS: The premature ovarian insufficiency dataset GSE201276 was downloaded from the GEO database. Differentially expressed genes were screened, and weighted gene co-expression network analysis was performed to identify module genes. Gene sets related to cellular senescence and endoplasmic reticulum stress were obtained from the GeneCards database, and intersected with differentially expressed genes and module genes. Consensus clustering analysis was then performed to identify subtype-specific differentially expressed genes, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and immune infiltration analysis. Two machine learning algorithms were applied to screen key genes associated with cellular senescence and endoplasmic reticulum stress in granulosa cells, and a diagnostic model was constructed and validated. Finally, a premature ovarian insufficiency mouse model was established in C57BL/6J mice, and the model was verified by estrous cycle monitoring, hematoxylin-eosin staining, and serum ELISA. The expression of key genes was validated by real-time quantitative PCR and western blot. RESULTS AND CONCLUSION: Consensus clustering identified 911 subtype-specific differentially expressed genes associated with cellular senescence and endoplasmic reticulum stress. Gene Ontology enrichment analysis showed that these genes were mainly involved in biological processes such as negative regulation of cell cycle, meiosis, and female gonad development. Kyoto Encyclopedia of Genes and Genomes analysis revealed enrichment in pathways such as oocyte meiosis, progesterone-mediated oocyte maturation, and transforming growth factor beta signaling. Immune infiltration analysis showed significantly higher infiltration levels of M1 macrophages and resting dendritic cells in the premature ovarian insufficiency group (P < 0.05). Machine learning algorithms identified four key genes, and the diagnostic model and calibration curves showed that aurora kinase A and actin binding protein exhibited good predictive performance. Animal experiments showed that compared with the control group, the model group exhibited disrupted estrous cycles, reduced numbers of primary, secondary, and antral follicles, and increased numbers of atretic follicles (P < 0.01). Serum follicle-stimulating hormone levels were elevated, while anti-Müllerian hormone levels were decreased, with significant differences (P < 0.01). Compared with the control group, the mRNA and protein expression levels of aurora kinase A and actin binding protein in ovarian tissues of the model group were significantly decreased (P < 0.05). These results indicate that aurora kinase A and actin binding protein may participate in the development of premature ovarian insufficiency by regulating granulosa cell senescence and endoplasmic reticulum stress, and their specific regulatory roles and molecular mechanisms require further experimental validation.

1. Introduction

Premature ovarian insufficiency is a clinical syndrome characterized by ovarian function decline in women before the age of 40, presenting with menstrual disorders (amenorrhea or oligomenorrhea), hypergonadotropinemia, and hypoestrogenism [1-2]. If not intervened in time, it can progress to premature ovarian failure [3]. The pathogenesis of premature ovarian insufficiency is associated with genetic abnormalities, autoimmune abnormalities, infectious factors, and iatrogenic factors; however, approximately 65% of patients have no clear etiology [4]. Therefore, it is urgent to explore the pathogenesis of premature ovarian insufficiency and screen for disease-specific biomarkers and therapeutic targets.

Abnormal senescence of ovarian granulosa cells is one of the key pathological changes in the pathogenesis of premature ovarian insufficiency. Abnormally senescent granulosa cells can drive the decline in oocyte quality and/or quantity by releasing senescence-associated secretory phenotype, regulating the ovarian inflammatory microenvironment, or through bidirectional signal communication [5-7], and can also accelerate oocyte senescence by releasing soluble death-associated Fas ligand [7-8]. Endoplasmic reticulum stress is a cellular stress state caused by the accumulation of unfolded or misfolded proteins in the endoplasmic reticulum [9]. When cells are stimulated by internal and external factors such as senescence and pro-inflammatory cytokines, protein processing and transport are hindered, which can induce endoplasmic reticulum stress [10]. Endoplasmic reticulum stress is closely related to the pathogenesis of various diseases. Studies have pointed out that phosphatidylethanolamine can promote macrophage senescence and secretion of senescence-associated secretory phenotype bioactive factors by activating the endoplasmic reticulum stress signaling pathway, aggravating liver injury [11]. Tau protein participates in the development of Alzheimer's disease by driving endoplasmic reticulum stress-induced neuronal death [12]. In ovarian tissues of patients with ovarian dysfunction, endoplasmic reticulum stress is elevated, suggesting a potential link between endoplasmic reticulum stress and ovarian aging.