Abstract
OBJECTIVES: Ovarian aging is considered the "pacemaker" and "biological clock" of systemic female aging, with early manifestations that are often insidious. In this study, we analyzed the shared and distinct molecular signatures between physiological and pathological ovarian aging models using proteomic approaches, with the aim of identifying early predictive markers and therapeutic targets for ovarian aging, evaluating model fidelity, and elucidating underlying molecular mechanisms. METHODS: Ovarian tissue samples were collected from female C57/BL6 mice representing chemotherapeutic ovarian aging (8-week-old, Cyclophosphamide-Busulfan model) and natural ovarian aging (18-month-old). Initial validation of the models was conducted through histological assessment and serum hormone measurements. Quantitative proteomics was employed to profile protein expression. Differentially expressed proteins were subjected to GO, KEGG, and WikiPathways functional enrichment and clustering analyses, followed by validation of selected candidate genes using quantitative real-time polymerase chain reaction (RT-qPCR). RESULTS: The cyclophosphamide-busulfan induced pathological ovarian aging model exhibited partial consistency with natural ovarian aging in terms of histological morphology, hormone levels, and proteomic profiles. protein-protein interaction (PPI) network construction and pathway enrichment analyses provided further evidence supporting the strong association between alterations in the subcortical maternal complex (SCMC) and ovarian dysfunction. Notably, Cyp17a1 and Lhcgr were identified as potential early biomarkers for ovarian aging. Additionally, 7 key molecular targets (pbk, sdhd, Gsta3, Gstm6, Nlrp5, Nlrp4f, and Nlrp14) closely related to chronic inflammation and oxidative stress were identified, some of which may reveal the close relationship between aging and tumors. Comparative analysis further revealed that pathological ovarian aging is predominantly characterized by DNA damage and cell cycle dysregulation, whereas physiological aging predominantly involved immune dysfunction, abnormal lipid metabolism, and chronic low-grade inflammation, underscoring the molecular heterogeneity between aging subtypes. CONCLUSIONS: This study confirmed the validity of the cyclophosphamide-busulfan induced mouse model of ovarian senescence, highlighted both the shared and distinct molecular mechanisms of physiological and pathological ovarian aging, and identified promising early biomarkers and therapeutic intervention targets.