ArticleReproductive biology and endocrinology : RB&E2024
Identification of energy metabolism anomalies and serum biomarkers in the progression of premature ovarian failure via extracellular vesicles' proteomic and metabolomic profiles.
Article in Reproductive biology and endocrinology : RB&E, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exosomes in diabetic kidney disease: pathogenesis, biomarker discovery, and emerging therapeutics-a comprehensive systematic review.Renal failure · 2026Pooled it
- Progress in research on the effects of Platelet-Rich Plasma (PRP) ovarian injection on early ovarian reserve dysfunction and premature ovarian insufficiency.Journal of ovarian research · 2026Review
- Genetic Associations Between Premature Ovarian Insufficiency and Bone Health Traits: A Bidirectional Two-Sample Mendelian Randomization Study.International journal of women's health · 2026Article
- Causal associations between plasma proteins and premature ovarian failure through multi-omics Mendelian randomization analyses.Gynecology and pelvic medicine · 2026Article
- A novel serum protein biomarker for the late-stage diagnosis of nasopharyngeal carcinoma.BMC cancer · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundPremature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers.
methodsThis study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning.
resultsThe proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF.
conclusionsOmics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis.
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