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Bioinformatics, Machine Learning and Functional Validation Reveal Mitophagy-Related FABP5 and HMOX1 as Diagnostic and Therapeutic Targets in Polycystic Ovary Syndrome
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DOI：10.1007/s43032-026-02156-x.png)
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Abstract
En 中文
Polycystic Ovary Syndrome (PCOS) is a heterogeneous endocrine-metabolic disorder characterized by ovulatory dysfunction, hyperandrogenism, and insulin resistance, in which mitochondrial dysfunction has been increasingly implicated. Mitochondria regulate energy metabolism and oxidative stress, with mitophagy maintaining cellular balance. Dysregulated mitophagy relates to PCOS metabolic issues like obesity and inflammation. This study analyzed gene expression datasets to find autophagy-related genes in PCOS, identifying AMFR, FABP5, and HMOX1 as key genes. We built a diagnostic model and confirmed their elevated expression in a hyperandrogenism-induced PCOS cell model, revealing potential small-molecule drugs targeting these genes. Our integrative bioinformatics analysis and systematic molecular experiments suggest that FABP5 and HMOX1 are potential PCOS diagnostic targets, with high-affinity compounds as therapies,highlighting the pathological relevance of mitophagy-related genes in PCOS.
Keywords:
PCOS
Dysregulated mitophagy
Integrative bioinformatics analysis
Machine learning
Molecular docking
Journal
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2.5
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409
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7.4K
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