ArticleFrontiers in medicine2025
Identification of fatty acid metabolism hub genes in endometriosis using integrative bioinformatics analysis.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Metabolic remodeling of endometriosis microenvironment: Energy stress and immune evasion.iScience · 2026Review
- Glycolytic reprogramming in endometriosis: molecular mechanisms, immune modulation, and non-hormonal therapeutic opportunities.BMC women's health · 2026Review
- Immune-epigenetic-metabolic crosstalk: attempting to unravel the multidimensional mechanisms of immune evasion in endometriosis.Frontiers in immunology · 2026Review
- Antioxidant dietary patterns mitigate pain and raise psychological wellbeing in ovarian endometrioma: surgical status shapes associations.Frontiers in nutrition · 2026Article
- Warburg-like Metabolic Reprogramming in Endometriosis: From Molecular Mechanisms to Therapeutic Approaches.Pharmaceuticals (Basel, Switzerland) · 2025Review
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Authors and funding
2 authors.
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Abstract
Background: Fatty acid metabolism plays a major role in several inflammatory diseases such as endometriosis. However, its specific mechanism in endometriosis remains unclear. Therefore, this study aimed to investigate the hub genes involved in endometriosis and fatty acid metabolism using bioinformatics analyses. Methods: The R package sva was used to remove batch effects from the GSE120103 and GSE25628 datasets, resulting in the creation of a combined GEO dataset. Differential analysis of the combined GEO dataset was interposed with fatty acid metabolism-related genes. Differentially expressed genes associated with fatty acid metabolism (FAMRDEGs) were subsequently identified. Functional enrichment analyses were performed using the clusterProfiler package, whereas gene set enrichment analysis (GSEA) was used to identify significant pathways. Protein-protein interaction (PPI) networks were constructed using STRING and visualized using Cytoscape to identify hub genes. Moreover, regulatory networks involving transcription factors and microRNAs were constructed using ChIPBase and ENCORI databases, respectively. Hub genes were validated via expression comparison and receiver operating characteristic curve analysis. Results: We identified 405 DEGs in the combined dataset, including 168 and 237 with upregulated and downregulated expression, respectively. Of these, 17 were FAMRDEGs. These genes were significantly involved in arachidonic acid and fatty acid metabolic processes. GSEA highlighted pathways such as Hamai_apoptosis_via_trail_dn for genes whose expression was downregulated, along with nuclear receptors in lipid metabolism and toxicity for genes with upregulated expression. The PPI network identified six hub genes: Conclusion: The identified hub genes may be potential biomarkers of fatty acid metabolism in endometriosis. This reveals the potential molecular mechanisms underlying this metabolic process and identifies therapeutic targets for future interventions.
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