{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:17:43Z","timestamp":1787026663592,"version":"build-2736575974"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2013protein 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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:\n                      <jats:italic>PTGS2, CYP2C9, HSDL2, HSD17B3, ACSL4<\/jats:italic>\n                      , and\n                      <jats:italic>CYP2C18<\/jats:italic>\n                      .\n                      <jats:italic>ACSL4<\/jats:italic>\n                      showed the strongest positive correlation with immune cell effector memory CD8 T cells, whereas\n                      <jats:italic>HSDL2<\/jats:italic>\n                      showed the strongest negative correlation with immune cell-activated CD8 T cells.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1529074","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:10:36Z","timestamp":1744848636000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Identification of fatty acid metabolism hub genes in endometriosis using integrative bioinformatics 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