Bioinformatic analysis of ferroptosis related biomarkers and potential therapeutic targets in vitiligo.

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To elucidate the role of ferroptosis-related genes (FRGs) in vitiligo, we conducted a comprehensive analysis of gene expression data from the GSE53146 and GSE65127 datasets obtained from the GEO database. We identified 31 differentially expressed FRGs (DE-FRGs), with 21 genes upregulated and 10 downregulated. Functional enrichment analysis revealed that these DE-FRGs are significantly involved in oxidative stress, immune regulation, and vitiligo-associated signaling pathways. Utilizing machine learning approaches, including LASSO and SVM-RFE, we identified four key marker genes (ALOX5, SNCA, SLC1A4, and IL33) with strong diagnostic potential. Immune landscape analysis demonstrated that these marker genes influence immune cell composition, particularly showing correlations with CD8 + T cells and regulatory T cells. Furthermore, drug-gene interaction analysis proposed potential therapeutic targets, while ceRNA network analysis uncovered intricate regulatory relationships involving miRNAs and lncRNAs. Collectively, our findings provide novel insights into the molecular mechanisms underpinning vitiligo and suggest new avenues for diagnostic and therapeutic development.", "claims": [{"id": "claim-method", "statement": "To evaluate the potential diagnostic capabilities of DE-FRGs in distinguishing vitiligo patients from healthy controls, we analyzed the merged datasets from GSE53146 and GSE65127 using two machine learning algorithms: LASSO logistic regression and SVM-RFE.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39814853", "locator": "sections[6] \"Four DE-FRGs identified as diagnostic genes for vitiligo\" paragraph 2", "quote": "To evaluate the potential diagnostic capabilities of DE-FRGs in distinguishing vitiligo patients from healthy controls, we analyzed the merged datasets from GSE53146 and GSE65127 using two machine learning algorithms: LASSO logistic regression and SVM-RFE."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used in this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39814853", "locator": "sections[30] \"notes\" paragraph 1", "quote": "The datasets used in this study are available from the corresponding author upon reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39814853"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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