Bioinformatics Analysis of Oxidative Stress-Related Genes and Immune Infiltration Patterns in Vitiligo.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40046940", "kind": "science-record", "revision": 4, "content_hash": "sha256:151ce7bfb6e16f7c4be4a1ad74708e902b3c8d878f6be46776c37afbeae03eed", "created_at": "2026-11-01T20:30:15Z", "updated_at": "2026-11-01T22:56:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics Analysis of Oxidative Stress-Related Genes and Immune Infiltration Patterns in Vitiligo.", "doi": "10.2147/CCID.S496781", "pmid": "40046940", "authors": ["Mingmei Yang", "Huiying Wang", "Ruzhi Zhang"], "journal": "Clinical, cosmetic and investigational dermatology", "year": 2025, "abstract": "Background: Vitiligo is an autoimmune disorder characterized by pigment loss, and current treatment options remain inadequate.\nObjective: This study aims to identify oxidative stress-related biomarkers and hub genes associated with vitiligo diagnosis through genomic analysis and to examine the role of immune cell infiltration in the pathogenesis of vitiligo.\nMethods: The mRNA expression profile dataset GSE75819 was retrieved from the GEO database. Differential expression of oxidative stress-related genes in vitiligo was analyzed using R software. Protein-protein interaction (PPI) analysis, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the differentially expressed genes (DEGs). Immune cell infiltration between vitiligo and normal control groups was assessed using the CIBERSORT algorithm. Additionally, two machine learning algorithms were employed to identify hub genes, perform enrichment analyses, and evaluate their correlation with immune infiltration.\nResults: A total of 415 Oxidative Stress-DEGs were identified in vitiligo, including 317 up-regulated and 98 down-regulated genes. PPI analysis highlighted the significance of certain ribosomal protein genes. KEGG enrichment analysis suggested an association between vitiligo and various neurodegenerative conditions, particularly through pathways such as oxidative phosphorylation and ribosome biogenesis. GO enrichment analysis indicated that the hub genes were significantly enriched in mitochondrial-related activities. Significant differences in immune infiltration patterns were observed between vitiligo patients and normal controls. Machine learning algorithms identified oxidative stress-related key genes associated with vitiligo, notably the DCT gene, whose expression was strongly linked to the activity of specific immune cell subsets and melanin biosynthetic pathways.\nConclusion: Oxidative stress-related DEGs, ribosomal proteins, immune infiltration, and hub genes related to melanin biosynthesis, particularly DCT, are closely associated with the pathogenesis of vitiligo. These findings enhance our understanding of vitiligo and may aid in identifying therapeutic targets for the disease.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) associated with vitiligo were identified using the “limma” package in R software. We combined the least absolute shrinkage and selection operator (LASSO) method with support vector machine recursive feature elimination (SVM-RFE) to identify key vitiligo-related genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40046940", "locator": "sections[4] \"Differential Analysis\" paragraph 2", "quote": "Differentially expressed genes (DEGs) associated with vitiligo were identified using the “limma” package in R software."}, {"asset": "source:pmid:40046940", "locator": "sections[8] \"Machine Learning Identification and Analysis of Key Genes\" paragraph 2", "quote": "We combined the least absolute shrinkage and selection operator (LASSO) method with support vector machine recursive feature elimination (SVM-RFE) to identify key vitiligo-related genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets are available in the NCBI-GEO repository (https://www.ncbi.nlm.nih.gov/geo/). All data generated or analyzed during this study are included in this published article. Data supporting the findings of this study may be obtained from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40046940", "locator": "sections[20] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets are available in the NCBI-GEO repository (https://www.ncbi.nlm.nih.gov/geo/). All data generated or analyzed during this study are included in this published article. Data supporting the findings of this study may be obtained from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 415 Oxidative Stress-DEGs were identified in vitiligo, including 317 up-regulated and 98 down-regulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40046940", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "A total of 415 Oxidative Stress-DEGs were identified in vitiligo, including 317 up-regulated and 98 down-regulated genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 415 Oxidative Stress-DEGs were identified in vitiligo, including 317 up-regulated and 98 down-regulated genes.", "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:40046940"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE75819"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE75819"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40046940", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/CCID.S496781", "sha256": "sha256:babdbed00920aeb201293140a34f990eaeb384abe6f452445141dcb8869ecbf4", "bytes": 35649, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/CCID.S496781", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11880687", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE75819", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75819", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE75819", "version": null, "note": "The datasets are available in the NCBI-GEO repository (https://www.ncbi.nlm.nih.gov/geo/). 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