An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40630119", "kind": "science-record", "revision": 3, "content_hash": "sha256:4d78757b1ab5efe4a9810e1882fb6b78baa2c72e274b5e334bc1600dc855316d", "created_at": "2026-11-01T20:30:43Z", "updated_at": "2026-11-01T22:14:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An analysis of gene expression profiles through machine learning uncovers the new diagnostic signature for diabetic foot ulcers.", "doi": "10.3389/fgene.2025.1620749", "pmid": "40630119", "authors": ["Yingnan Li", "Ning Xiao", "Zhuoqun Wang", "Wenhai Wang", "Fengjiao Li", "Jiren Wang"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Purpose: Diabetic foot ulcers (DFUs), a serious diabetes complication, greatly increase disability and mortality, underscoring the need for effective diagnostic markers.\nMethods: We used GSE199939 and GSE134431 datasets from the Gene Expression Omnibus (GEO) database, removed batch effects, and identified differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules, followed by the integration of the protein-protein interaction (PPI) network to screen key genes, which were further optimized using LASSO regression. The gene set enrichment analysis (GSEA) analyzed key gene-related pathways, CIBERSORT assessed immune infiltration, and potential target drugs were predicted using the DGIdb database.\nResults: We identified 403 DEGs in DFUs, intersected them with 2,342 genes from a DFU-related WGCNA module to find 193 overlapping genes, and screened candidates via PPI network. LASSO regression finalized DCT, PMEL, and KIT as the key genes. GSEA analysis showed these three genes may influence the MAPK and PI3K-Akt pathways and were positively correlated with Dendritic. cells.resting. Drug target prediction identified 85 potential drugs for KIT, six for DCT, and six for PMEL.\nConclusion: This research highlights DCT, PMEL, and KIT as diagnostic biomarkers for DFUs, which are linked to melanin production and the MAPK/PI3K-Akt signaling pathways.", "claims": [{"id": "claim-method", "statement": "Specifically, 10-fold cross-validation for LASSO regression was employed, and each dataset was randomly divided into ten subsets of similar sizes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40630119", "locator": "sections[7] \"Construction of LASSO model and validation of key genes\" paragraph 2", "quote": "Specifically, 10-fold cross-validation for LASSO regression was employed, and each dataset was randomly divided into ten subsets of similar sizes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40630119", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The diagnostic AUCs were 0.970 for DCT, 0.982 for PMEL, and 0.936 for KIT.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40630119", "locator": "sections[16] \"Identifying of diagnostic markers in key genes associated with DFUs\" paragraph 2", "quote": "Specifically, the AUC for DCT was 0.970, for PMEL it was 0.982, and for KIT it was 0.936, all of which indicate significant diagnostic potential (AUC >0.9)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The diagnostic AUCs were 0.970 for DCT, 0.982 for PMEL, and 0.936 for KIT.", "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:40630119"], "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:GSE134431"], "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:GSE134431"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40630119", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2025.1620749", "sha256": 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