CD93: A Promising NETs-Related Biomarker for Diagnosis and Therapy in Actinic Keratosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40901493", "kind": "science-record", "revision": 4, "content_hash": "sha256:b40f8a8d909ad49b19e8a9f2039d5266bfdf5c52b8bc532cce0a44ec78d1c4ad", "created_at": "2026-11-01T20:31:01Z", "updated_at": "2026-11-01T22:57:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "CD93: A Promising NETs-Related Biomarker for Diagnosis and Therapy in Actinic Keratosis.", "doi": "10.2147/OTT.S539790", "pmid": "40901493", "authors": ["Guolin Ke", "Tao Yuan", "Chen Wu", "Min Gao"], "journal": "OncoTargets and therapy", "year": 2025, "abstract": "Background: Actinic keratosis (AK), a UV-induced precancerous skin condition potentially progressing to cutaneous squamous cell carcinoma (cSCC) with undefined mechanisms, was analyzed for neutrophil extracellular traps (NETs)-related biomarkers to identify key clinical targets.\nMethods: Transcriptomic profiles of AK retrieved from the GEO database were analyzed using the \"limma\" package to screen differentially expressed genes (DEGs), which were intersected with a curated NETs-related gene set to extract differentially expressed NETs-related genes (DE-NRGs). Functional enrichment analyses via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations identified enriched biological processes and pathways. Diagnostic biomarkers were screened using LASSO regression, random forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), with performance assessed by receiver operating characteristic (ROC) curves. Clinical validation compared CD93-positive microvessel density (CD93-MVD) levels between 53 AK samples and normal skin controls. Single-sample gene set enrichment analysis (ssGSEA) evaluated immune cell infiltration and neutrophil-related pathway activity, while molecular docking screened potential CD93-targeting drugs.\nResults: Nine DE-NRGs were identified by comparing AK samples with controls. GO/KEGG enrichment highlighted neutrophil chemotaxis, migration, and IL-17 signaling pathways. LASSO, RF, and SVM-RFE selected CD93 as a key diagnostic biomarker, showing overexpression in training (GSE207744, AUC=0.863) and validation (GSE32628, AUC=0.956) datasets. Immunohistochemistry confirmed significantly higher CD93-MVD levels between AK and normal skin (p=5.36×10-¹¹), with elevation in elderly patients (p=0.042), multifocal lesions (p=0.028), and with increasing severity (clinical: p=0.040; dermoscopic: p=0.007; pathological: p=2.3×10-6). ssGSEA revealed increased immune cell infiltration and neutrophil pathway activity in AK. Molecular docking identified Gö6976 as a CD93 inhibitor (ΔG=-7.5 kcal/mol).\nConclusion: Our study establishes CD93 as a key NETs-related biomarker in AK, mechanistically linking neutrophil-driven inflammation to angiogenesis. The CD93-Gö6976 interaction provides a translational basis for developing novel targeted therapies against AK.", "claims": [{"id": "claim-method", "statement": "For identifying NETs-related biomarkers, three advanced analytical methods were utilized: LASSO regression, random forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40901493", "locator": "sections[6] \"Identification of NETs-Related Diagnostic Biomarkers\" paragraph 2", "quote": "For identifying NETs-related biomarkers, three advanced analytical methods were utilized: LASSO regression, random forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this study is freely available in the GEO online database (https://www.ncbi.nlm.nih.gov/geo/). Our analyses, protocols, raw figures, or other information related to this study could be requested from the corresponding author(s) upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40901493", "locator": "sections[24] \"Data Sharing Statement\" paragraph 2", "quote": "The data used in this study is freely available in the GEO online database (https://www.ncbi.nlm.nih.gov/geo/). Our analyses, protocols, raw figures, or other information related to this study could be requested from the corresponding author(s) upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO, RF, and SVM-RFE selected CD93 as a key diagnostic biomarker, showing overexpression in training (GSE207744, AUC=0.863) and validation (GSE32628, AUC=0.956) datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40901493", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "LASSO, RF, and SVM-RFE selected CD93 as a key diagnostic biomarker, showing overexpression in training (GSE207744, AUC=0.863) and validation (GSE32628, AUC=0.956) datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "LASSO, RF, and SVM-RFE selected CD93 as a key diagnostic biomarker, showing overexpression in training (GSE207744, AUC=0.863) and validation (GSE32628, AUC=0.956) datasets.", "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:40901493"], "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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