Keloid Biomarkers and Their Correlation With Immune Infiltration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35719372", "kind": "science-record", "revision": 5, "content_hash": "sha256:394abbebad6392a546049e09e0c6f74dbec7257130bdf6097f9e9453fcb1ef21", "created_at": "2026-11-01T18:14:42Z", "updated_at": "2026-11-01T22:49:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Keloid Biomarkers and Their Correlation With Immune Infiltration.", "doi": "10.3389/fgene.2022.784073", "pmid": "35719372", "authors": ["Xufeng Yin", "Wenbo Bu", "Fang Fang", "Kehui Ren", "Bingrong Zhou"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Objective: This work aimed to verify the candidate biomarkers for keloid disorder (KD), and analyze the role of immune cell infiltration (ICI) in the pathology of keloid disorder. Methods: The keloid-related datasets (GSE44270 and GSE145725) were retrieved from the Gene Expression Omnibus (GEO). Then, differential expressed genes (DEGs) were identified by using the \"limma\" R package. Support vector machine-recursive feature elimination (SVM-RFE) and LASSO logistic regression were utilized for screening candidate biomarkers of KD. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic power of candidate biomarkers. The candidate biomarkers were further verified through qRT-PCR of keloid lesions and the matched healthy skin tissue collected from eight cases. In addition, ICI in keloid lesions was estimated through single-sample gene-set enrichment analysis (ssGSEA). Finally, the potential drugs to the treatment of KD were predicted in the Connectivity Map Database (CMAP). Results: A total of 406 DEGs were identified between keloid lesion and healthy skin samples. Among them, STC2 (AUC = 0.919), SDC4 (AUC = 0.970), DAAM1 (AUC = 0.966), and NOX4 (AUC = 0.949) were identified as potential biomarkers through the SVM-RFE, LASSO analysis and ROC analysis. The differential expressions of SDC4, DAAM1, and NOX4 were further verified in collected eight samples by qRT-PCR experiment. ICI analysis result showed a positive correlation of DAAM1 expression with monocytes and mast cells, SDC4 with effector memory CD4+ T cells, STC2 with T follicular helper cells, and NOX4 with central memory CD8+ T cells. Finally, a total of 13 candidate small molecule drugs were predicted for keloids treatment in CMAP drug database. Conclusion: We identified four genes that may serve as potential biomarkers for KD development and revealed that ICI might play a critical role in the pathogenesis of KD.", "claims": [{"id": "claim-method", "statement": "DEGs between normal fibroblasts (NFs) and normalized Keloid fibroblasts (KFs) groups were screened using limma R package (Ritchie et al., 2015), according to adjusted p-value <0.05 and fold-change >1.5.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35719372", "locator": "sections[4] \"Identification of Differentially Expressed Genes\" paragraph 2", "quote": "DEGs between normal fibroblasts (NFs) and normalized Keloid fibroblasts (KFs) groups were screened using limma R package (Ritchie et al., 2015), according to adjusted p-value <0.05 and fold-change >1.5."}], "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:35719372", "locator": "sections[25] \"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": "Among them, STC2 (AUC = 0.919), SDC4 (AUC = 0.970), DAAM1 (AUC = 0.966), and NOX4 (AUC = 0.949) were identified as potential biomarkers through the SVM-RFE, LASSO analysis and ROC analysis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35719372", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Among them, STC2 (AUC = 0.919), SDC4 (AUC = 0.970), DAAM1 (AUC = 0.966), and NOX4 (AUC = 0.949) were identified as potential biomarkers through the SVM-RFE, LASSO analysis and ROC analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Among them, STC2 (AUC = 0.919), SDC4 (AUC = 0.970), DAAM1 (AUC = 0.966), and NOX4 (AUC = 0.949) were identified as potential biomarkers through the SVM-RFE, LASSO analysis and ROC analysis.", "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:35719372"], "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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