Identification of Dopachrome Tautomerase (DCT) and Kinesin Family Member 1A (KIF1A) as Related Biomarkers and Immune Infiltration Characteristics of Vitiligo Based on Lasso-SVM Algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38084289", "kind": "science-record", "revision": 4, "content_hash": "sha256:d8ca6477506bac44841e295b5639907e171db2afadcaef86e29c05528a1be25e", "created_at": "2026-11-01T19:03:01Z", "updated_at": "2026-11-01T22:53:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Dopachrome Tautomerase (DCT) and Kinesin Family Member 1A (KIF1A) as Related Biomarkers and Immune Infiltration Characteristics of Vitiligo Based on Lasso-SVM Algorithms.", "doi": "10.2147/CCID.S443165", "pmid": "38084289", "authors": ["Yilu Zhao", "Kang Ge", "Ruzhi Zhang"], "journal": "Clinical, cosmetic and investigational dermatology", "year": 2023, "abstract": "Objective: To identify potential diagnostic markers for vitiligo and determine the significance of immune cell infiltration in pathology.\nMethods: Three publicly available gene expression profiles (GSE53146, GSE75819 and GSE65127 datasets) from human vitiligo and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 20 vitiligo and 20 control samples. Logical regression of the selection operator (LASSO) model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic curve (AUC) value was obtained and was used to evaluate the discriminatory ability. The expression level and diagnostic value of the biomarkers in vitiligo were further validated in the GSE65127 dataset (10 vitiligo patients and 10 healthy controls). Finally, the immune cell infiltration of vitiligo was evaluated by CIBERSORT, and the correlation between biomarkers and infiltrating immune cells was analyzed. The compositional patterns of the 22 types of immune cell fractions in vitiligo were estimated from the pooled cohorts using CIBERSORT. In addition, we established a mouse model of vitiligo with monobenzone and validated the screened biomarkers.\nResults: A total of 23 associated DEGs were identified, including 9 up-regulated and 14 down-regulated genes. Subsequently, 17 genes meeting prognostic criteria and 2 common genes (DCT and KIF1A) were obtained by SVM and Venn diagram screening. Immunodifferential analysis showed that microenvironment of vitiligo patients was altered. Finally, the different expression was verified by polymerase chain reaction (PCR).\nConclusion: Biomarkers associated with vitiligo can be screened by comprehensive strategies, and immune cell infiltration plays a key role in the development of vitiligo.", "claims": [{"id": "claim-method", "statement": "Two methods were used to identify possible biological markers of vitiligo. LASSO regression was used to study the application of VRDEGs, and 17 biochemical indicators that could be used in the diagnosis of vitiligo were identified (Figure 3A). VRDEGs were identified using the SVM-RFE method for four feature points (Figure 3B).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38084289", "locator": "sections[17] \"Identification and Validation of Diagnostic Feature Biomarkers\" paragraph 2", "quote": "Two methods were used to identify possible biological markers of vitiligo. LASSO regression was used to study the application of VRDEGs, and 17 biochemical indicators that could be used in the diagnosis of vitiligo were identified (Figure 3A). VRDEGs were identified using the SVM-RFE method for four feature points (Figure 3B)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38084289", "locator": "sections[26] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC values of the two related genes were 0.998 (95% CI 0.985–1.000), indicating high sensitivity and specificity.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The AUC values of the two related genes were 0.998 (95% CI 0.985–1.000), indicating high sensitivity and specificity."}, "evidence": [{"asset": "source:pmid:38084289", "locator": "sections[17] \"Identification and Validation of Diagnostic Feature Biomarkers\" paragraph 2", "quote": "The AUC values of the two related genes were 0.998 (95% CI 0.985–1.000), indicating high sensitivity and specificity."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The AUC values of the two related genes were 0.998 (95% CI 0.985–1.000), indicating high sensitivity and specificity.", "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:38084289"], "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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In addition, we established a mouse model of vitiligo with monobenzone and validated t", "ing CIBERSORT. In addition, we established a mouse model of vitiligo with monobenzone and valid", "as treated with petrolatum. The backs of the mice were shaved every 2–3 days so that MBEH was appli", "que in the “ggplot2” suite. Establishment of Mouse Vitiligo Model and Experimental Design Male"], "order_basis": "paper_text_order:1374", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Two methods were used to identify possible biological markers of vitiligo. 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