Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36193416", "kind": "science-record", "revision": 4, "content_hash": "sha256:b13f048b6e6170983cbdf59ef5be3f56e24021626994f11870d0b46168852e9a", "created_at": "2026-11-01T18:15:30Z", "updated_at": "2026-11-01T22:07:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis.", "doi": "10.1155/2022/7731082", "pmid": "36193416", "authors": ["Lu Xing", "Tao Wu", "Li Yu", "Nian Zhou", "Zhao Zhang", "Yunjing Pu", "Jinnan Wu", "Hong Shu"], "journal": "Mediators of inflammation", "year": 2022, "abstract": "Background: Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms.\nMethods: GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region- (DMR-) genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, a protein-protein interaction (PPI) network was established to analyze the interaction between differentially expressed methylated genes. Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. Finally, the expressions of hub genes in psoriasis lesions and healthy controls were detected by immunohistochemistry (IHC) and quantitative real-time PCR (RT-qPCR).\nResults: In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which GJB2 was the final identified hub gene for psoriasis, with robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significantly higher in psoriasis samples than those in healthy controls. Additionally, GJB2 may be involved in the development and progression of psoriasis by disrupting the body's immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database.\nConclusion: The abnormal expression of GJB2 might play a critical role in psoriasis development and progression. The genes identified in our study might serve as a diagnostic indicator and therapeutic target in psoriasis.", "claims": [{"id": "claim-method", "statement": "The receiver operating characteristic (ROC) curve analysis was used to evaluate the discrimination ability of the hub gene in the GSE13355 dataset; the discrimination ability of each model was quantified by the area under the ROC curve (AUC).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36193416", "locator": "sections[9] \"2.7. The Receiver Operating Characteristic (ROC) Curve Analysis\" paragraph 2", "quote": "The receiver operating characteristic (ROC) curve analysis was used to evaluate the discrimination ability of the hub gene in the GSE13355 dataset; the discrimination ability of each model was quantified by the area under the ROC curve (AUC)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this study are freely available from the GSE13355 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13355), the GSE14905 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14905), and the GSE73894 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73894).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36193416", "locator": "sections[25] \"Data Availability\" paragraph 2", "quote": "The data underlying this study are freely available from the GSE13355 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13355), the GSE14905 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14905), and the GSE73894 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73894)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Further, ROC curves indicated that GJB2 was able to effectively differentiate between psoriatic samples and healthy control samples both in the GSE13355 dataset and in the GSE14905 dataset, with an AUC of 1 in the GSE13355 dataset (Figure 6(d)) and an AUC of 0.965 in the GSE14905 dataset (Figure 6(e)).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36193416", "locator": "sections[19] \"3.4. GJB2 Was a Diagnostic Indicator for Psoriasis\" paragraph 3", "quote": "Further, ROC curves indicated that GJB2 was able to effectively differentiate between psoriatic samples and healthy control samples both in the GSE13355 dataset and in the GSE14905 dataset, with an AUC of 1 in the GSE13355 dataset (Figure 6(d)) and an AUC of 0.965 in the GSE14905 dataset (Figure 6(e))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Further, ROC curves indicated that GJB2 was able to effectively differentiate between psoriatic samples and healthy control samples both in the GSE13355 dataset and in the GSE14905 dataset, with an AUC of 1 in the GSE13355 dataset (Figure 6(d)) and an AUC of 0.965 in the GSE14905 dataset (Figure 6(e)).", "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:36193416"], "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:GSE13355"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE13355"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36193416", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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