Identification of Novel Hub Genes Associated with Psoriasis Using Integrated Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36499614", "kind": "science-record", "revision": 3, "content_hash": "sha256:0db5026fbff8eeb37fac1d1cb18613f9eb14cffc436c6a131ee1b5d98216736e", "created_at": "2026-11-01T19:00:59Z", "updated_at": "2026-11-01T22:08:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Novel Hub Genes Associated with Psoriasis Using Integrated Bioinformatics Analysis.", "doi": "10.3390/ijms232315286", "pmid": "36499614", "authors": ["Qi Yue", "Zhaoxiang Li", "Qi Zhang", "Quanxin Jin", "Xinyuan Zhang", "Guihua Jin"], "journal": "International journal of molecular sciences", "year": 2022, "abstract": "Psoriasis is a chronic, prolonged, and recurrent inflammatory skin disease and the current therapeutics can only alleviate the symptoms rather than cure it completely. Therefore, we aimed to identify the molecular signatures and specific biomarkers of psoriasis to provide novel clues for psoriasis and targeted therapy. In the present study, the Gene Expression Omnibus (GEO) database was used to retrieve three microarray datasets (GSE166388, GSE50790 and GSE42632) and to explore the differentially expressed genes (DEGs) in psoriasis using the Affy package in R software. The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment were utilized to determine the common DEGs and their capabilities. The STRING database was used to develop DEG-encoded proteins and a protein-protein interaction network (PPI) and the Cytohubba plugin to classify hub genes. Using the NetworkAnalyst platform, we detected transcription factors (TFs), microRNAs and drug candidates interacting with hub genes. In addition, the expression levels of hub genes in HaCaT cells were detected by western blot. We screened the up- and downregulated DEGs from the transcriptome microarrays of corresponding psoriasis patients. Functional enrichment of DEGs in psoriasis was mainly associated with positive regulation of leukocyte cell-cell adhesion and T cell activation, cytokine binding, cytokine activity and the Wnt signaling pathway. Through further data processing, we obtained 57 intersecting genes in the three datasets and probed them in STRING to determine the interaction of their expressed proteins and we obtained the critical 10 hub genes in the Cytohubba plugin, including TOP2A, CDKN3, MCM10, PBK, HMMR, CEP55, ASPM, KIAA0101, ESC02, and IL-1β. Using these hub genes as targets, we obtained 35 TFs and 213 miRNAs that may regulate these genes and 33 potential therapeutic agents for psoriasis. Furthermore, the expression levels of TOP2A, MCM10, PBK, ASPM, KIAA0101 and IL-1β were observably increased in HaCaT cells. In conclusion, we identified potential biomarkers, risk factors and drugs for psoriasis.", "claims": [{"id": "claim-method", "statement": "The associated cellular immune infiltration algorithm was passed through CIBERSORT Methods. The significance of differences was determined using the Formulation t-test; p < 0.05 was considered statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36499614", "locator": "sections[17] \"4.5. Evaluation of Immune Cell Infiltration\" paragraph 3", "quote": "The associated cellular immune infiltration algorithm was passed through CIBERSORT Methods."}, {"asset": "source:pmid:36499614", "locator": "sections[23] \"4.11. Statistical Analysis\" paragraph 2", "quote": "The significance of differences was determined using the Formulation t-test; p < 0.05 was considered statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The three microarray psoriasis data sets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 6 June 2022)) with accession numbers GSE166388, GSE50790 and GSE42632. Information of PPI was available at https://cn.string-db.org/cgi (accessed on 10 June 2022). The network of miRNAs, TF and drugs associated with hub genes were available at https://www.networkanalyst.ca/NetworkAnalyst/home.xhtml (accessed on 18 June 2022). Identified drugs were input into the ClinicalTrials.gov registry (https://clinicaltrials.gov/ (accessed on 20 June 2022)) and pharmsnap (https://pharmsnap.zhihuiya.com/ (accessed on 20 June 2022)).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36499614", "locator": "sections[27] \"Data Availability Statement\" paragraph 2", "quote": "The three microarray psoriasis data sets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 6 June 2022)) with accession numbers GSE166388, GSE50790 and GSE42632. Information of PPI was available at https://cn.string-db.org/cgi (accessed on 10 June 2022). The network of miRNAs, TF and drugs associated with hub genes were available at https://www.networkanalyst.ca/NetworkAnalyst/home.xhtml (accessed on 18 June 2022). Identified drugs were input into the ClinicalTrials.gov registry (https://clinicaltrials.gov/ (accessed on 20 June 2022)) and pharmsnap (https://pharmsnap.zhihuiya.com/ (accessed on 20 June 2022))."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified potential biomarkers, risk factors, and candidate drugs for psoriasis through integrated bioinformatics analysis and experimental validation of selected hub genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36499614", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In conclusion, we identified potential biomarkers, risk factors and drugs for psoriasis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified potential biomarkers, risk factors, and candidate drugs for psoriasis through integrated bioinformatics analysis and experimental validation of selected hub genes.", "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:36499614"], "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:GSE166388"], "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:GSE166388"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36499614", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/ijms232315286", "sha256": "sha256:afac552c9892687cb51e58ea3fd279cc1ba3e40ccfef0076ea9d9143a15d02a5", "bytes": 44561, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/ijms232315286", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9737295", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE166388", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE166388", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE166388", "version": null, "note": "The three microarray psoriasis data sets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 6 June 2022)) with accession numbers GSE166388, GSE50790 and GSE42632. 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