{"title":"Screening of potential markers for vitiligo based on bioinformatics and LASSO regression and prediction of Chinese medicine","authors":"Wei liang , Minni Huang , Yue Sun , Shuyu Guan","doi":"10.1016/j.jhip.2025.06.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to use bioinformatics techniques to screen biomarkers related to vitiligo.</div></div><div><h3>Methods</h3><div>Firstly, the gene expression profiles of vitiligo were obtained from the GEO database, and differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on these differentially expressed genes. Through weighted gene co-expression network analysis (WGCNA), the core genes in the module most closely related to vitiligo were identified, and an intersection analysis was conducted with the DEGs. Next, a protein-protein interaction (PPI) network analysis was carried out on the intersection genes. Key genes were further screened using Cytohubba and least absolute shrinkage and selection operator (LASSO) regression analysis, and the roles of these key genes in immune cell infiltration were explored through single-sample gene set enrichment analysis (ssGSEA). In addition, the diagnostic effectiveness of the key genes was verified by the receiver operating characteristic (ROC) curve, and drugs related to the key genes were predicted using databases. Finally, the expression levels of these key genes were verified through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot experiments.</div></div><div><h3>Results</h3><div>A total of 667 DEGs were identified, and the enrichment analysis mainly involved cell adhesion molecules, T cell receptor signaling pathway, etc. Nineteen core genes were screened out from the five algorithms of Cytohubba, and LASSO regression analysis further determined four key genes <em>(IL7R, GZMH, CD3G,</em> and <em>UBD</em>). Immune cell infiltration analysis showed that these four key genes had high expression in immune cells. The prediction results of traditional Chinese medicine showed that 15 traditional Chinese medicines were related to the key genes. The results of RT-qPCR showed that the expressions of <em>IL7R</em>, <em>GZMH</em>, and <em>CD3G</em> were significantly upregulated (<sup>∗</sup><em>P</em> < 0.05, <sup>∗∗</sup><em>P</em> < 0.01, <sup>∗∗∗</sup><em>P</em> < 0.001), and Western blot showed obvious expressions of <em>IL7R</em>, <em>GZMH</em>, <em>CD3G</em>, and <em>UBD</em>.</div></div><div><h3>Conclusion</h3><div>This study used bioinformatics methods to explore the biomarkers of vitiligo, and verified the potential of <em>IL7R</em>, <em>GZMH</em>, and <em>CD3G</em> as novel candidate genes through <em>in vitro</em> experiments. These genes may become new targets for the diagnosis, prognosis, and treatment of vitiligo.</div></div>","PeriodicalId":100787,"journal":{"name":"Journal of Holistic Integrative Pharmacy","volume":"6 2","pages":"Pages 224-234"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Holistic Integrative Pharmacy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S270736882500024X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Objective
This study aimed to use bioinformatics techniques to screen biomarkers related to vitiligo.
Methods
Firstly, the gene expression profiles of vitiligo were obtained from the GEO database, and differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on these differentially expressed genes. Through weighted gene co-expression network analysis (WGCNA), the core genes in the module most closely related to vitiligo were identified, and an intersection analysis was conducted with the DEGs. Next, a protein-protein interaction (PPI) network analysis was carried out on the intersection genes. Key genes were further screened using Cytohubba and least absolute shrinkage and selection operator (LASSO) regression analysis, and the roles of these key genes in immune cell infiltration were explored through single-sample gene set enrichment analysis (ssGSEA). In addition, the diagnostic effectiveness of the key genes was verified by the receiver operating characteristic (ROC) curve, and drugs related to the key genes were predicted using databases. Finally, the expression levels of these key genes were verified through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot experiments.
Results
A total of 667 DEGs were identified, and the enrichment analysis mainly involved cell adhesion molecules, T cell receptor signaling pathway, etc. Nineteen core genes were screened out from the five algorithms of Cytohubba, and LASSO regression analysis further determined four key genes (IL7R, GZMH, CD3G, and UBD). Immune cell infiltration analysis showed that these four key genes had high expression in immune cells. The prediction results of traditional Chinese medicine showed that 15 traditional Chinese medicines were related to the key genes. The results of RT-qPCR showed that the expressions of IL7R, GZMH, and CD3G were significantly upregulated (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001), and Western blot showed obvious expressions of IL7R, GZMH, CD3G, and UBD.
Conclusion
This study used bioinformatics methods to explore the biomarkers of vitiligo, and verified the potential of IL7R, GZMH, and CD3G as novel candidate genes through in vitro experiments. These genes may become new targets for the diagnosis, prognosis, and treatment of vitiligo.