Screening of potential markers for vitiligo based on bioinformatics and LASSO regression and prediction of Chinese medicine

Wei liang , Minni Huang , Yue Sun , Shuyu Guan
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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.
基于生物信息学和LASSO回归预测的白癜风潜在标志物筛选
目的利用生物信息学技术筛选与白癜风相关的生物标志物。方法首先从GEO数据库中获取白癜风基因表达谱,鉴定差异表达基因(DEGs);随后,对这些差异表达基因进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析。通过加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA),鉴定出模块中与白癜风关系最密切的核心基因,并与deg进行交叉分析。然后,对交叉基因进行蛋白-蛋白相互作用(PPI)网络分析。通过Cytohubba和least absolute shrinkage and selection operator (LASSO)回归分析进一步筛选关键基因,并通过单样本基因集富集分析(ssGSEA)探讨这些关键基因在免疫细胞浸润中的作用。此外,通过受试者工作特征(ROC)曲线验证关键基因的诊断有效性,并利用数据库预测关键基因相关的药物。最后通过逆转录定量聚合酶链反应(RT-qPCR)和Western blot实验验证这些关键基因的表达水平。结果共鉴定出667个deg,富集分析主要涉及细胞粘附分子、T细胞受体信号通路等。从Cytohubba的5种算法中筛选出19个核心基因,LASSO回归分析进一步确定了4个关键基因(IL7R、GZMH、CD3G和UBD)。免疫细胞浸润分析表明,这四个关键基因在免疫细胞中均有高表达。中药预测结果显示,有15种中药与关键基因相关。RT-qPCR结果显示,IL7R、GZMH和CD3G的表达显著上调(∗P <;0.05, * * P <;0.01, * * * P <;0.001), Western blot显示IL7R、GZMH、CD3G、UBD明显表达。结论本研究采用生物信息学方法探索白癜风的生物标志物,并通过体外实验验证了IL7R、GZMH和CD3G作为新的候选基因的潜力。这些基因可能成为白癜风诊断、预后和治疗的新靶点。
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