Analyzing the Differential Expression of Vitiligo Genes by Bioinformatics Methods.
Background: Vitiligo is a hypopigmentation skin disease that is easy to diagnose but difficult to treat. The etiology of vitiligo is unknown, which may be related to genetic and immune factors. Objective: To provide potential targets for the treatment of vitiligo through identifying signature genes...
| Publicado en: | Dermatology Research & Practice Vol. 2025; pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/5/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187780788&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187780788 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16876105 32R7 jtl: Dermatology Research & Practice issn: 16876105 maglogo: N pubinfo: dt: 9/5/2025 vid: 2025 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 187780788 187780788 187780788 10.1155/drp/6672081 187780788 ppf: 1 ppct: 11 formats: tig: atl: Analyzing the Differential Expression of Vitiligo Genes by Bioinformatics Methods. aug: au: Lu, Quansheng He, Xi Sun, Yao Lu, Yu Jiang, Guan Bassukas, Ioannis D. affil: Department of Dermatology,, Affiliated Hospital of Xuzhou Medical University,, Xuzhou, Jiangsu, China, xzmc.edu.cn sug: subj: Gene Expression Vitiligo Familial and Genetic Bioinformatics Vitiligo Therapy Convolutional Neural Networks Human Algorithms Reverse Transcriptase Polymerase Chain Reaction RNA Statistical Significance Random Forest Spearman's Rank Correlation Coefficient Data Analysis Software ab: Background: Vitiligo is a hypopigmentation skin disease that is easy to diagnose but difficult to treat. The etiology of vitiligo is unknown, which may be related to genetic and immune factors. Objective: To provide potential targets for the treatment of vitiligo through identifying signature genes based on an artificial neural network (ANN) model. Methods: We downloaded two publicly available datasets from GEO database and identified DEGs. We trained the random forest and ANN algorithm using training set GSE75819 to further identify new gene features and predicted the possibility of vitiligo. In addition, we further validated the performance of our model through the test set GSE53148 and verified the diagnostic value of our model with the validation set GSE53148. Finally, we used RT‐qPCR to compare the expression of two genes randomly selected in this study in patients with vitiligo and healthy people. Results: Two genes were randomly selected from the 30 key genes identified by ANN and validated through RT‐qPCR in 6 vitiligo patients. The results showed that compared with the control group, the mRNA expression of FLJ21901 in the disease group was significantly upregulated, and the mRNA expression of MAST1 was significantly downregulated, with statistical significance. Conclusions: Through the identification of characteristic genes and the construction of a neural network model, it was found that the differentially expressed genes can provide a new potential target for the treatment of vitiligo. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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