Clinical, Cosmetic and Investigational Dermatology (Dec 2023)
Identification of Dopachrome Tautomerase (DCT) and Kinesin Family Member 1A (KIF1A) as Related Biomarkers and Immune Infiltration Characteristics of Vitiligo Based on Lasso-SVM Algorithms
Abstract
Yilu Zhao,1,2,* Kang Ge,3,* Ruzhi Zhang4 1Department of Dermatology, the First Affiliated Hospital of Bengbu Medical University, Bengbu Medical University, Bengbu, Anhui, People’s Republic of China; 2Department of Dermatology, the Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, People’s Republic of China; 3Department of Dermatology, the Affiliated Hospital of Jiaxing University, the First Hospital of Jiaxing, Jiaxing, Zhejiang, People’s Republic of China; 4Department of Dermatology and STD, the Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ruzhi Zhang, Department of Dermatology and STD, the Second Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, 241001, People’s Republic of China, Tel +8618761161826, Email [email protected]: To identify potential diagnostic markers for vitiligo and determine the significance of immune cell infiltration in pathology.Methods: Three publicly available gene expression profiles (GSE53146, GSE75819 and GSE65127 datasets) from human vitiligo and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 20 vitiligo and 20 control samples. Logical regression of the selection operator (LASSO) model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic curve (AUC) value was obtained and was used to evaluate the discriminatory ability. The expression level and diagnostic value of the biomarkers in vitiligo were further validated in the GSE65127 dataset (10 vitiligo patients and 10 healthy controls). Finally, the immune cell infiltration of vitiligo was evaluated by CIBERSORT, and the correlation between biomarkers and infiltrating immune cells was analyzed. The compositional patterns of the 22 types of immune cell fractions in vitiligo were estimated from the pooled cohorts using CIBERSORT. In addition, we established a mouse model of vitiligo with monobenzone and validated the screened biomarkers.Results: A total of 23 associated DEGs were identified, including 9 up-regulated and 14 down-regulated genes. Subsequently, 17 genes meeting prognostic criteria and 2 common genes (DCT and KIF1A) were obtained by SVM and Venn diagram screening. Immunodifferential analysis showed that microenvironment of vitiligo patients was altered. Finally, the different expression was verified by polymerase chain reaction (PCR).Conclusion: Biomarkers associated with vitiligo can be screened by comprehensive strategies, and immune cell infiltration plays a key role in the development of vitiligo.Keywords: vitiligo, predictive biomarker, Lasso-SVM algorithms
