Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies

Abstract

This study aims to explore biomarkers associated with vitiligo and analyze the pathological role of immune cell infiltration in the disease. We used the robust rank aggregation (RRA) method to integrate three vitiligo data sets downloaded from gene expression omnibus database, identify the differentially expressed genes (DEGs) and analyze the functional correlation. Then, the comprehensive strategy of combined weighted gene coexpression network analysis (WGCNA) and logical regression of the selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) machine learning algorithm are employed to screen and biomarkers associated with vitiligo. Finally, the immune cell infiltration of vitiligo was evaluated by CIBERSORT, and the correlation between biomarkers and infiltrating immune cells was analyzed. Herein, we identified 131 robust DEGs, and enrichment analysis results showed that robust DEGs and melanogenesis were closely associated with vitiligo development and progression. TYR, TYRP1, DCT and LARP7 were identified as vitiligo-related biomarkers. Immune infiltration analysis demonstrated that CD4 T Cell, CD8 T Cell, Tregs, NK cells, dendritic cells, and macrophages were involved in vitiligo's pathogenesis. In summary, we adopted a comprehensive strategy to screen biomarkers related to vitiligo and explore the critical role of immune cell infiltration in vitiligo.Abbreviations: TYR, Tyrosinase; TYRP1, Tyrosinase-related protein-1; DCT, dopachrome tautomerase; LARP7, La ribonucleoprotein domain family, member-7; RRA, robust rank aggregation; DEGs, differentially expressed genes; WGCNA, weighted gene coexpression network analysis; LASSO, logical regression of the selection operator; SVM-RFE, support vector machine recursive feature elimination; RF, random forest; GWAS, Genome-wide association study; FasL, Fas-Fas ligand; Tregs, T-regulatory cells; NK, natural killer; GEPCs, gene expression profiling chips; GO, gene ontology; GSEA, gene set enrichment analysis; FDR, false discovery rate; AUC, area under the curve; ROC, receiver-operating characteristic; BP, biological process; CC, cellular component; MF, molecular function.

Keywords: Vitiligo; biomarkers; cibersort; immune cells; machine learning algorithm.

Graphical abstract

Figure 1.

Volcano plots of DEGs distribution in GSE53146 (a), GSE65127 (b) and GSE75819 (c). the yellow and purple dots represent upregulated and downregulated genes, respectively. (d) the heatmap of top 20 upregulated and downregulated robust DEGs identified by RRA method. yellow represents a high expression of robust DEGs, while purple represents a low expression of robust DEGs

Figure 2.

Functional enrichment analysis of robust DEGs. (a) GO enrichment analysis and its BP, CC, and MF three parts. (b) KEGG enrichment analysis. (c) GSEA showed that the top 5 signal pathways were most related to vitiligo pathology. (d) rank-based enrichment analysis visualized five signal pathways and showed melanogenesis signal pathways and gene ranks at the leading edge

Figure 3.

Screening characteristic related biomarkers via comprehensive strategy. (a) the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm is used to retain the most predictive features. (b) different colors represent different genes. based on support vector machine recursive feature elimination (SVM-RFE) algorithm (c) and random forest (RF) algorithm (d) to screen biomarkers

Figure 4.

(a) the cluster dendrogram of genes in independent data sets. the branching of clustering dendrograms of the most closely connected genes produced 10 gene coexpression modules. (b) relationships of consensus modules with samples. it contains a set of highly linked genes. each specified color represents a specific gene module. (c) analysis of the scale-free fit index for various soft-thresholding powers (beta). the red line represents merging threshold. (d) the mean connectivity of various soft threshold power was analyzed

Figure 5.

(a) the venn diagram showed the intersection of biomarkers obtained by four algorithms. (b) four associated markers were fitted into one variable, and ROC curve was used to verify the associated efficiency

Figure 6.

Immune cells infiltration analysis. (a) PCA results of immune infiltration between lesional and normal samples. (b) The correlation heatmap showed 22 kinds of immune cell infiltration, and 4 kinds of immune cells with no difference were removed. The size of color square represents correlation intensity, red represents the positive correlation, and blue represents the negative correlation. (c) The violin plot showed the difference of 22 kinds of immune cell infiltration between two groups. The red markers represent immune cells with significant differences in infiltration

Figure 7.

Analysis of the correlation between biomarkers and infiltrating immune cells. (a) Correlation between TYR and infiltrating immune cells. (b) Correlation between TYRP1 and infiltrating immune cells. (c) Correlation between DCT and infiltrating immune cells. (d) Correlation between LARP7 and infiltrating immune cells. The dot size represents correlation intensity between genes and immune cells. The lower the p-value, the more yellow the color, and the higher the p-value, the redder the color