Performance exploration of multi-gene panels of alopecia areata susceptibility and drug-binding targets.

OBJECTIVE

This study aims to identify potential target genes and therapeutic drugs for alopecia areata (AA).

METHODS

Utilizing training and testing data, we evaluated multi-gene panels derived from commonly upregulated genes in publicly available AA patient datasets. The functions of these genes in biological processes were analyzed to identify special multi-gene panels that may play crucial roles in AA. Differences in immune cell infiltration between AA patients and healthy controls were assessed using gene set variation analysis (GSVA) and the Wald test. Signature genes were further validated in specific subsets using single-cell RNA sequence data. Finally, molecular docking and molecular dynamics simulation were conducted to evaluate interactions between protein structures encoded by signature genes and the potential new drug candidates.

RESULTS

When the cut-off value of logFoldChage was greater than 1.0, 51 common upregulated genes were identified in the datasets GSE68801 and GSE45512, and the enrichment analysis of biological process indicated the significant involvement of immune cells in AA. The predictive performance of multi-gene panels demonstrated excellent accuracy in pathways related to "regulation of T cell-mediated cytotoxicity" and "cell killing." GSVA and the Wald test demonstrated that the infiltration of T cells and NK cells in AA patients was significantly higher than in healthy controls. Based on single-cell immune cell subsets, we found that within the macrophage migration inhibitory factor signaling pathway, the interactions between NK T cells, CD8 T cells, and melanocytes were observed exclusively in AA patients but not in healthy controls. This indicates that NK T and CD8 T cells may play an important role in the attack on hair follicles via melanocytes. Additionally, we selected several important biomarkers for molecular docking with interacting chemicals, evaluated the stability of drug-protein binding patterns through molecular dynamics simulation, and identified several potential targeted therapeutic agents.

CONCLUSION

In this study, we screened several key genes associated with immune cells and potential drug-like chemicals that could serve as targeted therapies for AA.