Alopecia areata (AA) is an autoimmune disease typified by nonscarring hair loss with a variable clinical course. 

The goal of a new study published in Dermatology was to identify biomarkers that reflect the risk of AA progressing to alopecia totalis (AT) or alopecia universalis (AU).

The present study entailed bioinformatics analyses to select key genes that correlated to AU or AT, based on the whole-genome gene expression of 122 human scalp skin biopsy specimens obtained from NCBI-GEO GSE68801.

Overall, four key genes were identified that significantly increased (CD28) or decreased (HOXC13, KRTAP1-3, and GPRC5D) in AA tissues—especially, in the subtypes of AT and AU. The predictive accuracy for forecasting AA patients progressing to AT/AU reached 90.7% (87.9%) by logistic regression; 93.8% (79.9%) by classification trees; 100.0% (76.3%) by random forest; 96.9% (76.3%) by support vector machine; 83.5% (79.9%) by K-nearest neighbours; 97.1% (87.3%) by XGBoost; and 93.3% (80.6%) by neural network algorithms for the training (internal validation) cohort. In addition, two molecule drugs – azacitidine and anisomycin, were identified by Cmap database, which could have potential therapeutic effects on AA patients with high risk of progressing to AT/AU.

It was concluded that the high accuracy models for predicting the risk of AA patients progressing to AT or AU may be important in facilitating personalized therapeutic strategies and clinical management for different AA patients.

Source: Dermatology. 2021 May 18;1-11. doi: 10.1159/000515764.