Prediction of the Risk of Alopecia Areata Progressing to Alopecia Totalis and Alopecia Universalis: Biomarker Development with Bioinformatics Analysis and Machine Learning

Prediction of the Risk of Alopecia Areata Progressing to Alopecia Totalis and Alopecia Universalis: Biomarker Development with Bioinformatics Analysis and Machine Learning

Background: Alopecia areata (AA) is an autoimmune disease typified by nonscarring hair loss with a variable clinical course. Although there is an increased understanding of AA pathogenesis and progress in its treatments, the outcome of AA patients remains unfavorable, especially when they are progressing to the subtypes of alopecia totalis (AT) or alopecia universalis (AU). Thus, identifying biomarkers that reflect the risk of AA progressing to AT or AU could lead to better interventions for AA patients.

Methods: In this study, we conducted 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. Then, we built a biomarker using 8 different machine learning (ML) algorithms based on the key genes selected by bioinformatics analyses.

Results: We identified 4 key genes that significantly increased (CD28) or decreased (HOXC13, KRTAP1-3, and GPRC5D) in AA tissues, especially in the subtypes of AT and AU. Besides, the predictive accuracy (area under the curve [AUC] value) of the prediction models for forecasting AA patients progressing to AT/AU models 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 neighbors, 97.1% (87.3%) by XGBoost, and 93.3% (80.6%) by neural network algorithms for the training (internal validation) cohort. Besides, 2 molecule drugs, azacitidine and anisomycin, were identified by Cmap database. They might have the potential therapeutic effects on AA patients with high risk of progressing to AT/AU.

Conclusions: In the present study, we conducted high accuracy models for predicting the risk of AA patients progressing to AT or AU, which may be important in facilitating personalized therapeutic strategies and clinical management for different AA patients.


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