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

Abstract

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) c…