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May 26, 2026Genes0 citationsOpen Access

Identification of Autophagy-Related Biomarker and Molecular Subtypes in Alopecia Areata Based on Bioinformatics Analysis, Machine Learning, and Experimental Validation

Key Points

This study aims to identify potential autophagy-related biomarkers and molecular subtypes in alopecia areata.
Identified autophagy-related differential expression genes (ARDEGs) by analyzing the GSE68801 dataset.
Employed three machine learning algorithms to find candidate hub genes and assessed their diagnostic potential using ROC curves.
Used consensus clustering to categorize alopecia areata into two molecular subtypes and analyzed immune characteristics.
Identified 10 ARDEGs and four hub genes (ATG9B, EIF4EBP1, WIPI1, CCR2) validated in independent cohorts.
Nomogram integrating the four genes demonstrated strong diagnostic performance.
Two molecular subtypes showed distinct immune infiltration and pathways, with alterations in autophagy-related proteins.

Abstract

Background: Alopecia areata (AA) is a common autoimmune alopecia disease. Evidence suggests that autophagy-related genes (ARGs) may contribute to its pathophysiology. This study aims to explore and identify potential autophagy-related biomarkers and molecular subtypes in AA. Methods: In this study, autophagy-related differential expression genes (ARDEGs) in AA were identified by comparing the differentially expressed genes (DEGs) in the GSE68801 dataset with the ARGs. Then, we applied three different machine learning methods to identify key hub genes and further verified them on independent datasets. We used the receiver operating characteristic (ROC) curve to evaluate the diagnostic potential of these hub genes and constructed a predictive nomogram. In addition, this study also used the consensus clustering method to define two AA subtypes and explored their immune characteristics and functional pathways through ssGSEA, MCPcounter and enrichment analysis. Experimental validation included qRT-PCR for four hub genes and Western blotting for critical autophagy markers. Results: Our analysis detected 10 ARDEGs in AA. Applying three machine learning algorithms, we identified four candidate hub genes, ATG9B, EIF4EBP1, WIPI1 and CCR2, and verified their expression patterns in independent cohorts. The combined four-gene model and nomogram showed potential diagnostic performance. Consensus cluster analysis divided AA cases into two subtypes, each associated with different immune infiltration and functional pathways. Downregulation of ATG9B and EIF4EBP1 and upregulation of CCR2 were verified by qRT-PCR. Western blotting further suggested altered autophagy-related protein expression in AA lesions, characterized by a reduced LC3B-II/I ratio and Beclin-1 expression and increased SQSTM1 expression. Conclusions: This study identified four candidate autophagy-related genes and two exploratory molecular subtypes in AA and may provide clues for understanding autophagy-related immune dysregulation and support further validation of candidate diagnostic markers.

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