Investigation of hub genes and immune infiltration in androgenetic alopecia using bioinformatics analysis

BACKGROUND: Androgenetic alopecia (AGA) is a type of non-scarring hair loss. Current drugs for AGA are accompanied by adverse reactions and a high recurrence rate. Thus, the discovery of diagnostic biomarkers and therapeutic targets for AGA remains imperatively warranted. METHODS: The GSE90594 dataset, which contained scalp skin biopsies from 14 male AGA cases and healthy volunteers, was used to identify the differentially expressed genes (DEGs). Functional enrichment analysis was subsequently performed. Next, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database combined with the cytoHubba plugin of Cytoscape were used to obtain the key genes of AGA. Thereafter, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was performed to evaluate the relative abundance of immune cells between male AGA patients and healthy controls. The correlation between key genes and infiltrating immune cells was analyzed to obtain the significant immune-cell related genes (IRGs), then intersected with the DEGs between immortalized balding and non-balding human dermal papilla cells (DPCs) of the GSE93766 dataset as well as the DEGs obtained by the GSE90594 dataset, thus obtaining the hub genes of AGA. Finally, the hub genes were validated using GSE36169, which contained expression profiling of tissues biopsied from haired and bald scalps of five individuals with AGA. RESULTS: A total of 234 DEGs were obtained from the GSE90594 dataset, which were mainly enriched in the extracellular matrix (ECM)-related pathways and immune-related activities. The STRING database and ten algorithms in the cytoHubba plugin of Cytoscape disclosed 21 key DEGs. The results of the CIBERSORT algorithm revealed the relative abundances of 20 kinds of immune cells between diseased and healthy individuals, and yielded 15 IRGs involved in the pathogenesis of AGA. Next, the intersection analysis identified four hub genes of AGA, comprising COL1A2, PCOLCE, ITGAX, and LOX. The GSE36169 dataset validated the expression pattern of hub genes in the haired scalp of AGA patients. CONCLUSIONS: We discovered that the hub genes identified are closely linked with the causative factors of AGA, which could be used as the viable diagnostic and therapeutic target in the clinical applications.