Construction of regulatory network for alopecia areata progression and identification of immune monitoring genes based on multiple machine-learning algorithms

Abstract

Objectives Alopecia areata (AA) is an autoimmune related non cicatricial alopecia, with complete alopecia (AT) or generalized alopecia (AU) as severe AA. However, there are limitations in early identification and intervention of AA patients who may progress to severe AA, which will help to improve the incidence rate and prognosis of severe AA. Methods We obtained two AA-related datasets from the GEO database, identified the differentially expressed genes (DEGs), and identified the module genes most related to severe AA through weighted gene co-expression network analysis. Functional enrichment analysis, construction of protein–protein interaction network and competing endogenous RNA network, and immune cell infiltration analysis were performed to clarify the underlying biological mechanisms of severe AA. Subsequently, pivotal immune monitoring genes (IMGs) were screened through multiple machines learning algorithms, and the diagnostic effectiveness of the pivotal IMGs were validated by ROC. Results A total of 150 severe AA-related DEGs were identified, and the upregulated DEGs were mainly enriched in immune response, while the downregulated DEGs were mainly enriched in pathways related to hair cycle and skin development. Following four IMGs (LGR5, SHISA2, HOXC13 and S100A3) with good diagnostic efficiency were obtained. As an important gene of hair follicle stem cells stemness, we verified in vivo that LGR5 downregulation may be an important link leading to severe AA. Conclusion Our findings provide comprehensive understanding of the pathogenesis and underlying biological processes in patients with AA, and identification four potential IMGs, which is helpful for the early diagnosis of severe AA.

Bioinspired engineering ADSC nanovesicles thermosensitive hydrogel enhance autophagy of dermal papilla cells for androgenetic alopecia treatment

“…To assess the levels of macrophage chemotaxis, activation, and cytokine production abundances in AGA patients, we employed the ssGSEA algorithm. The methods for implementing bioinformatics analysis in detail were previously described [ 55 ].…”