Background: Frontal fibrosis alopecia (FFA) is a primary cicatricial alopecia and has received increasing attention in recent years. However, the pathogenesis of FFA has not been fully elucidated.
Methods and results: Herein, we collected the transcriptome data of scalp lesions of seven patients with FFA and seven healthy controls. The differential expression analysis and weighted gene co-expression network analysis were conducted and we identified 458 differentially expressed genes (DEGs) in two key modules. Later, we performed functional enrichment analysis and functional modules identification, revealing the participation of immune response and fatty acid metabolism. Based on the results, we processed further studies. On the one hand, we analyzed the infiltrating immune cells of FFA through CIBERSORT algorithm, indicating the activation of M1 macrophage and CD8+ T cell. On the other hand, considering lipid metabolism of FFA and oxidative stress of hair follicle cells in alopecia, we explored the potential ferroptosis of FFA. By intersection of DEGs and ferroptosis-related genes from FerrDb database, 19 genes were identified and their expression was validated in an external dataset containing 36 FFA cases and 12 controls. Then, we used LASSO algorithms to construct a four-gene diagnostic model, which achieved an AUC of 0.924 in validation dataset. Additionally, the immune cells were found to be related to ferroptosis in FFA.
Conclusion: Taken together, this study contributed to reveal the molecular mechanisms of FFA and is expected to inspire future research on treatment.
Keywords: AI; DEG; ferroptosis; frontal fibrosis alopecia; immune cells; transcriptome analysis.
© 2024 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd.