Primary cicatricial alopecias are characterized by dysregulation of shared gene expression pathways.
Eddy H C WangIsha MongaBrigitte N SalleeJames C ChenAlexa R AbdelazizRolando Perez-LorenzoLindsey A BordoneAngela M ChristianoPublished in: PNAS nexus (2022)
The primary forms of cicatricial (scarring) alopecia (PCA) are a group of inflammatory, irreversible hair loss disorders characterized by immune cell infiltrates targeting hair follicles (HFs). Lichen planopilaris (LPP), frontal fibrosing alopecia (FFA), and centrifugal cicatricial alopecia (CCCA) are among the main subtypes of PCAs. The pathogenesis of the different types of PCAs are poorly understood, and current treatment regimens yield inconsistent and unsatisfactory results. We performed high-throughput RNA-sequencing on scalp biopsies of a large cohort PCA patients to develop gene expression-based signatures, trained into machine-learning-based predictive models and pathways associated with dysregulated gene expression. We performed morphological and cytokine analysis to define the immune cell populations found in PCA subtypes. We identified a common PCA gene signature that was shared between LPP, FFA, and CCCA, which revealed a significant over-representation of mast cell (MC) genes, as well as downregulation of cholesterogenic pathways and upregulation of fibrosis and immune signaling genes. Immunohistological analyses revealed an increased presence of MCs in PCAs lesions. Our gene expression analyses revealed common pathways associated with PCAs, with a strong association with MCs. The indistinguishable differences in gene expression profiles and immune cell signatures between LPP, FFA, and CCCA suggest that similar treatment regimens may be effective in treating these irreversible forms of hair loss.
Keyphrases
- gene expression
- genome wide
- dna methylation
- single cell
- machine learning
- high throughput
- end stage renal disease
- genome wide identification
- chronic kidney disease
- copy number
- cell proliferation
- ejection fraction
- signaling pathway
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- drug delivery
- poor prognosis
- genome wide analysis
- artificial intelligence
- combination therapy
- long non coding rna
- replacement therapy
- bioinformatics analysis
- neural network
- idiopathic pulmonary fibrosis