ArticleScientific reports2024
Identification of SLC40A1, LCN2, CREB5, and SLC7A11 as ferroptosis-related biomarkers in alopecia areata through machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Integrated Meta-Analysis of Scalp Transcriptomics and Serum Proteomics Defines Alopecia Areata Subtypes and Core Disease Pathways.International journal of molecular sciences · 2025Pooled it
- Artificial Intelligence in Hair Disorders: A Narrative Review.Dermatology and therapy · 2026Review
- CREB5 Inhibits Neuronal Ferroptosis via Transactivating ApoL6 to Regulate Lipid Droplet Metabolism After Spinal Cord Injury.CNS neuroscience & therapeutics · 2026Article
- Stress-induced sympathetic hyperactivation drives hair follicle necrosis to trigger autoimmunity.Cell · 2026Article
- Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets.Scientific reports · 2025Article
- Metabolic and Vascular Inflammation in Alopecia Areata: Linking Uric Acid, Lipid Imbalance and ICAM-1 Upregulation.Experimental dermatology · 2025Article
- A symphony of signals: the intricate role of lncRNAs in dermatological disorders.Clinical and experimental medicine · 2025Review
- Emerging Roles of Ferroptosis in Skin Pathophysiology.The Journal of investigative dermatology · 2025Review
- LncRNA NEAT1 and miRNA 101 as potential diagnostic biomarkers in patients with alopecia areata.Non-coding RNA research · 2025Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Alopecia areata (AA) is a common non-scarring hair loss condition driven by the collapse of immune privilege and oxidative stress. The role of ferroptosis, a type of cell death linked to oxidative stress, in AA is yet to be explored, even though it's implicated in various diseases. Using transcriptome data from AA patients and controls from datasets GSE68801 and GSE80342, we aimed to identify AA diagnostic marker genes linked to ferroptosis. We employed Single-sample gene set enrichment analysis (ssGSEA) for immune cell infiltration evaluation. Correlations between ferroptosis-related differentially expressed genes (FRDEGs) and immune cells/functions were identified using Spearman analysis. Feature selection was done through Support vector machine-recursive feature elimination (SVM-RFE) and LASSO regression models. Validation was performed using the GSE80342 dataset, followed by hierarchical internal validation. We also constructed a nomogram to assess the predictive ability of FRDEGs in AA. Furthermore, the expression and distribution of these molecules were confirmed through immunofluorescence. Four genes, namely SLC40A1, LCN2, CREB5, and SLC7A11, were identified as markers for AA. A prediction model based on these genes showed high accuracy (AUC = 0.9052). Immunofluorescence revealed reduced expression of these molecules in AA patients compared to normal controls (NC), with SLC40A1 and CREB5 showing significant differences. Notably, they were primarily localized to the outer root sheath and in proximity to the sebaceous glands. Our study identified several ferroptosis-related genes associated with AA. These findings, emerging from the integration of immune cell infiltration analysis and machine learning, contribute to the evolving understanding of diagnostic and therapeutic strategies in AA. Importantly, this research lays a solid foundation for subsequent studies exploring the intricate relationship between AA and ferroptosis.
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Registered trials
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