ArticleIET systems biology2022
CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity.
Article in IET systems biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Artificial Intelligence in Hair Disorders: A Narrative Review.Dermatology and therapy · 2026Review
- Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets.Scientific reports · 2025Article
- Identification of lncRNAs involved in the hair follicle cycle transition of cashmere goats in response to photoperiod change.BMC genomics · 2025Article
- Shengqing Jiangzhuo Capsule Alleviates Intestinal Inflammation in Chronic Kidney Disease by Downregulating CHAC1 to Inactivate the HIF-1 Pathway.Mediators of inflammation · 2025Article
- Review
- CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity.IET systems biology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA-miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.