![]() We constructed high-accuracy biomarkers by using machine learning (ML) algorithms based on four key genes ( CD28, HOXC13, KRTAP1-3, and GPRC5D) involved in immune response and epidermis and hair development processes ( 4). Our previous study found that the over-activating of immune response and the dysfunction of epidermis and hair development processes are two essential factors for AA occurrence and development. ![]() Therefore, it is critical to establish a novel predictive tool to anticipate the recurrence of AA that might lead to more individualized interventions for AA patients and prevent the recurrence of AA. Although numbers treatments for AA have been introduced, the long-term efficacy and the therapeutic response varies widely ( 3). The prognosis in each AA patient is variable and unpredictable, and the extent of hair loss is the most significant prognostic factor. According to the intensity and area of hair loss, AA is divided into three subtypes: alopecia areata in patches (AAP), alopecia totalis (AT), and alopecia universalis (AU) ( 2). Finally, a prediction model that combined multiple markers was established by conducting a logistic regression analysis.Ĭonclusion: In the present study, we construct a novel model based on serum levels of BMP2, CD8A, PRF1, and XCL1, which served as a potential non-invasive prognostic biomarker for forecasting the recurrence of AA patients with high accuracy.Īlopecia areata (AA) is characterized by chronic, recurrent and non-scarring hair loss, with a 2% lifetime risk ( 1). Similarly, the serum levels of these markers were found remarkedly correlated with the Severity of Alopecia Tool (SALT) score. Then, the serum levels of these markers in different groups of AA patients were detected to validate the results of bioinformatics analysis. Results: We identified four key genes that significantly increased ( CD8A, PRF1, and XCL1) or decreased ( BMP2) in AA tissues, especially in the subtypes of AT and AU. Moreover, 40 serum samples of healthy children from the Department of Health Care, Wuhan Children’s Hospital were used for healthy control. And the serum level of proteins coded by key genes were quantitatively detected by ELISA. Clinical information and serum samples were collected before and after treatment. ![]() Then, 80 AA children were enrolled at the Department of Dermatology, Wuhan Children’s Hospital between January 2020 to December 2020. Methods: In this study, we conducted weighted gene co-expression network analysis (WGCNA) and functional annotation analysis to identify key genes that correlated to the severity of AA. Therefore, identifying clinically available biomarkers that predict the risk of AA recurrence could improve the prognosis for AA patients. When they progress to the subtypes of alopecia totalis (AT) or alopecia universalis (AU), the outcome is unfavorable. The outcome of AA patients varies greatly. Department of Dermatology, Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Chinaīackground: Alopecia areata (AA) is a disease featured by recurrent, non-scarring hair loss with a variety of clinical manifestations.Yuanquan Zheng * †, Yingli Nie * †, Jingjing Lu, Hong Yi and Guili Fu * ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |