Zheng Yuanquan, Nie Yingli, Lu Jingjing, Yi Hong, Fu Guili
Department of Dermatology, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Med (Lausanne). 2023 Jun 8;10:1189134. doi: 10.3389/fmed.2023.1189134. eCollection 2023.
Alopecia areata (AA) is a disease featured by recurrent, non-scarring hair loss with a variety of clinical manifestations. The outcome of AA patients varies greatly. When they progress to the subtypes of alopecia totalis (AT) or alopecia universalis (AU), the outcome is unfavorable. Therefore, identifying clinically available biomarkers that predict the risk of AA recurrence could improve the prognosis for AA patients.
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. Then, 80 AA children were enrolled at the Department of Dermatology, Wuhan Children's Hospital between January 2020 to December 2020. Clinical information and serum samples were collected before and after treatment. And the serum level of proteins coded by key genes were quantitatively detected by ELISA. Moreover, 40 serum samples of healthy children from the Department of Health Care, Wuhan Children's Hospital were used for healthy control.
We identified four key genes that significantly increased (, and ) or decreased () in AA tissues, especially in the subtypes of AT and AU. Then, the serum levels of these markers in different groups of AA patients were detected to validate the results of bioinformatics analysis. Similarly, the serum levels of these markers were found remarkedly correlated with the Severity of Alopecia Tool (SALT) score. Finally, a prediction model that combined multiple markers was established by conducting a logistic regression analysis.
In the present study, we construct a novel model based on serum levels of , and , which served as a potential non-invasive prognostic biomarker for forecasting the recurrence of AA patients with high accuracy.
斑秃（AA）是一种以复发性、非瘢痕性脱发为特征且具有多种临床表现的疾病。AA患者的病情转归差异很大。当病情进展为全秃（AT）或普秃（AU）亚型时，预后不佳。因此，识别临床上可用的预测AA复发风险的生物标志物可以改善AA患者的预后。
在本研究中，我们进行了加权基因共表达网络分析（WGCNA）和功能注释分析，以识别与AA严重程度相关的关键基因。然后，于2020年1月至2020年12月期间在武汉市儿童医院皮肤科招募了80名AA患儿。在治疗前后收集临床信息和血清样本。通过酶联免疫吸附测定（ELISA）定量检测关键基因编码的蛋白质的血清水平。此外，将武汉市儿童医院保健科40名健康儿童的血清样本用作健康对照。
我们鉴定出四个关键基因，其在AA组织中显著升高（、和）或降低（），尤其是在AT和AU亚型中。然后，检测不同组AA患者中这些标志物的血清水平以验证生物信息学分析结果。同样，发现这些标志物的血清水平与脱发严重程度工具（SALT）评分显著相关。最后，通过进行逻辑回归分析建立了一个结合多个标志物的预测模型。
在本研究中，我们基于、和的血清水平构建了一个新模型，该模型作为一种潜在的非侵入性预后生物标志物，可高精度预测AA患者的复发情况。
