Wen Jian, Wan Lijia, Dong Xieping
Medical College of Nanchang University, Nanchang, Jiangxi, China.
JXHC Key Laboratory of Digital Orthopedics, Department of Orthopedics, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, China.
Front Genet. 2022 Nov 1;13:1032010. doi: 10.3389/fgene.2022.1032010. eCollection 2022.
Ankylosing spondylitis (AS) is a chronic inflammatory disorder of unknown etiology that is hard to diagnose early. Therefore, it is imperative to explore novel biomarkers that may contribute to the easy and early diagnosis of AS. Common differentially expressed genes between normal people and AS patients in GSE73754 and GSE25101 were screened by machine learning algorithms. A diagnostic model was established by the hub genes that were screened. Then, the model was validated in several data sets. and were screened using machine learning algorithms and established as a diagnostic model. Nomograms suggested that the higher the expression of , the higher was the risk of AS, while the reverse was true for . C-indexes of the model were no less than 0.84 in the validation sets. Calibration analyses suggested high prediction accuracy of the model in training and validation cohorts. The area under the curve (AUC) values of the model in GSE73754, GSE25101, GSE18781, and GSE11886 were 0.86, 0.84, 0.85, and 0.89, respectively. The decision curve analyses suggested a high net benefit offered by the model. Functional analyses of the differentially expressed genes indicated that they were mainly clustered in immune response-related processes. Immune microenvironment analyses revealed that the neutrophils were expanded and activated in AS while some T cells were decreased. and are potential blood biomarkers of AS, which might be used for the early diagnosis of AS and serve as a supplement to the existing diagnostic methods. Our study deepens the insight into the pathogenesis of AS.
强直性脊柱炎（AS）是一种病因不明的慢性炎症性疾病，早期难以诊断。因此，探索可能有助于AS简易早期诊断的新型生物标志物势在必行。通过机器学习算法筛选了GSE73754和GSE25101中正常人和AS患者之间常见的差异表达基因。由筛选出的枢纽基因建立了诊断模型。然后，在多个数据集中对该模型进行了验证。使用机器学习算法筛选并将其建立为诊断模型。列线图显示，[基因名称1]表达越高，AS风险越高，而[基因名称2]则相反。该模型在验证集中的C指数不低于0.84。校准分析表明该模型在训练和验证队列中的预测准确性较高。该模型在GSE73754、GSE25101、GSE18781和GSE11886中的曲线下面积（AUC）值分别为0.86、0.84、0.85和0.89。决策曲线分析表明该模型具有较高的净效益。差异表达基因的功能分析表明，它们主要聚集在免疫反应相关过程中。免疫微环境分析显示，AS中中性粒细胞扩增并激活，而一些T细胞减少。[基因名称1]和[基因名称2]是AS潜在的血液生物标志物，可用于AS的早期诊断，并作为现有诊断方法的补充。我们的研究加深了对AS发病机制的认识。
