Department of Internal Medicine, Asthma and Allergy of The Medical University of Lodz, Medical University of Lodz, Lodz, Poland.
Department of Biostatistics and Translational Medicine of The Medical University of Lodz, Medical University of Lodz, Lodz, Poland.
Front Immunol. 2022 Jun 14;13:746360. doi: 10.3389/fimmu.2022.746360. eCollection 2022.
TGF-β and its receptors play a crucial role in asthma pathogenesis and bronchial remodeling in the course of the disease. TGF-β1, TGF-β2, and TGF-β3 isoforms are responsible for chronic inflammation, bronchial hyperreactivity, myofibroblast activation, fibrosis, bronchial remodeling, and change the expression of approximately 1000 genes in asthma. TGF-β SNPs are associated with the elevated plasma level of TGF-β1, an increased level of total IgE, and an increased risk of remodeling of bronchi.
The analysis of selected TGF-β1, TGF-β2, TGF-β3-related single-nucleotide polymorphisms (SNP) was conducted on 652 DNA samples with an application of the MassARRAY using the mass spectrometry (MALDI-TOF MS). Dataset was randomly split into training (80%) and validation sets (20%). For both asthma diagnosis and severity prediction, the C5.0 modelling with hyperparameter optimization was conducted on: clinical and SNP data (Clinical+TGF), only clinical (OnlyClinical) and minimum redundancy feature selection set (MRMR). Area under ROC (AUCROC) curves were compared using DeLong's test.
Minor allele carriers (MACs) in SNP rs2009112 [OR=1.85 (95%CI:1.11-3.1), p=0.016], rs2796821 [OR=1.72 (95%CI:1.1-2.69), p=0.017] and rs2796822 [OR=1.71 (95%CI:1.07-2.71), p=0.022] demonstrated an increased odds of severe asthma. Clinical+TGF model presented better diagnostic potential than OnlyClinical model in both training (p=0.0009) and validation (AUCROC=0.87 vs. 0.80,p=0.0052). At the same time, the MRMR model was not worse than the Clinical+TGF model (p=0.3607 on the training set, p=0.1590 on the validation set), while it was better in comparison with the Only Clinical model (p=0.0010 on the training set, p=0.0235 on validation set, AUCROC=0.85 vs. 0.87). On validation set Clinical+TGF model allowed for asthma diagnosis prediction with 88.4% sensitivity and 73.8% specificity.
Derived predictive models suggest the analysis of selected SNPs in TGF-β genes in combination with clinical factors could predict asthma diagnosis with high sensitivity and specificity, however, the benefit of SNP analysis in severity prediction was not shown.
TGF-β及其受体在哮喘发病机制和疾病过程中的支气管重塑中起着至关重要的作用。TGF-β1、TGF-β2 和 TGF-β3 同工型负责慢性炎症、支气管高反应性、成肌纤维细胞激活、纤维化、支气管重塑，并改变哮喘中约 1000 个基因的表达。TGF-βSNP 与 TGF-β1 血浆水平升高、总 IgE 水平升高以及支气管重塑风险增加有关。
应用基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS），对 652 个 DNA 样本进行了 TGF-β1、TGF-β2、TGF-β3 相关单核苷酸多态性（SNP）的分析。数据集随机分为训练集（80%）和验证集（20%）。对于哮喘的诊断和严重程度预测，使用 C5.0 模型进行了超参数优化，包括临床和 SNP 数据（Clinical+TGF）、仅临床数据（OnlyClinical）和最小冗余特征选择集（MRMR）。使用 DeLong 检验比较 ROC 曲线下面积（AUCROC）。
SNP rs2009112 的次要等位基因携带者（MAC）[OR=1.85（95%CI：1.11-3.1），p=0.016]、rs2796821 [OR=1.72（95%CI：1.1-2.69），p=0.017] 和 rs2796822 [OR=1.71（95%CI：1.07-2.71），p=0.022] 显示出严重哮喘的几率增加。Clinical+TGF 模型在训练集（p=0.0009）和验证集（AUCROC=0.87 对 0.80，p=0.0052）中均优于 OnlyClinical 模型，具有更好的诊断潜力。同时，MRMR 模型并不逊于 Clinical+TGF 模型（训练集 p=0.3607，验证集 p=0.1590），但优于 OnlyClinical 模型（训练集 p=0.0010，验证集 p=0.0235，AUCROC=0.85 对 0.87）。在验证集上，Clinical+TGF 模型可实现 88.4%的敏感性和 73.8%的特异性来预测哮喘的诊断。
所提出的预测模型表明，结合临床因素分析 TGF-β 基因中的选定 SNP 可以高度敏感和特异性地预测哮喘的诊断，但 SNP 分析在严重程度预测中的获益尚未得到证实。
