UNASSIGNED: We retrieved the data on CRRGs from previous studies and the GSE150910 dataset. The participants from the GSE150910 dataset were divided into training and internal validation sets. Next, we used several various bioinformatics methods and machine learning algorithms to screen genes. Next, we identified SEMA5A, COL7A1, and TUBB3, which were included in the random forest (RF) diagnostic model. Finally, external validation was conducted on data retrieved from the GSE184316 datasets.
UNASSIGNED: The results revealed that the RF diagnostic model could diagnose patients with IPF in the internal validation set with the area under the ROC curve (AUC) value of 0.905 and in the external validation with the AUC value of 0.767. Furthermore, real-time quantitative PCR and western blotting results revealed a significant decrease in SEMA5A (p < 0.05) expression level and an increase in COL7A1 and TUBB3 expression levels in TGF-β1-treated normal human lung fibroblasts.
UNASSIGNED: We constructed an RF diagnostic model based on SEMA5A, COL7A1, and TUBB3 expression in lung tissue for diagnosing patients with IPF.
■我们从以前的研究和GSE150910数据集中检索了CRRG的数据。来自GSE150910数据集的参与者被分为训练集和内部验证集。接下来,我们使用了几种不同的生物信息学方法和机器学习算法来筛选基因。接下来,我们确定了SEMA5A,COL7A1和TUBB3被包括在随机森林(RF)诊断模型中。最后,对从GSE184316数据集中检索的数据进行外部验证.
■结果显示,RF诊断模型可以在ROC曲线下面积(AUC)值为0.905的内部验证组中诊断IPF患者,在AUC值为0.767的外部验证中诊断IPF患者。此外,实时定量PCR和蛋白质印迹结果显示,TGF-β1处理的正常人肺成纤维细胞中SEMA5A表达水平显着降低(p<0.05),COL7A1和TUBB3表达水平升高。
■我们构建了基于SEMA5A的射频诊断模型,肺组织中COL7A1和TUBB3的表达用于诊断IPF患者。