Xuan Shurui, Zhang Jiayue, Guo Qinxing, Zhao Liang, Yao Xin
Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Diagnostics (Basel). 2023 Apr 17;13(8):1440. doi: 10.3390/diagnostics13081440.
Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed, and early detection is urgent to prevent advanced progression. Circulating microRNAs (miRNAs) have been diagnostic candidates for multiple diseases. However, their diagnostic value has not yet been fully established in COPD. The purpose of this study was to develop an effective model for the diagnosis of COPD based on circulating miRNAs. We included circulating miRNA expression profiles of two independent cohorts consisting of 63 COPD and 110 normal samples, and then we constructed a miRNA pair-based matrix. Diagnostic models were developed using several machine learning algorithms. The predictive performance of the optimal model was validated in our external cohort. In this study, the diagnostic values of miRNAs based on the expression levels were unsatisfactory. We identified five key miRNA pairs and further developed seven machine learning models. The classifier based on LightGBM was selected as the final model with the area under the curve (AUC) values of 0.883 and 0.794 in test and validation datasets, respectively. We also built a web tool to assist diagnosis for clinicians. Enriched signaling pathways indicated the potential biological functions of the model. Collectively, we developed a robust machine learning model based on circulating miRNAs for COPD screening.
慢性阻塞性肺疾病（COPD）的诊断严重不足，早期检测对于预防疾病进展至关重要。循环微RNA（miRNA）已成为多种疾病的诊断标志物。然而，其在COPD中的诊断价值尚未完全明确。本研究旨在建立一种基于循环miRNA的COPD诊断模型。我们纳入了两个独立队列的循环miRNA表达谱，其中包括63例COPD样本和110例正常样本，然后构建了基于miRNA对的矩阵。使用多种机器学习算法开发诊断模型。在外部队列中验证了最佳模型的预测性能。在本研究中，基于表达水平的miRNA诊断价值并不理想。我们鉴定了五个关键的miRNA对，并进一步开发了七个机器学习模型。基于LightGBM的分类器被选为最终模型，在测试和验证数据集中的曲线下面积（AUC）值分别为0.883和0.794。我们还构建了一个网络工具来辅助临床医生进行诊断。富集的信号通路表明了该模型的潜在生物学功能。我们共同开发了一种基于循环miRNA的强大机器学习模型用于COPD筛查。
