Table6_In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data.XLSX

MicroRNAs (miRNAs) are promising biomarkers for the early detection of disease, and many miRNA-based diagnostic models have been constructed to distinguish patients and healthy individuals. To thoroughly utilize the miRNA-profiling data across different sequencing platforms or multiple centers, the models accounting the batch effects were demanded for the generalization of medical application. We conducted transcription factor (TF)-mediated miRNA–miRNA interaction network analysis and adopted the within-sample expression ratios of miRNA pairs as predictive markers. The ratio of the expression values between each miRNA pair turned out to be stable across multiple data sources. A genetic algorithm-based classifier was constructed to quantify risk scores of the probability of disease and discriminate disease states from normal states in discovery, with a validation dataset for COVID-19, renal cell carcinoma, and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pairs demonstrated good performances in the discovery and validation datasets, and the classifier may be used accurately for the early detection of disease.

MicroRNAs（miRNAs）作为疾病早期诊断的潜在生物标志物，展现出巨大的应用前景。众多基于miRNA的诊断模型已被构建，旨在区分患者与健康个体。为了充分利用不同测序平台或多个中心收集的miRNA表达谱数据，并确保模型在医学应用中的泛化能力，需求对批次效应进行校正的模型。本研究通过转录因子（TF）介导的miRNA-miRNA相互作用网络分析，采纳了miRNA对内部表达比值作为预测指标。结果表明，各miRNA对的表达比值在多个数据源中保持稳定。基于遗传算法的分类器被构建，以量化疾病发生概率的风险评分，并在发现阶段将疾病状态与正常状态区分开来，同时使用COVID-19、肾细胞癌和肺腺癌的验证数据集进行验证。基于相互作用miRNA对表达比值的预测模型在发现和验证数据集中均表现出良好的性能，该分类器有望被准确应用于疾病的早期诊断。