Table3_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）作为疾病早期检测的潜在生物标志物，具有广阔的应用前景。众多基于miRNA的诊断模型已被构建以区分患者与健康个体。为全面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普适性，需求对批次效应进行考虑的模型。本研究通过对转录因子（TF）介导的miRNA-miRNA相互作用网络进行分析，并采用miRNA对内样本表达比值作为预测标志物。结果表明，每对miRNA表达值之间的比值在不同数据源中表现出稳定性。我们构建了一种基于遗传算法的分类器，用于量化疾病发生概率的风险评分，并在发现阶段区分疾病状态与正常状态，同时使用COVID-19、肾细胞癌和肺腺癌的验证数据集。基于相互作用的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优异的性能，该分类器有望在疾病早期检测中实现精准应用。