In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data - Ma, Cuidie; Zhang, Yonghao; Ding, Rui; Chen, Han; Wu, Xudong; Xu, Lida; Yu, Changyuan (Frontiers in Genetics, 2024)
miRNAs are promising biomarkers for 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 factors (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 source. Genetic algorithm-based classifier was constructed to quantify risk scores of probability of disease and to discriminate disease from normal in discovery, validation dataset for COVID-19, renal cell carcinoma and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pair demonstrated good performances in discovery and validation datasets, the classifier maybe used accurately for early detection of disease.