miRBiT: a rules-based single-sample serum miRNA classifier for pan-cancer detection with multi-cohort validation

Liquid biopsy offers a minimally invasive approach to early cancer diagnosis. MicroRNAs (miRNAs) are small, non-coding RNAs showing excellent diagnostic potential due to their stability and dysregulation upon different physiological conditions. However, existing miRNA-based cancer classifiers rely on cohort-based comparisons, limiting their clinical utility. Extensive analyses in this study present miRNA binary trend (miRBiT), a miRNA rules-based single-sample classifier, trained on 16, 190 samples, tested across nine independent datasets, and further validated on 8 distinct disease cohorts. miRBiT utilizes miRNA expression signatures at an intra-sample level to classify 'cancer' and 'noncancer' samples, including healthy and other diseases with high sensitivity and specificity, enabling personalized predictions. Additionally, an interactive web application, miRBiT Explorer serum miRNA expression resource, is developed to visualize and validate serum miRNA expression patterns in 46, 349 samples. This study highlights the potential of miRNAs in robust cancer classification, enabling personalized, minimally invasive cancer screening and early detection at an unprecedented scale.