Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.

Introduction: MicroRNAs (miRNAs) regulate gene expression and play an important role in carcinogenesis through complex interactions with messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). Despite their established influence on tumor progression and therapeutic resistance, the application of miRNA interaction networks for tumor tissue-of-origin (TOO) classification remains underexplored. Methods: We developed a machine learning (ML) framework that integrates miRNA-mRNA-lncRNA interaction networks to classify tumors by their tissue of origin. Using transcriptomic profiles from 14 cancer types in The Cancer Genome Atlas (TCGA), we constructed co-expression networks and applied multiple feature selection techniques including recursive feature elimination (RFE), random forest (RF), Boruta, and linear discriminant analysis (LDA) to identify a minimal yet informative subset of miRNA features. Ensemble ML algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions. Results: Our models achieved an overall 99% classification accuracy, distinguishing 14 cancer types with high robustness and generalizability. A minimal set of 150 miRNAs selected via RFE resulted in optimal performance across all classifiers. Furthermore, in silico validation revealed that many of the top miRNAs, including Discussion: Our integrated framework provides a biologically grounded, interpretable, and highly accurate approach for tumor tissue-of-origin classification. The identified miRNA biomarkers demonstrate strong translational potential, supported by clinical trial overlap, drug sensitivity data, and survival analyses. This work highlights the power of combining miRNA network biology with ML to improve precision oncology diagnostics and supports future development of liquid biopsy-based cancer classification.