Integrating traditional omics and AI-driven approaches for discovery and validation of novel MicroRNA biomarkers and therapeutic targets in thyroid cancer

Our study exemplifies the synergistic value of integrating traditional omics approaches with AI-driven analytics for biomarker and drug target discovery. The combination of machine learning-based feature selection from bulk transcriptomics with single-cell spatial validation addresses limitations of each approach used independently. This integrated framework successfully identified has-miR-6756-5p as both a diagnostic biomarker and therapeutic target, demonstrating how traditional experimental validation coupled with computational prediction enhances translational potential. The multi-scale approach spanning bulk transcriptomics, AI-driven biomarker selection, single-cell characterization, and functional validation represents an effective paradigm for developing clinically relevant cancer biomarkers and therapeutic targets.