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Precise detection of trace miRNAs in liquid biopsies is critical for early lung cancer screening, yet current methods are frequently limited by complex reverse transcription procedures and potential enzymatic amplification biases. Herein, we developed a sequencing-free and highly portable dual-mode biosensing platform for decentralized clinical diagnostics by synergizing highly specific CRISPR/Cas13a recognition with exponential non-enzymatic catalytic hairpin assembly amplification (CHA). Trace target miRNAs activate Cas13a to trans-cleave the RNA trigger strand, while intact RNA triggers can precisely initiate downstream assembly networks. The Cas13a molecular lock completely eliminates spontaneous hairpin crosslinking interference. Based on this mechanism, we separately constructed a fluorescence and a multiplex surface-enhanced Raman scattering biosensing platform. The catalytic network drives reporter-embedded bimetallic nanoparticles to specifically bind plasmonic chips, forming sandwich resonance structures for absolute multiplex spectral quantification. The system synchronously processes and analyzes four lung cancer-associated miRNAs within 80 min, achieving an ultralow attomolar diagnostic limit and an exceptionally broad linear dynamic range spanning ten orders of magnitude. Clinical validation using 50 plasma samples demonstrated high quantitative agreement with standard qRT-PCR alongside exceptional anti-interference characteristics. Integrating a machine classification model to extract spectral features successfully clustered distinct pathological stages with the accuracy of 80.00 and 93.33%, respectively. The systematic framework, merging miniature programmable biochemical cascade pathways with multidimensional artificial intelligence predictions, provides a highly powerful novel diagnostic paradigm to overcome traditional barriers in complex liquid biopsy translational applications.
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Fan et al. (2026) studied this question.
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