A two-dimensional nanosheet-enhanced fluorescent biosensor with nucleic acid amplification for breast cancer-related miRNA detection.

Breast cancer remains a major global health burden with high incidence and mortality, necessitating advanced diagnostic solutions. While microRNAs (miRNAs) have emerged as promising biomarkers, the clinical translation faces a formidable obstacle: the lack of reliable detection techniques that are convenient, ultrasensitive, and highly specific enough for accurate diagnosis. To tackle these challenges, we presented an innovative strategy combined with catalyzed hairpin assembly (CHA) and a two-dimensional nanosheet for miRNA biomarker to enable precise breast cancer diagnosis (CHA-[Cu(tz)] system). The system leveraged the selective binding affinity of two-dimensional copper(I) 1,2,4-triazolate nanosheets ([Cu(tz)]) toward single-stranded DNA over double-stranded DNA, combined with CHA reaction for signal amplification. In this system, FAM-labeled hairpin DNA probes were adsorbed on the [Cu(tz)] nanosheets in the absence of the target, leading to fluorescence quenching via fluorescence resonance energy transfer. Upon introduction of miRNA-21, the target triggered the CHA cascade, effectively generating numerous rigid double-stranded DNA assemblies. These duplex structures exhibited minimal affinity toward the nanosheets, resulting in the restoration of fluorescence signals under isothermal conditions. The fluorescence recovery showed a strong linear correlation with miRNA-21 concentration in the range of 2-200 nM with a detection limit of 0.85 nM, which was further validated in serum samples and demonstrated excellent detection accuracy. Notably, the work not only overcome the drawbacks of complex operation and high background interference by facilitating a rapid (within 35 min) transition from standard to amplified detection, but also markedly enhanced detection efficiency by reducing non-specific signals and streamlining the workflow, thus providing a valuable new tool for cancer research and clinical diagnostics.