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The expression levels of microRNAs (miRNAs) are strongly linked to cancer progression, making them promising biomarkers for cancer detection. Enzyme-free signal amplification DNA circuits have facilitated the detection of low-abundance miRNAs. However, these methods may neglect the diagnostic value (or weight) of different miRNAs. Here, a molecular computing approach with weighted signal amplification is presented. Polymerase-mediated strand displacement is employed to assign weights to target miRNAs, reflecting the miRNAs' diagnostic values, followed by amplification of the weighted signals using localized DNA catalytic hairpin assembly. This method is applied to diagnose miRNAs for non-small cell lung cancer (NSCLC). Machine learning is used to identify NSCLC-specific miRNAs and assign corresponding weights for optimum classification of healthy and lung cancer individuals. With the molecular computing of the miRNAs, the diagnostic output is simplified as a single channel of fluorescence intensity. Cancer tissues (n = 18) and adjacent cancer tissues (n = 10) are successfully classified within 2.5 h (sample-to-result) with an accuracy of 92.86%. The weighted amplification strategy has the potential to extend to the digital detection of multidimensional biomarkers, advancing personalized disease diagnostics in point-of-care settings.
Zhao et al. (Fri,) studied this question.