ArticleJournal of nanobiotechnology2025
Polymerase-based DNA reactions for molecularly computing cancerous diagnostic valences of multiple miRNAs.
Article in Journal of nanobiotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Enzymatic DNA Reaction Networks for Orchestrating Stimuli-Dependent Temporal Molecular Pulse.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Diagnosis of Endometriosis: Dual-Amplification Strategy Driven by Copper Nanoclusters.Analytical chemistry · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Conventional miRNA-based diagnostic methods often treat all biomarkers equally, overlooking the fact that each miRNA contributes differently to disease classification. This differential diagnostic importance is captured by the concept of Cancerous Diagnostic Valence (CDV)-a metric that quantifies both the direction (oncogenic or protective) and magnitude of each miRNA's association with cancer. Here, we introduce a polymerase-based DNA molecular computing system that directly encodes and integrates CDVs to perform weighted molecular classification of non-small cell lung cancer (NSCLC). By coupling DNA polymerase-mediated strand extension and displacement (PB-DSD and cascade PB-DSD), the system translates miRNA inputs into proportional molecular signals spanning a wide CDV range (1-25), with minimal probe complexity. Seven NSCLC-related miRNAs with machine learning-derived CDVs were used to construct a diagnostic classifier, achieving 95% accuracy in tissue and 90% in plasma samples. Compared to conventional toehold strand displacement systems, this approach offers broader scalability, lower background interference, and more accurate diagnostic logic. Furthermore, we demonstrate its utility for therapeutic monitoring by tracking drug-induced shifts in CDV-weighted miRNA profiles in tumor-bearing mice treated with allicin and curcumin. This work establishes a molecularly programmable and biologically informed diagnostic platform that advances the precision and interpretability of miRNA-based cancer diagnostics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.