Yan Yumin, Zhao Hongyang, Xing Lijie, Ouyang Ye, Zhang Linghao, Yang Jiayu, Qiu Jing, Qian Yongzhong, Ma Liang, Weng Rui, Su Xin
Key Laboratory of Agro-food Safety and Quality of Ministry of Agriculture and Rural Affairs, Institute of Quality Standard and Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
State Key Laboratory of Organic-Inorganic Composites, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
J Nanobiotechnology. 2025 Sep 1;23(1):598. doi: 10.1186/s12951-025-03643-0.
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.
传统的基于微小RNA（miRNA）的诊断方法通常对所有生物标志物一视同仁，忽略了每个miRNA对疾病分类的贡献各不相同这一事实。癌性诊断效价（CDV）的概念捕捉到了这种差异诊断重要性——这是一种量化每个miRNA与癌症关联的方向（致癌或保护）和程度的指标。在此，我们介绍一种基于聚合酶的DNA分子计算系统，该系统直接编码并整合CDV，以对非小细胞肺癌（NSCLC）进行加权分子分类。通过耦合DNA聚合酶介导的链延伸和置换（PB-DSD和级联PB-DSD），该系统将miRNA输入转化为跨越较宽CDV范围（1-25）的成比例分子信号，且探针复杂度最低。使用七个具有机器学习衍生CDV的NSCLC相关miRNA构建诊断分类器，在组织样本中准确率达到95%，在血浆样本中达到90%。与传统的toehold链置换系统相比，这种方法具有更广泛的可扩展性、更低的背景干扰和更准确的诊断逻辑。此外，我们通过跟踪用大蒜素和姜黄素治疗的荷瘤小鼠中CDV加权miRNA谱的药物诱导变化，证明了其在治疗监测中的效用。这项工作建立了一个分子可编程且基于生物学信息的诊断平台，提高了基于miRNA的癌症诊断的精度和可解释性。
