Early and precise cancer diagnosis substantially improves patient survival. Recent work has revealed that the levels of multiple microRNAs in serum are informative as biomarkers for the diagnosis of cancers. Here, we designed a DNA molecular computation platform for the analysis of miRNA profiles in clinical serum samples. A computational classifier is first trained in silico using miRNA profiles from The Cancer Genome Atlas. This is followed by a computationally powerful but simple molecular implementation scheme using DNA, as well as an effective in situ amplification and transformation method for miRNA enrichment in serum without perturbing the original variety and quantity information. We successfully achieved rapid and accurate cancer diagnosis using clinical serum samples from 22 healthy people (8) and people with lung cancer (14) with an accuracy of 86.4%. We envision that this DNA computational platform will inspire more clinical applications towards inexpensive, non-invasive and rapid disease screening, classification and progress monitoring.

早期和精确的癌症诊断可以大大提高患者的生存率。最近的工作表明，血清中多种microRNA的含量可作为诊断癌症的生物标志物。在这里，我们设计了一个DNA分子计算平台，用于分析临床血清样品中的miRNA谱。首先使用The Cancer Genome Atlas的miRNA配置文件对计算机进行分类训练。其次是使用DNA的计算功能强大但简单的分子实施方案，以及有效的原位扩增和转化方法，可在不干扰原始品种和数量信息的情况下富集血清中的miRNA。我们使用22位健康人（8）和肺癌（14）的临床血清样本成功实现了快速，准确的癌症诊断，准确度为86.4％。我们设想，这种DNA计算平台将激发更多的临床应用，朝着廉价，无创和快速的疾病筛查，分类和进展监测的方向发展。