Cancer is a global health issue, significantly impacting individuals and society, and is one of the major threats to public health. Currently, 1 in 6 deaths worldwide is caused by cancer. According to the WHO’s “World Cancer Report 2020,” the number of cancer cases globally could increase by 60% over the next 20 years. The greatest leverage in saving lives lies in the timely diagnosis and treatment of cancer. Therefore, exploring cancer detection and diagnostic methods to intervene in cancer incidence and mortality is of great importance.

In recent years, cancer diagnostic technologies have continually evolved, but most traditional cancer screening methods still face issues such as high invasiveness, high cost, inconvenience, long detection cycles, radiation exposure, low accuracy, and low sensitivity and specificity. These factors result in low compliance among the public for screening.

To address these shortcomings, liquid biopsy technology has rapidly developed, emerging as a highly promising technological pathway. However, it currently faces the challenge of extremely low concentrations of biochemical cancer biomarkers. If new methods can be introduced to improve current detection limits, it could revolutionize the current cancer screening dilemma, minimizing harm and false positives while maximizing the benefits of screening. This would significantly improve patient survival rates and reduce the socioeconomic burden.

Molecular computing is an emerging paradigm that plays a crucial role in data storage, biological computing, and clinical diagnostics, showing a promising developmental trajectory:

Synthetic DNA is a programmable polymer capable of storing and processing information, enabling multifunctional computing devices from logic circuits to neural networks. Additionally, DNA provides a natural interface for molecular recognition (e.g., DNA, RNA, proteins, and metabolic molecules), which is essential for creating complex biological input patterns. DNA-based molecular classifiers can achieve powerful pattern recognition and accurate disease diagnosis when training classifiers in computational tasks. Compared to Boolean logic types, neural network architectures offer faster response times, higher parallelism in amplification circuits, and greater tolerance to damaged inputs in complex environments.

Based on the 2023 BUCT-China team’s project and the aforementioned advantages, we aim to construct a classifier based on DNA integrated circuits capable of performing neuromorphic computing at the molecular level for medical diagnostics.

● microRNAs are crucial intracellular components that play key roles in both normal and pathological states, especially in cancer research. Extensive studies have shown that microRNAs regulate the expression of their target mRNAs, playing significant roles in processes such as cancer proliferation, apoptosis, invasion, metastasis, and genomic instability, as well as in resistance to chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Moreover, tumor microRNA expression profiles can be used to define cancer-related subtypes, predict patient survival rates, and treatment responses. Importantly, because cancer cells release intracellular RNA into the surrounding bodily fluids during apoptosis, the detection of microRNA biomarkers in biological fluids enables non-invasive cancer monitoring.

● CRISPR technology, as a revolutionary gene-editing tool, has been widely used in biomedical research and clinical applications. With its highly specific DNA recognition and cleavage capabilities, CRISPR can precisely target and modify specific gene sequences, playing a critical role in gene function studies and showing tremendous potential in disease treatment, gene repair, and diagnostics. Its rapid, efficient, and versatile characteristics make it a pivotal tool in modern molecular biology and genetic engineering.

Therefore, this project aims to develop a diagnostic platform targeting microRNAs, combining polymerase self-amplification and a two-step CRISPR reaction to diagnose various cancers. The system first utilizes machine learning to screen target cancer-related microRNAs, then amplifies the target signal using a signal self-enhancement system constructed by a two-step polymerase reaction, and combines the CRISPR system for secondary signal amplification. Finally, the results are captured by single-molecule fluorescence super resolution microscopy for automated numerical analysis to derive the diagnostic outcome, establishing a quantitative analysis platform for fluorescence values and target concentrations. This diagnostic system:

1. Does not require PCR
2. Is suitable for various operational environmentss
3. Offers high accuracy and adjustable high sensitivity