DNA-Mediated Bioinspired MXene Gas Sensor Array with Machine Learning for Noninvasive Cancer Recognition

Noninvasive odor sensing is important in environmental monitoring and medical diagnosis. The two-dimensional material MXene is widely used due to its unique sensing properties but has limitations in specifically recognizing a certain gas. This study developed a bioinspired biosensor array (BBA) whose core mechanism involved programming MXene sensing interfaces through DNA base sequences to mimic molecular recognition principles of biological olfactory receptors. Integrated with machine learning algorithms, the BBA achieved preliminary identification of cancer patients by detecting exhaled gas samples from different populations. Starting from DNA molecular length, base type, and chemical modification, different DNAs were prepared and screened by orthogonal experiments to obtain five DNA/MXene composite biosensing materials with excellent sensing properties. Combined with pure MXene, a Six-channel bioinspired biosensor Array(6C-BBA) was established, and a neural network algorithm was utilized for the preliminary validation of the real odor molecules of fruits. Finally, the high-performance 6C-BBA was used to detect breath samples from stomach cancer patients, lung cancer patients, intestinal cancer patients, and healthy people, and the array showed 86.3, 94.1, 89.5, and 86.3% accuracy. This study could offer a specific method for advanced biosensor development and reveal important applications in early noninvasive recognition of diseases.