Lung cancer diagnosis through extracellular vesicle analysis using label-free surface-enhanced Raman spectroscopy coupled with machine learning.

Label-free surface-enhanced Raman spectroscopy (SERS) based on extracellular vesicles (EVs) has great potential in cancer diagnosis. However, the repeatability and stability of the SERS signals and the accurate early prediction of multiple cell types based on a small number of samples still require further research. We developed a highly accurate classification approach to distinguish EVs derived from lung cancer and normal cells. This method was further validated using mixed samples of cell-derived EVs and plasma-derived EVs from both healthy and lung cancer mouse models and patients. The approach integrates label-free SERS analysis of EVs with machine learning techniques, including support vector machines (SVM) and convolutional neural networks (CNN), for robust classification. To preserve the native state of EVs, a capillary-based liquid-phase sampling method was employed, avoiding the need for drying. Additionally, the size and related properties of the SERS substrates were systematically optimized. Bayesian optimization was further applied to refine the SVM hyperparameters, enhancing classification performance. The classification error rate of the five-fold cross-validation (CVloss) of the SVM model (with hyperparameters optimized by Bayesian method) of A549 and BEAS-2B cell-derived EVs was 3.7%, and the overall accuracy of the independent test set reached 98.7%. The results of principal component analysis, the Shapley values and partial dependence plot analysis indicate higher levels of collagen and adenine in cancer cells compared to normal cells, this may be due to the large amount of collagen used as a source of nutrients in cancer cells and abnormal DNA or RNA metabolism. The overall accuracy of the test set predicted by the SVM and CNN models of plasma-derived EVs from lung cancer and healthy mice was 97.5 % and 95.8 %, respectively. Finally, the proposed strategy was used to discriminate plasma-derived EVs from lung cancer patients and healthy people, the CVloss of the SVM and CNN model was 7.7% and 8.3%, the overall accuracy of the independent test set was 91.5% and 95.4%, respectively. The proposed machine learning-assisted, liquid-phase enhanced SERS method offers notable advantages, including minimal sample volume, high stability, and excellent accuracy. The promising classification performance demonstrates its potential as a rapid and reliable approach for the early detection and monitoring of lung cancer through clinical blood sample analysis.