Label-free surface-enhanced Raman spectroscopy analysis method for liquid biopsy and its application in serum-based lung cancer diagnosis and classification

Liquid biopsy offers promise for the diagnosis of malignant tumors or their precancerous lesions at the subclinical stage, which is crucial for improving the survival rate of cancer. Label-free surface-enhanced Raman spectroscopy (SERS) has become an emerging detection method in liquid biopsy. The accuracy of SERS-based diagnosis relies on both precise measurement and effective data analysis. In our previous study, a large laser spot SERS method was proposed for the precise detection of complex liquids. In this work, a logical Raman data analysis method was developed, to address the lack of Raman characteristics and biological significance in current SERSbased diagnosis data analysis. The measured Raman data is classified to obtain a weighting matrix of classification criteria, and this matrix is used to establish a material correspondence with the Raman spectra, extract key Raman peaks, determine corresponding biomolecules and biological processes, thus enabling a logical analysis of the data. This method has been used to analyze the SERS data of lung cancer, it can not only show exceptional performance in diagnosing lung cancer and differentiating small cell lung cancer, but also obtain the Raman diagnostic and classification criteria with clear numerical values and reasonable biological significance, demonstrating its great potential value in precise biosensing.