LC-MS/MS-assisted label-free SERS blood analysis by self-position plasmonic platform for tumor screening

Label-free surface-enhanced Raman spectroscopy (SERS) blood analysis become an emerging technique in biomedical diagnosis. However, the poor signal homogeneity, the unsatisfied spectral features, and the low throughput of spectral analysis hinder its further clinical application. Herein, we illustrated a self-position SERS platform driven by the hydrophilic-hydrophobic features and combined with machine learning algorithm for precise lung cancer identification from benign group. To solve the problem that biomolecular information in SERS spectra partly lost caused by the formation of "protein crown", the SERS signals from serum components with different molecular weights were analyzed through the serum filtration process with a Nanosep tool, which results were confirmed by liquid chromatography with tandem mass spectrometry (LC-MS/MS) methods. Following that, robust machine learning classifiers were employed to explore the potential diagnostic information contained in the blood spectral data, achieving the exciting detection accuracy of 96.3% for identifying samples of the lung cancer from the of benign ones. This blood-SERS technology provides a promising way to overcome the clinical challenges in the identification of lung malignant and benign groups, and the functional SERS platform proposed in this work would further advance the application of blood-SERS technology in clinical cancer detection.