Au/SiNCA-based SERS analysis coupled with machine learning for the early-stage diagnosis of cisplatin-induced liver injury.

Cisplatin has been widely applied in the clinical treatment of various cancers, whereas liver injury induced by its hepatotoxicity is still a severe issue. Reliable identification of early-stage cisplatin-induced liver injury (CILI) can improve clinical care and help to streamline drug development. Traditional methods, however, cannot achieve enough information at the subcellular level due to the requirement of the labeling process and low sensitivity. To overcome these, we designed an Au-coated Si nanocone array (Au/SiNCA) to fabricate the microporous chip as the surface-enhanced Raman scattering (SERS) analysis platform for the early diagnosis of CILI. A CILI rat model was established, and the exosome spectra were obtained. The principal component analysis (PCA)-representation coefficient-based k-nearest centroid neighbor (RCKNCN) classification algorithm was proposed as the multivariate analysis method to build the diagnosis and staging model. The PCA-RCKNCN model has been validated to achieve a satisfactory result, with accuracy and AUC of over 97.5%, and sensitivity and specificity of over 95%, indicating that SERS combined with the PCA-RCKNCN analysis platform can be a promising tool for clinical applications.

Serum-based surface-enhanced Raman spectroscopy combined with PCA-RCKNCN for rapid and accurate identification of lung cancer.

Early and precise diagnosis of lung cancer is critical for a better prognosis. However, it is still a challenge to develop an effective strategy for early precisely diagnose and effective treatments. Here, we designed a label-free and highly accurate classification serum analytical platform for identifying mice with lung cancer. Specifically, the microarray chip integrated with Au nanostars (AuNSs) array was employed to measure the surface-enhanced Raman scattering (SERS) spectra of serum of tumor-bearing mice at different stages, and then a recognition model of SERS spectra was constructed using the principal component analysis (PCA)-representation coefficient-based k-nearest centroid neighbor (RCKNCN) algorithm. The microarray chip can realize rapid, sensitive, and high-throughput detection of SERS spectra of serum. RCKNCN based on the PCA-generated features successfully differentiated the SERS spectra of serum of tumor-bearing mice at different stages with a classification accuracy of 100%. The most prominent spectral features for distinguishing different stages were captured in PCs loading plots. This work not only provides a practical SERS chip for the application of SERS technology in cancer screening, but also provides a new idea for analyzing the feature of serum at the spectral level.