Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques.

The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection.

Understanding DNA Epigenetics by Means of Raman/SERS Analysis for Cancer Detection.

This study delves into the intricate interaction between DNA and nanosystems, exploring its potential implications for biomedical applications. The focus lies in understanding the adsorption geometry of DNA when in proximity to plasmonic nanoparticles, utilizing ultrasensitive vibrational spectroscopy techniques. Employing a combined Raman-SERS analysis, we conducted an in-depth examination to clarify the molecular geometry of interactions between DNA and silver nanoparticles. Our findings also reveal distinctive spectral features regarding DNA samples due to their distinctive genome stability. To understand the subtle differences occurring between normal and cancerous DNA, their thermal stability was investigated by means of SERS measurement performed before and after a thermal treatment at 94 °C. It was proved that thermal treatment did not affect DNA integrity in the case of normal cells. On the other hand, due to epimutation pattern that characterizes cancerous DNA, variations between spectra recorded before and after heat treatment were observed, suggesting genome instability. These findings highlight the potential of DNA analysis using SERS for cancer detection. They demonstrate the applicability of this approach to overcoming challenges associated with low DNA concentrations (e.g., circulating tumor DNA) that occur in biofluids. In conclusion, this research contributes significant insights into the nanoscale behavior of DNA in the presence of nanosystems.