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. 2023 Aug 13;13(8):813.
doi: 10.3390/bios13080813.

High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis

Bogdan Adrian Buhas  1   2   3 , Valentin Toma  4 , Nicolae Crisan  2   5 , Guillaume Ploussard  1 , Teodor Andrei Maghiar  3 , Rareș-Ionuț Știufiuc  4   6 , Constantin Mihai Lucaciu  6
Affiliations

High-Accuracy Renal Cell Carcinoma Discrimination through Label-Free SERS of Blood Serum and Multivariate Analysis

Bogdan Adrian Buhas et al. Biosensors (Basel). .

Abstract

Renal cell carcinoma (RCC) represents the sixth most frequently diagnosed cancer in men and is asymptomatic, being detected mostly incidentally. The apparition of symptoms correlates with advanced disease, aggressive histology, and poor outcomes. The development of the Surface-Enhanced Raman Scattering (SERS) technique opened the way for investigating and detecting small molecules, especially in biological liquids such as serum or blood plasma, urine, saliva, and tears, and was proposed as a simple technique for the diagnosis of various diseases, including cancer. In this study, we investigated the use of serum label-free SERS combined with two multivariate analysis tests: Principal Component Analysis combined with Linear Discriminate Analysis (PCA-LDA) and Supported Vector Machine (SVM) for the discrimination of 50 RCC cancer patients from 45 apparently healthy donors. In the case of LDA-PCA, we obtained a discrimination accuracy of 100% using 12 principal components and a quadratic discrimination function. The accuracy of discrimination between RCC stages was 88%. In the case of the SVM approach, we obtained a training accuracy of 100%, a validation accuracy of 92% for the discrimination between RCC and controls, and an accuracy of 81% for the discrimination between stages. We also performed standard statistical tests aimed at improving the assignment of the SERS vibration bands, which, according to our data, are mainly due to purinic metabolites (uric acid and hypoxanthine). Moreover, our results using these assignments and Student's t-test suggest that the main differences in the SERS spectra of RCC patients are due to an increase in the uric acid concentration (a conclusion in agreement with recent literature), while the hypoxanthine concentration is not statistically different between the two groups. Our results demonstrate that label-free SERS combined with chemometrics holds great promise for non-invasive and early detection of RCC. However, more studies are needed to validate this approach, especially when combined with other urological diseases.

Keywords: LDA-PCA; SERS; SVM; human serum; multivariate analysis; renal cell carcinoma; urine.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Mean SERS spectra of serum samples collected from controls (blue) and RCC (red) their standard deviations and the main vibrational peaks.
Figure 2
Figure 2
Score plot for the PCA analysis for the first 3 PCs, which explains 99% of the variance.
Figure 3
Figure 3
(A) Loadings plot for the first principal component PC1 and (B) the difference spectrum between the mean SERS of control and RCC patients samples.
Figure 4
Figure 4
Discrimination plot obtained by using a quadratic discrimination function, 12 Principal Components.
Figure 5
Figure 5
Discrimination plot for stages of RCC: green triangles = controls (CTRL), blue squares = Stage one (1), and red circles Stages 2 or 3 (for convenience in the graph legend they are marked 2).
Figure 6
Figure 6
Mean SERS spectra for RCC patients samples and controls after area normalization and their difference. Shadows show the standard deviations.
See this image and copyright information in PMC

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