SERS Liquid Biopsy Profiling of Serum for the Diagnosis of Kidney Cancer

Abstract

Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre‐diabetes and diabetes

“…We emphasize that these results were obtained using only the training set in PCA feature reduction for SVM classification, not from the whole dataset, as it has been done in the literature, with biased and over‐optimistic results using Raman spectroscopy in various applications [16, 32, 33].…”

Label-Free SERS for Rapid Differentiation of SARS-CoV-2-Induced Serum Metabolic Profiles in Non-Hospitalized Adults

“…According to the CV loadings ( Figure S5a ), the major explanatory SERS vibrational modes attributed to the separations observed on CVA scores ( Figure 3 a) displayed intensity differences that corresponded to metabolites present in higher or lower relative levels in patient sera. The dominant peaks at 722, 1093, and 1331 cm –1 (ring modes in hypoxanthine/purines and analogues), 32 − 35 and bands at 1451 cm –1 (CH 2 deformations in lipids/proteins) and 1581 cm –1 (ring modes in amino acids), 36 , 37 as shown in Table S1 , were detected in higher quantities in the sera of COV– than COV+ patients group ( Figure 3 a), indicating metabolite downregulation in response to SARS-CoV-2 infection and recovery. On the other hand, the main characteristic bands detected at 630 cm –1 (uric acid) and 1250 cm –1 (amide III vibration), 34 , 36 were present in greater amounts in the sera of COV+ patients than COV– subjects ( Figure 2 a), suggesting upregulation of biomolecules in COV+ groups.…”

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

“…Furthermore, it is often concluded by a histopathological test, which is unfortunately time-consuming and is highly prone to human interpretation error. In addition to identification using urine, serum [ 139 ], and tear samples [ 140 ], SERS has also been used to understand the drug carrier mechanism [ 141 ] and classification of different stages of breast cancer [ 142 ], as shown in Figure 4 .…”