Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques
Abstract
:1. Introduction
2. Results and Discussion
2.1. Surface-Enhanced Raman Scattering (SERS) of Urine Samples and Correlations between the Surface-Enhanced Raman Scattering (SERS) Vibrational Bands
2.2. Univariate Analysis
2.3. Multivariate Analysis
2.3.1. Multivariate Analysis of Raw Data
2.3.2. Multivariate Analysis of Normalized Data
2.4. Supported Vector Machine (SVM) Analysis
3. Materials and Methods
3.1. Nanoparticle Synthesis and Preparation of the Substrate
3.2. Analyte Deposition and Surface-Enhanced Raman Spectroscopy (SERS) Measurements
3.3. Research Ethics
3.4. Cohort of Patient Samples
3.5. Urine Collection
3.6. Multivariate Analysis
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Raman Shift Measured [cm−1] | Raman Shift Range Reported [cm−1] | Assignments in Literature | Tentative Assignments in This Study |
---|---|---|---|
525 | 495–533 | Cholesterol ester/ nucleic acids [33,34,35,36] Creatinine [37] Urea [38,39] Albumin [40,41] Uric acid [36] | Urea |
596 | 575–605 | Albumin [42] Creatinine [39,43,44] Urea [39] | Creatinine |
671 | 635–702 | Uric acid [33,34,35] Creatinine [38,39,44,45,46,47,48] Urea [37] Cholesterol [49] Xanthine [44] Collagen type I [40] DNA and proteins [36,41] Tyrosine [36] | Creatinine |
721 | 725–744 | Adenine, coenzyme A [33,36] Creatinine [38] Phosphatidylserine [40] Uric acid [34,35] Hypoxanthine [34,35] DNA [36] | Hypoxanthine/Adenine |
804 | 797–813 | Albumin [33,34,35] Creatinine [38] Phosphodiester [34,49] DNA [41] Uric acid [36] Serine [36] | Uric acid/Creatinine |
893 | 839–912 | D-Galactosamine [33,34,35,36] Creatinine [38,39,47,48,50] Urea [37] Albumin [42,49] Calcium oxalate [40] Hydroxybutyrate [39] Acetoacetate [39] Nitrogenous compounds [39] Isoprostanes [39] | Uric acid/Creatinine |
941 | 958–959 | Urea [38] Creatinine [50] Hydroxyproline [34] | ? |
1004 | 999–1096 | Phenylalanine [33,41] Urea [34,35,36,37,38,39,42,44,45,46,47,49,50,51] Creatinine [37,52] Albumin [42,49,53,54] Hydroxybutyrate [39,52] C–C aromatic ring stretching [40] Phosphate [39] | Urea |
1156 | 1128–1173 | Phenylalanine [33,34,35,36] Creatinine [37] Glucose [48] Urea [38,39] Cytosine/guanine [49] C–C skeletal stretching [40] Albumin [41] Tyrosine [41] D-mannose [36] | Cytosine? |
1235 | 1206–1250 | Albumin [50] Xanthine [55] Phenylalanine [34,41] Tryptophan [41] | ? |
1296 | 1285–1308 | Cytosine [40,49] Glutamine [41] | Cytosine? |
1335 | 1323–1383 | Guanine [33,36,40] Creatinine [56] Albumin [50] CH3 band [40,49] Tryptophan [40] Adenine [34,35,36] DNA/RNA bases [34,35,36] Hydroxybutyrate [39] Nucleic acids [41] | ketone bodies? |
1405 | 1420–1490 | Tryptophan [33,34,35] Creatinine [39,43,47,50] Albumin [38,42] Acetoacetate [39] Hydroxybutyrate [39] Glutamine [41] | ketone bodies? |
1598 | 1579–1615 | C=N and C=C stretching [40] Phenylalanine [34,35,41] Albumin [57] Urea [39] Tyrosine [41] Phenylalanine [41] | C=N and C=C stretching |
1675 | 1621–1745 | Tryptophan (IgG) [49] Carbonyl stretch(C=O)/cholesterol ester [40] C=C stretch vibration [40] Creatinine [44] Lipids [41] Phospholipids [41] | Carbonyl stretch/C=C stretch vibration |
CTRL | Stage 1 | Stage 2 | Stage 3 | Predicted | |
---|---|---|---|---|---|
CTRL | 43 | 2 | 0 | 0 | 45 |
Stage 1 | 1 | 27 | 1 | 3 | 32 |
Stage 2 | 0 | 3 | 10 | 1 | 14 |
Stage 3 | 0 | 0 | 0 | 3 | 3 |
Actual | 44 | 32 | 11 | 7 |
Discrimination Function | Two PCs | Three PCs | ||
---|---|---|---|---|
PC1 and PC5 | PC1–PC2 | PC1, PC5, PC7 | PC1–PC3 | |
Linear | 81 | 71 | 90 | 68 |
Quadratic | 80 | 73 | 86 | 80 |
Mahalanobis | 81 | 68 | 88 | 81 |
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Buhas, B.A.; Toma, V.; Beauval, J.-B.; Andras, I.; Couți, R.; Muntean, L.A.-M.; Coman, R.-T.; Maghiar, T.A.; Știufiuc, R.-I.; Lucaciu, C.M.; et al. Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques. Int. J. Mol. Sci. 2024, 25, 3891. https://doi.org/10.3390/ijms25073891
Buhas BA, Toma V, Beauval J-B, Andras I, Couți R, Muntean LA-M, Coman R-T, Maghiar TA, Știufiuc R-I, Lucaciu CM, et al. Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques. International Journal of Molecular Sciences. 2024; 25(7):3891. https://doi.org/10.3390/ijms25073891
Chicago/Turabian StyleBuhas, Bogdan Adrian, Valentin Toma, Jean-Baptiste Beauval, Iulia Andras, Răzvan Couți, Lucia Ana-Maria Muntean, Radu-Tudor Coman, Teodor Andrei Maghiar, Rareș-Ionuț Știufiuc, Constantin Mihai Lucaciu, and et al. 2024. "Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques" International Journal of Molecular Sciences 25, no. 7: 3891. https://doi.org/10.3390/ijms25073891