A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Population and Study Design
2.2. Data and Sample Collection
2.3. Histopathological Analysis
2.4. Metabolome Analysis by GC-MS
2.5. Statistical Analysis
2.5.1. Classification Models
2.5.2. Ensemble Machine Learning Score (EML-Score)
2.6. Relevant Metabolites
2.6.1. Variables Important in Projection (VIP)
2.6.2. Volcano Plot
2.6.3. Genetic Algorithm
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Low Grade Bladder Cancer (BCL) (n = 55) | High Grade Bladder Cancer (BCH) (n = 94) | Control (n = 81) | |
---|---|---|---|
Age (years) 1 | 68.4 (64.0–73.0)/46–87 | 73.3 (69.0–79.0)/49–88 | 67.3 (58.0–73.0)/47–87 |
Men | 46 (83.6%) | 81 (86.2%) | 67 (82.7%) |
Women | 9 (16.4%) | 13 (13.8%) | 14 (17.3%) |
Non muscle invasive cancer (NMIC) | 55 (100%) (46 pTa; 9 pT1) 3 | 69 (73.4%) § (27 pTa; 42 pT1) | NA 2 |
Muscle invasive cancer (MIC) | 0 | 26 (26.6%) | NA |
Smokers | 33 (60.0%) * | 56 (59.6%) * | 20 (24.7%) |
No-Smokers | 22 (40.0%) * | 38 (40.4) * | 61 (75.3%) |
S 1 | Sp | PPV | NPV | PLR | NLR | A | ||
---|---|---|---|---|---|---|---|---|
Model I | Decision Tree | 0.97 ± 0.01 | 0.96 ± 0.02 | 0.98 ± 0.01 | 0.95 ± 0.04 | 26.28 | 0.03 | 0.97 |
PLS-DA | 1.00 ± 0.00 | 0.96 ± 0.02 | 0.98 ± 0.01 | 1.00 ± 0.00 | 27.00 | 0.00 | 0.99 | |
Naïve Bayes | 0.81 ± 0.03 | 0.99 ± 0.01 | 0.99 ± 0.01 | 0.74 ± 0.04 | 65.78 | 0.19 | 0.87 | |
Random Forest | 0.97 ± 0.01 | 0.98 ± 0.02 | 0.99 ± 0.01 | 0.95 ± 0.02 | 39.41 | 0.03 | 0.97 | |
k-NN | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | ND | 0.00 | 1.00 | |
aNN | 1.00 ± 0.00 | 0.99 ± 0.01 | 0.99 ± 0.01 | 1.00 ± 0.01 | 81.00 | 0.00 | 1.00 | |
Logistic Regression | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | ND | 0.00 | 1.00 | |
SVM | 1.00 ± 0.00 | 0.98 ± 0.02 | 0.99 ± 0.01 | 1.00 ± 0.00 | 40.50 | 0.00 | 0.99 | |
Deep Learning | 1.00 ± 0.00 | 0.99 ± 0.01 | 0.99 ± 0.01 | 1.00 ± 0.01 | 81.00 | 0.00 | 1.00 | |
Ensemble | 1.00 ± 0.00 | 0.98 ± 0.02 | 0.99 ± 0.01 | 1.00 ± 0.00 | 40.50 | 0.00 | 0.99 | |
Model II | Decision Tree | 0.94 ± 0.03 | 0.85 ± 0.05 | 0.92 ± 0.03 | 0.89 ± 0.04 | 6.44 | 0.07 | 0.91 |
PLS-DA | 0.64 ± 0.05 | 0.89 ± 0.04 | 0.91 ± 0.04 | 0.60 ± 0.05 | 5.96 | 0.41 | 0.73 | |
Naïve Bayes | 0.46 ± 0.05 | 0.93 ± 0.04 | 0.91 ± 0.04 | 0.50 ± 0.05 | 6.29 | 0.59 | 0.63 | |
Random Forest | 0.98 ± 0.01 | 0.56 ± 0.07 | 0.79 ± 0.04 | 0.94 ± 0.04 | 2.24 | 0.04 | 0.83 | |
k-NN | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | ND | 0.00 | 1.00 | |
aNN | 0.99 ± 0.01 | 0.35 ± 0.06 | 0.72 ± 0.04 | 0.95 ± 0.05 | 1.51 | 0.03 | 0.75 | |
Logistic Regression | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | ND | 0.00 | 1.00 | |
SVM | 0.98 ± 0.01 | 0.27 ± 0.06 | 0.70 ± 0.04 | 0.88 ± 0.08 | 1.35 | 0.08 | 0.72 | |
Deep Learning | 0.86 ± 0.04 | 0.80 ± 0.05 | 0.88 ± 0.03 | 0.77 ± 0.06 | 4.31 | 0.17 | 0.84 | |
Ensemble | 0.79 ± 0.06 | 0.83 ± 0.07 | 0.87 ± 0.05 | 0.73 ± 0.08 | 4.56 | 0.26 | 0.80 | |
Model III | Decision Tree | 1.00 ± 0.00 | 0.20 ± 0.13 | 0.93 ± 0.03 | 1.00 ± 0.00 | 1.25 | 0.00 | 0.94 |
PLS-DA | 0.51 ± 0.05 | 0.50 ± 0.11 | 0.86 ± 0.04 | 0.15 ± 0.04 | 1.03 | 0.97 | 0.51 | |
Naïve Bayes | 0.49 ± 0.05 | 0.90 ± 0.07 | 0.97 ± 0.02 | 0.24 ± 0.05 | 4.91 | 0.57 | 0.55 | |
Random Forest | 1.00 ± 0.00 | 0.00 ± 0.00 | 0.85 ± 0.03 | ND | 1.00 | ND | 0.85 | |
k-NN | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | ND | 0.00 | 1.00 | |
aNN | 1.00 ± 0.00 | 0.30 ± 0.10 | 0.89 ± 0.03 | 1.00 ± 0.00 | 1.43 | 0.00 | 0.90 | |
Logistic Regression | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | 1.00 ± 0.00 | ND | 0.00 | 1.00 | |
SVM | 1.00 ± 0.00 | 0.10 ± 0.07 | 0.86 ± 0.03 | 1.00 ± 0.00 | 1.11 | 0.00 | 0.87 | |
Deep Learning | 1.00 ± 0.00 | 0.50 ± 0.11 | 0.92 ± 0.02 | 1.00 ± 0.00 | 2.00 | 0.00 | 0.93 | |
Ensemble | 1.00 ± 0.00 | 0.00 ± 0.00 | 0.85 ± 0.03 | ND | 1.00 | ND | 0.85 |
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Troisi, J.; Colucci, A.; Cavallo, P.; Richards, S.; Symes, S.; Landolfi, A.; Scala, G.; Maiorino, F.; Califano, A.; Fabiano, M.; et al. A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer. Appl. Sci. 2021, 11, 2835. https://doi.org/10.3390/app11062835
Troisi J, Colucci A, Cavallo P, Richards S, Symes S, Landolfi A, Scala G, Maiorino F, Califano A, Fabiano M, et al. A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer. Applied Sciences. 2021; 11(6):2835. https://doi.org/10.3390/app11062835
Chicago/Turabian StyleTroisi, Jacopo, Angelo Colucci, Pierpaolo Cavallo, Sean Richards, Steven Symes, Annamaria Landolfi, Giovanni Scala, Francesco Maiorino, Alfonso Califano, Marco Fabiano, and et al. 2021. "A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer" Applied Sciences 11, no. 6: 2835. https://doi.org/10.3390/app11062835
APA StyleTroisi, J., Colucci, A., Cavallo, P., Richards, S., Symes, S., Landolfi, A., Scala, G., Maiorino, F., Califano, A., Fabiano, M., Silvestre, G., Mastella, F., Caputo, A., D’Antonio, A., & Altieri, V. (2021). A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer. Applied Sciences, 11(6), 2835. https://doi.org/10.3390/app11062835