Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Dec 21;20(1):8.
doi: 10.1007/s11306-023-02067-x.

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

Márcio Felipe Oliveira #  1   2 , Moacir Cavalcante de Albuquerque Neto #  3 , Thiago Siqueira Leite  3 , Paulo André Araújo Alves  3 , Salvador Vilar Correia Lima  3 , Ricardo Oliveira Silva  4
Affiliations

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

Márcio Felipe Oliveira et al. Metabolomics. .

Abstract

Introduction: In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue.

Objectives: Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis.

Methods: We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled.

Results: Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy.

Conclusion: The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

Keywords: Biomarkers; Feature selection; Metabonomics; Overfitting; Prostatic neoplasms; Proton magnetic resonance spectroscopy.

PubMed Disclaimer

References

    1. Calzolari, M. (2022). sklearn-genetic. https://doi.org/10.5281/zenodo.5854662 .
    1. Casadei-Gardini, A., Del Coco, L., Marisi, G., Conti, F., Rovesti, G., Ulivi, P., Canale, M., Frassineti, G. L., Foschi, F. G., Longo, S., Fanizzi, F. P., & Giudetti, A. M. (2020). 1H-NMR based serum metabolomics highlights different specific biomarkers between early and advanced Hepatocellular Carcinoma stages. Cancers, 12(1), 241. https://doi.org/10.3390/cancers12010241 - DOI - PubMed - PMC
    1. Chen, T., & Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939785 - DOI
    1. Diaz, S. O., Barros, A. S., Goodfellow, B. J., Duarte, I. F., Galhano, E., Pita, C., Almeida, M. D. C., Carreira, I. M., & Gil, A. M. (2013). Second trimester maternal urine for the diagnosis of trisomy 21 and prediction of poor pregnancy outcomes. Journal of Proteome Research, 12(6), 2946–2957. https://doi.org/10.1021/pr4002355 . - DOI - PubMed
    1. Gómez-Cebrián, N., Rojas-Benedicto, A., Albors-Vaquer, A., López-Guerrero, J. A., Pineda-Lucena, A., & Puchades-Carrasco, L. (2019). Metabolomics contributions to the discovery of prostate cancer biomarkers. Metabolites. https://doi.org/10.3390/metabo9030048 - DOI - PubMed - PMC

LinkOut - more resources