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. 2021 Jul 31;11(8):591.
doi: 10.3390/membranes11080591.

Detection of Prostate Cancer via IR Spectroscopic Analysis of Urinary Extracellular Vesicles: A Pilot Study

Xin-Le Yap  1 , Bayden Wood  2 , Teng-Aik Ong  3 , Jasmine Lim  3 , Bey-Hing Goh  4   5 , Wai-Leng Lee  1
Affiliations

Detection of Prostate Cancer via IR Spectroscopic Analysis of Urinary Extracellular Vesicles: A Pilot Study

Xin-Le Yap et al. Membranes (Basel). .

Abstract

Extracellular vesicles (EVs) are membranous nanoparticles naturally released from living cells which can be found in all types of body fluids. Recent studies found that cancer cells secreted EVs containing the unique set of biomolecules, which give rise to a distinctive absorbance spectrum representing its cancer type. In this study, we aimed to detect the medium EVs (200-300 nm) from the urine of prostate cancer patients using Fourier transform infrared (FTIR) spectroscopy and determine their association with cancer progression. EVs extracted from 53 urine samples from patients suspected of prostate cancer were analyzed and their FTIR spectra were preprocessed for analysis. Characterization of morphology, particle size and marker proteins confirmed that EVs were successfully isolated from urine samples. Principal component analysis (PCA) of the EV's spectra showed the model could discriminate prostate cancer with a sensitivity of 59% and a specificity of 81%. The area under curve (AUC) of FTIR PCA model for prostate cancer detection in the cases with 4-20 ng/mL PSA was 0.7, while the AUC for PSA alone was 0.437, suggesting the analysis of urinary EVs described in this study may offer a novel strategy for the development of a noninvasive additional test for prostate cancer screening.

Keywords: EVs; FTIR; diagnosis; exosomes; prostate cancer; urine test.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure A1
Figure A1
Principal component analysis of the subsets of EVs preprocessed by Unscrambler X. Score plot of the first and second PCs, with the corresponding percentage of explained variance in parentheses. (C: average spectrum of cancerous samples, NC: average spectrum of non-cancerous samples).
Figure A2
Figure A2
Principal component analysis of the subsets of prostate cancer associated urinary EVs preprocessed by Unscrambler X. Score plot of the first and second PCs, with the corresponding percentage of explained variance in parentheses.
Figure A3
Figure A3
Loading plots of the first and second PCs. (a) Cancerous and non-cancerous groupings. (b) Gleason score groupings.
Figure A4
Figure A4
Weighted regression coefficients generated using partial least squares (PLS) analysis for the major spectral peaks responsible for (a) the cancer discrimination, and (b) the Gleason score discrimination.
Figure A5
Figure A5
Area under the receiver operating characteristic (ROC) curve (AUC). Range of PSA Levels: (a) 4–1200 ng/mL; (b) 4–20 ng/mL; (c) 4–10 ng/mL.
Figure A6
Figure A6
Suggest workflow for FTIR analysis of urinary EVs and derivation of diagnostic classifier for prostate cancer detection using PCA score plot.
Figure A7
Figure A7
Example of prostate cancer classification using FTIR PCA model established using urinary EVs.
Figure 1
Figure 1
Characterization of urinary EVs: (a) transmission electron micrographs; (b) intensity plot for EV size distribution analyzed using Zetasizer; (c) comparison of average diameter between urinary EVs of healthy individuals and prostate cancer patients (Student t-test, n = 15); (d) immunoblot analysis of EV markers in urinary EVs and DU145 cell lysate.
Figure 2
Figure 2
Average raw FTIR spectra of urinary EVs: (a) cancerous vs. non-cancerous EVs; (b) Gleason score groups (GS < 7, GS = 7, and GS > 7).
See this image and copyright information in PMC

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