Prostate cancer (PCa) is the third most frequent cancer in men and prostate-specific antigen is currently the biomarker used despite its low specificity. Lately, extracellular vesicles (EVs) which are secreted by all types of cells have raised research interest for their association with cancer progression. Urinary EVs UEVs) has emerged as a potential biomarker for PCa detection as it is non-invasive and urine samples are easily obtained from patients. Therefore, we hypothesize that PCa cells secrete EVs containing a unique set of biomolecules which can be exploited as a signature profile of the cancer. In this study, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy was used for analysis of the UEVs aiming to obtain a signature spectrum for early detection of PCa. Urine samples from PCa and healthy subjects were subjected to ultracentrifugation for isolation of UEVs. Principal Component Analysis (PCA) indicated that FTIR spectra of the UEVs of PCa patients are distinct from those of healthy individuals at the following wavenumber values: amide I peak (1640 cm-1), RNA ribose peak (1120 cm-1), C-C, C-N stretch peak (967 cm-1) and C4–C5/C=N, imidazole ring peak (1610 cm-1). The obtained IR spectra were also analyzed using Linear Discriminant Analysis (LDA) and the resulting diagnostic classifier for PCa achieved a sensitivity of 83.33% and a specificity of 60%. In conclusion, ATR-FTIR analysis of UEVs in combine with PCA-LDA statistic model described in this study may offer a novel strategy for the development of a non-invasive urine test for early screening of prostate cancer.
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.
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