Follow-Up Biomarkers in the Evolution of Prostate Cancer, Levels of S100A4 as a Detector in Plasma.

The management and screening of prostate cancer (PC) is still the main problem in clinical practice. In this study, we investigated the role of aggressiveness genetic markers for PC stratification. We analyzed 201 plasma samples from PC patients and controls by digital PCR. For selection and validation, 26 formalin-fixed paraffin-embedded tissues, 12 fresh tissues, and 24 plasma samples were characterized by RNA-Seq, immunochemistry, immunofluorescence, Western blot, and extracellular-vesicles analyses. We identified three novel non-invasive biomarkers; all with an increased expression pattern in patients (PCA3: p = 0.002, S100A4: p ≤ 0.0001 and MRC2: p = 0.005). S100A4 presents the most informative AUC (area under the curve) (0.735). Combination of S100A4, MRC2, and PCA3 increases the discriminatory power between patients and controls and between different more and less aggressive stages (AUC = 0.761, p ≤ 0.0001). However, although a sensitivity of 97.47% in PCA3 and a specificity of 90.32% in S100A4 was reached, the detection signal level could be variable in some analyses owing to tumor heterogeneity. This is the first time that the role of S100A4 and MRC2 has been described in PC aggressiveness. Moreover, the combination of S100A4, MRC2, and PCA3 has never been described as a non-invasive biomarker for PC screening and aggressiveness.

Novel lambda FRET spectral confocal microscopy imaging method.

We report a highly specific, sensitive, and robust method for analyzing fluorescence resonance energy transfer (FRET) based on spectral laser scanning confocal microscopy imaging. The lambda FRET (lambdaFRET) algorithm comprises imaging of a FRET sample at multiple emission wavelengths rendering a FRET spectrum, which is separated into its donor and acceptor components to obtain a pixel-based calculation of FRET efficiency. The method uses a novel off-line precalibration procedure for spectral bleed-through correction based on the acquisition of reference reflection images, which simplifies the method and reduces variability. LambdaFRET method was validated using structurally characterized FRET standards with variable linker lengths and stoichiometries designed for this purpose. LambdaFRET performed better than other well-established methods, such as acceptor photobleaching and sensitized emission-based methods, in terms of specificity, reproducibility, and sensitivity to distance variations. Moreover, lambdaFRET analysis was unaffected by high fluorochrome spectral overlap and cellular autofluorescence. The lambdaFRET method demonstrated outstanding performance in intra- and intermolecular FRET analysis in both fixed and live cell imaging studies.