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Renal cancer (RC) represents 3% of all cancers, with a 2% annual increase in incidence worldwide, opening the discussion about the need for screening. However, no established screening tool currently exists for RC. To tackle this issue, we assessed surface-enhanced Raman scattering (SERS) profiling of serum as a liquid biopsy strategy to detect renal cell carcinoma (RCC), the most prevalent histologic subtype of RC. Thus, serum samples were collected from 23 patients with RCC and 27 controls (CTRL) presenting with a benign urological pathology such as lithiasis or benign prostatic hypertrophy. SERS profiling of deproteinized serum yielded SERS band spectra attributed mainly to purine metabolites, which exhibited higher intensities in the RCC group, and Raman bands of carotenoids, which exhibited lower intensities in the RCC group. Principal component analysis (PCA) of the SERS spectra showed a tendency for the unsupervised clustering of the two groups. Next, three machine learning algorithms (random forest, kNN, naïve Bayes) were implemented as supervised classification algorithms for achieving discrimination between the RCC and CTRL groups, yielding an AUC of 0.78 for random forest, 0.78 for kNN, and 0.76 for naïve Bayes (average AUC 0.77 ± 0.01). The present study highlights the potential of SERS liquid biopsy as a diagnostic and screening strategy for RCC. Further studies involving large cohorts and other urologic malignancies as controls are needed to validate the proposed SERS approach.

Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

The aim of this study was to evaluate the effect of the PGE1 analogue, Misoprostol, on gallbladder fasting volume and meal-stimulated emptying. Prostaglandins' effects on the gallbladder were studied principally regarding mucus production during lithogenesis. In the few in vitro and in vivo studies, contradictory results concerning their influence upon gallbladder motility were obtained.13 healthy subjects, 8 females, 5 males, aged 23.4 years (ranges 22-25).Gallbladder volumes were assessed by ultrasound, after measuring the three diameters of the gallbladder in two perpendicular planes, using a conventional 2D equipment and a 3D equipment, after the 3D-reconstruction of the gallbladder. The volumes were calculated by means of the ellipsoid formula. Gallbladder emptying variables (residual volume, ejection fraction, area under emptying curve) were assessed during 90 minutes after a test meal (14 g fat, 425 kcal). Gallbladder emptying was evaluated in each subject on three different days: without prior Misoprostol administration, after 200 mg Misoprostol, and after 400 mg Misoprostol. Misoprostol was given orally as a single dose, 60 minutes before the meal. The two-tailed Student's t test for paired observations was used to compare the results.Misoprostol induced a significant decrease of the gallbladder fasting volume: from 12.8 +/- 4.4 (SD) ml (controls) to 9.1 +/- 3.6 ml (200 mg Misoprostol) and 5.4 +/- 2.6 ml (400 mg Misoprostol). Gallbladder meal-stimulated emptying was not influenced by Misoprostol.Our results indicated that, in healthy subjects, misoprostol induced a dose-dependent gallbladder emptying in the fasting state, but did not influence gallbladder postprandial emptying. Pre-prandial Misoprostol administration might be useful to treat gallbladder stasis in patients with chronic constipation, thus preventing gallstone formation.