Timely diagnosis is crucial to improve the long-term survival of bladder cancer (BC) patients. The discovery of new BC biomarkers based in urine analysis is very attractive because this biofluid is in direct contact with the inner bladder layer, in which most of the neoplasms develop, and is non-invasively collected. Hence, this work aimed to unveil alterations in the urinary volatile profile of patients diagnosed with BC compared with cancer-free individuals, as well as differences among patients diagnosed at different tumor stages, to identify candidate biomarkers for non-invasive BC diagnosis and staging. Urine analysis was performed by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS). The results unveiled that BC patients have a distinct urinary volatile profile characterized by higher levels of several alkanes and aromatic compounds, and lower levels of aldehydes, ketones and monoterpenes. Seventeen significantly altered volatiles were used to evaluate the performance for overall BC detection, disclosing 70% sensitivity, 89% specificity and 80% accuracy. Moreover, distinct urinary volatile profiles were found among patients diagnosed at different tumor stages (Ta/Tis, T1 and ≥T2). This work identified distinct urinary volatile signatures of BC patients with potential for non-invasive detection and staging of bladder cancer.

Background: Decreased renal cell cancer-related mortality is an important societal goal, embodied by efforts to develop effective biomarkers enabling early detection and increasing the likelihood of curative treatment. Herein, we sought to develop a new biomarker for early and minimally invasive detection of renal cell carcinoma (RCC) based on a microRNA panel assessed by ddPCR. Methods: Plasma samples from patients with RCC (n = 124) or oncocytomas (n = 15), and 64 healthy donors, were selected. Hsa-miR-21-5p, hsa-miR-126-3p, hsa-miR-155-5p and hsa-miR-200b-3p levels were evaluated using a ddPCR protocol. Results: RCC patients disclosed significantly higher circulating levels of hsa-miR-155-5p compared to healthy donors, whereas the opposite was observed for hsa-miR-21-5p levels. Furthermore, hsa-miR-21-5p and hsa-miR-155-5p panels detected RCC with high sensitivity (82.66%) and accuracy (71.89%). The hsa-miR-126-3p/hsa-miR-200b-3p panel identified the most common RCC subtype (clear cell, ccRCC) with 74.78% sensitivity. Conclusion: Variable combinations of plasma miR levels assessed by ddPCR enable accurate detection of RCC in general, and of ccRCC. These findings, if confirmed in larger studies, provide evidence for a novel ancillary tool which might aid in early detection of RCC.

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer usually associated with asymptomatic development and risk of systemic progression. Hence, reliable molecular biomarkers of ccRCC are needed to provide early and minimally invasive detection. In this study, urinary volatilome profiling of patients diagnosed with ccRCC (n = 75), and cancer-free controls (n = 75), was performed to investigate the presence of a volatile signature characteristic of ccRCC. Volatile organic compounds (VOCs) in general, and more specifically volatile carbonyl compounds (VCCs), present in urine were extracted by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS–SPME-GC-MS). Supervised multivariate models showed a good discriminatory power of ccRCC patients from controls in urine. Overall, 22 volatile metabolites were found significantly altered between the two groups, including aldehydes, ketones, aromatic hydrocarbons, and terpenoids. A candidate six-biomarker panel, comprising octanal, 3-methylbutanal, benzaldehyde, 2-furaldehyde, 4-heptanone, and p-cresol, depicted the best performance for ccRCC detection with 83% sensitivity, 79% specificity, and 81% accuracy. Moreover, the ccRCC urinary volatilome signature suggested dysregulation of energy metabolism and overexpression of enzymes associated with carcinogenesis. These findings provide the molecular basis for the fine-tuning of gas-sensing materials for application in the development of a bioelectronic sensor.

Testicular germ cell tumors (TGCTs) are the most common cancers in young-adult male patients aged between 15 and 39 years. Hsa-miR-371a-3p is currently the most reliable biomarker for diagnosis and monitoring of these patients non-invasively in liquid biopsies, and it is destined to be introduced in the clinic due to improved performance compared to the classical serum tumor markers available. Current studies have focused on real-time quantitative PCR (RT-qPCR) protocols for its determination; still, some challenges remain, since these protocols often require preamplification steps (costly and time-consuming), and report relative levels normalized to a housekeeping microRNA, not always performed the same way. Droplet digital PCR (ddPCR) shows the promise to overcome these challenges, skipping normalization and preamplifications, but has hardly been explored in the field of TGCTs. In this work, we provide a report of a ddPCR-based pipeline for the quantification of hsa-miR-371a-3p (the DigiMir pipeline) and compare it with two RT-qPCR protocols. A total of 107 plasma samples were investigated in the validation setting. The DigiMir pipeline detected TGCTs in a manner representative of tumor burden, with a sensitivity and specificity of 94% and 100%, respectively, outperforming the combined sensitivity of all three classical serum tumor markers (61.5%). Therefore, in this proof-of-concept investigation, we have shown that the DigiMir pipeline constitutes a new promising methodology to accurately report hsa-miR-371a-3p in the clinical setting.