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.

Advanced urothelial bladder cancer (UBC) patients are tagged by a dismal prognosis and high mortality rates, mostly due to their poor response to standard-of-care platinum-based therapy. Mediators of chemoresistance are not fully elucidated. This work aimed to study the metabolic profile of advanced UBC, in the context of cisplatin resistance. Three isogenic pairs of parental cell lines (T24, HT1376 and KU1919) and the matching cisplatin-resistant (R) sublines were used. A set of functional assays was used to perform a metabolic screening on the cells. In comparison to the parental sublines, a tendency was observed towards an exacerbated glycolytic metabolism in the cisplatin-resistant T24 and HT1376 cells; this glycolytic phenotype was particularly evident for the HT1376/HT1376R pair, for which the cisplatin resistance ratio was higher. HT1376R cells showed decreased basal respiration and oxygen consumption associated with ATP production; in accordance, the extracellular acidification rate was also higher in the resistant subline. Glycolytic rate assay confirmed that these cells presented higher basal glycolysis, with an increase in proton efflux. While the results of real-time metabolomics seem to substantiate the manifestation of the Warburg phenotype in HT1376R cells, a shift towards distinct metabolic pathways involving lactate uptake, lipid biosynthesis and glutamate metabolism occurred with time. On the other hand, KU1919R cells seem to engage in a metabolic rewiring, recovering their preference for oxidative phosphorylation. In conclusion, cisplatin-resistant UBC cells seem to display deep metabolic alterations surpassing the Warburg effect, which likely depend on the molecular signature of each cell line.

Early detection of bladder cancer (BC) remains a major clinical challenge due to the limitations of current diagnostic methods, which are often invasive, expensive, or insufficiently sensitive, particularly for early-stage disease. Metabolomics approaches, when integrated with machine learning (ML) techniques, offer a powerful platform for identifying novel, non-invasive biomarkers. In this study, urinary volatile organic compounds (VOCs) were analysed from 87 BC patients and 90 age- and sex-matched cancer-free controls using headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS). Of the 90 VOCs identified, 27 were selected and used to train five ML algorithms-random forest (RF), support vector machine (SVM), partial least squares-discriminant analysis (PLS-DA), extreme gradient boosting (XGBoost), and k-nearest neighbors (k-NN). Model performance was evaluated using cross-validation and an independent validation set, with metrics including area under the curve (AUC), sensitivity, specificity, and accuracy. RF achieved the highest performance using all 27 features (AUC = 0.913; sensitivity, specificity, and accuracy = 85 %). After feature selection, an eight-VOC panel improved performance on the validation set (AUC = 0.872; sensitivity = 89 %, specificity = 92 %, accuracy = 91 %). The panel included ketones, aldehydes, a short fatty alcohol, and a phenol compound-seven elevated in BC, and one (acetone) decreased. This panel outperformed FDA-approved urinary assays and closely matched the specificity of urine cytology. These findings underscore the promise of VOC-based urinary biomarkers, in combination with ML, for the non-invasive detection of BC. Further large-scale validation studies are essential to confirm diagnostic utility and enable clinical translation.

Bladder cancer (BC) is the ninth most prevalent malignancy worldwide. It remains a significant clinical burden due to high recurrence rates and the need for reliable, non-invasive diagnostic tools. Metabolomics is a powerful strategy for non-invasive cancer detection, with urine representing an ideal biofluid for biomarker discovery, given its direct contact with the urinary tract and its rich diversity of metabolites. This study aimed to identify urinary metabolites showing significant differences in urinary levels between BC patients and controls, and to evaluate their potential for diagnosis and disease monitoring. Beyond identifying metabolites differentiating BC patients from controls, we also assessed whether urinary metabolic patterns could distinguish BC subtypes [non–muscle invasive BC (NMIBC) versus muscle-invasive BC (MIBC)]. Following chemical derivatization, urinary samples were analysed by gas chromatography–mass spectrometry, and the resulting datasets were evaluated using univariate and multivariate statistical approaches. Among the 32 metabolites identified (e.g. amino acids, organic acids, alcohols, and sugar-derivatives), lactate was identified as significantly upregulated in BC versus controls, particularly in MIBC cases. Receiver operating characteristic analysis demonstrated a good performance for overall BC detection and in discriminating between MIBC and NMIBC cases. These results, independent of smoking status and sex, position lactate as a promising non-invasive biomarker for invasive BC.