Gianluigi Taverna

Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration.

It is well known that gas sensor responses are affected by the presence of humidity in the analyzed gas. This is particularly true when dealing with biological fluid samples, whose high moisture content interferes with the adsorption of the trace volatile organic compounds (VOCs) on the sensors’ active layer. To address this challenge, this study focuses on designing and testing a novel sampling system for the dehumidification of biological fluid headspace to be characterized by an electronic nose (e-Nose). Such a system, based on the use of disposable polymeric sampling bags purged with dry air, exploits the polymers’ permeability to water vapor to reduce sample humidity. Tested materials included Nalophan^TM (20 μm), high-density polyethylene (HDPE, 8, 9, 10 and 11 μm), low-density polyethylene (LDPE, 12 and 50 μm), and biodegradable polyester (Bio-PS, 15 μm). First, dehumidification performance was characterized as a function of dry air flow rate and film type. A purge of 1 L/min accelerated the sample humidity removal compared to passive storage of bags from >2 h to TM dryer. Comparison was based on the e-Nose’s ability to discriminate between pooled vs. spiked samples and reveal real-life metabolomic changes. Nalophan^TM bags and Nafion^TM dryer provided the highest VOC fingerprint to support discrimination by the e-Nose, while Bio-PS provided the fastest sample dehumidification. The proposed bag-based system offers a cost-effective, disposable, and contamination-free solution to humidity interference in e-Noses.

Background and Clinical Significance: Bladder cancer is common, with urothelial carcinoma (UC) comprising most cases in Western countries. Metastases usually involve pelvic structures, lymph nodes, and organs such as the liver, lungs, bones, and adrenal glands. Identifying unusual metastatic sites is critical for accurate diagnosis and treatment planning. Case Presentation: A 65-year-old man with a history of high-grade (G3) UC and carcinoma in situ, previously treated with TURBT, second-look resection, and SWOG-protocol BCG, presented with a new bladder lesion (pT1). Staging CT revealed extravesical spread and a 1.5 cm pancreatic body nodule. EUS-guided biopsy confirmed metastatic UC with concordant immunohistochemistry (GATA3+), excluding primary pancreatic cancer. The patient was referred for systemic therapy with immune checkpoint inhibitors and Enfortumab Vedotin. Conclusions: This case demonstrates the rare occurrence of pancreatic metastasis from bladder UC. EUS-guided biopsy with immunohistochemistry is essential to distinguish secondary lesions from primary pancreatic tumors. Accurate diagnosis is crucial to guide systemic therapy, particularly with emerging immunotherapy and antibody–drug conjugates.

Purpose Vitamin D, a fat-soluble prohormone essential for calcium–phosphate homeostasis and bone health, also regulates innate and adaptive immunity through receptors expressed on B cells, T cells, and antigen-presenting cells capable of synthesizing its active form. Deficiency in vitamin D is linked to dysregulated immune responses and an increased risk of autoimmune diseases and infections, particularly urinary tract infections (UTIs) in both children and adults. Here, we explore 12 machine learning models that utilize urinary 25-hydroxyvitamin D (25(OH)D) levels, urine pH, gender, and age to predict UTIs. Methods A cohort of 358 subjects was analyzed. Demographic, biochemical, and microbiological data were collected for each participant. The dataset was randomly divided into a training set (70%) and an independent test set (30%). Four predictors, age, gender, urine pH, and vitamin D, were included in the analysis. Twelve machine learning models were assessed based on accuracy, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), area under the ROC curve (AUC-ROC), and F1 score. Results A significant difference in urinary 25(OH)D levels was found between individuals with positive (1.33 ± 4.13 ng/mL) and negative (2.48 ± 4.52 ng/mL) urine cultures (p < 0.001). Using urinary 25(OH)D, urine pH, age, and gender as predictors, 12 machine learning models showed accuracies of 64–87%, sensitivities of 59–79%, specificities of 51–95%, PPVs of 61–94%, NPVs of 63–82%, AUC-ROC values of 0.63–0.93, and F1 scores of 0.63–0.86. A stacking machine learning model achieved 88% accuracy, 83% sensitivity, 94% specificity, 93% PPV, 84% NPV, AUC-ROC of 0.93, and an F1 score of 0.88. Conclusion Significant differences in urinary 25(OH)D levels between positive and negative urine cultures confirm the association between low vitamin D levels and UTI occurrence. The developed machine learning models demonstrated high accuracy and represent a promising adjunct for clinicians in UTI diagnosis. With additional validation and assay development, such models may eventually complement conventional culture methods in clinical screening programs. Further external validation using independent datasets, along with prospective studies assessing their impact on antibiotic prescribing practices, is warranted. While these models estimate UTI risk, they do not identify the causative pathogen or determine antibiotic susceptibility.

Electronic noses (E-Noses) equipped with metal-oxide semiconductor (MOS) sensors are promising tools for non-invasive medical diagnostics. Their adoption in clinical practice, however, is limited—among others—by sensor variability across devices, which makes individual calibration necessary. This study presents an approach for the development of a calibration transfer (CT) methodology for urine headspace analysis, involving the design and realization of a dedicated experimental setup and protocol. Partial least squares-discriminant analysis (PLS-DA) models were trained on human urine samples enriched with selected biomarkers to simulate pathological states. Models from a reference (“master”) device were transferred to other (“slave”) units in multiple master–slave configurations using Direct Standardization (DS). To overcome the variability of human urine, synthetic urine recipes were formulated to mimic sensor responses and serve as reproducible transfer samples. Several strategies for selecting transfer samples were evaluated, including the Kennard–Stone algorithm, a DBSCAN-based approach, and random selection. Without CT, classification accuracy on slave devices decreased markedly (37–55%) compared to the master’s performance (79%), whereas applying DS with synthetic standards restored accuracy to 75–80%. These results demonstrate that combining reproducible synthetic standards with DS enables effective model transfer across E-Noses, reducing calibration requirements and supporting their broader applicability in medical diagnostics.

Background: Bladder cancer (BLCA) remains a prevalent and complex malignancy characterized by significant heterogeneity. Treatment strategies are diverse, based on patient characteristics and cancer stage. Early identification of biomarkers is crucial for improving diagnosis, staging, and treatment planning. These biomarkers offer valuable insights into lesion features, tumor differentiation, and disease progression, thereby playing a pivotal role in the personalized management of BLCA. Objective: This study investigated the expression of cancer-testis antigens SPEF1 and SPEF2 in BLCA using comprehensive bioinformatic analyses to assess their potential as biomarkers. Methods: The UALCAN database, based on The Cancer Genome Atlas datasets, was employed to compare SPEF1 and SPEF2 expression levels in normal bladder tissues and BLCA samples. In addition, the Kaplan–Meier Plotter, OncoDB, and TIMER 2.0 platforms were utilized to evaluate the prognostic and immunotherapeutic relevance of these antigens. Results: The findings suggest that SPEF1 and SPEF2 are integral to various biological processes driving BLCA onset and progression. Both genes appear to facilitate BLCA cell progression and migration, contributing to poor prognosis through specific pathways and by altering tumor microenvironment. Notably, SPEF1 expression was significantly upregulated in BLCA tissues compared to normal tissues. Conversely, higher SPEF2 expression was associated with longer overall survival and positively correlated with immunotherapeutic targets. Conclusion: Although these results were derived from in silico analyses, they offer insights into the potential roles of SPEF1 and SPEF2 as biomarkers. Further studies are warranted to validate these biomarkers in retrospective patient cohorts to establish their clinical utility.

Background: Prostate cancer (PCa), is the second most prevalent solid tumor among men worldwide (7.3%), and the leading non-skin cancer in USA where it represents 14.9% of all new cancer cases diagnosed in 2024. This multifactorial disease exhibits substantial variation in incidence and mortality across different ethnic groups and geographic regions. Although prostate-specific antigen (PSA) remains widely used as a biomarker for PCa, its limitations reduce its effectiveness for accurate detection. Consequently, finding molecules that can either complement PSA and other biomarkers is a major goal in PCa research. Methods: Urine samples were collected from healthy donors (n = 5) and patients with low- and high-risk PCa (4 and 7 subjects, respectively) and were analyzed using proteomic data-derived systems and biology approaches. The most promising proteins were further investigated by means of The Cancer Genome Atlas (TCGA) database to assess their associations with clinical and histopathological characteristics in a larger in silico patient population. Results: By evaluating the variations in the urinary proteome as a mirror of the changes occurring in prostate tumor tissue, components of complement and coagulation cascades and glutathione metabolism emerged as hallmarks of low- and high-risk PCa patients, respectively. Moreover, our integrated approach highlighted new potential biomarkers, including CPM, KRT8, ITIH2, and RCN1. Conclusions: The good overlap of our results with what is already reported in the literature supports the new findings in the perspective of improving the knowledge on PCa. Furthermore, they increase the panel of biomarkers that could enhance PCa management. Of course, further investigations on larger patient cohorts are required.

Background: Prostate cancer (PCa) is the second most prevalent solid tumor among men globally and the leading non-skin cancer in American men. In 2020 alone, approximately 1.4 million new cases of prostate cancer were diagnosed worldwide, representing 14.1% of all new cancer cases in men. This multifactorial disease exhibits substantial variation in incidence and mortality across different ethnic groups and geographic regions. Although prostate-specific antigen (PSA) remains widely used as a biomarker for PCa, its limitations reduce its effectiveness for accurate detection. Consequently, finding biomarkers that can either complement or replace PSA is a major goal in PCa research. Methods: Urine samples were collected from healthy individuals (n=5) and patients with low- and high-risk PCa (4 and 7 subjects, respectively) and were analyzed using proteomic data-derived systems biology approaches. The most promising potential biomarkers were further investigated using The Cancer Genome Atlas (TCGA) database to assess their associations with clinical and histopathological characteristics in a larger in silico patient population. Results: In addition to describing the variations of the urinary proteome, the integration of protein profiles, network models and TCGA database has highlighted new potential biomarkers, including CPM, KRT8, ITIH2 and RCN1 that could enhance PCa management. Conclusions: Our findings support the technical feasibility of this combined approach for biomarker discovery and suggest further investigation into specific urinary proteins as potential novel biomarkers in larger patient cohorts.