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Background: Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70–80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model. Methods: This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers. Results: The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity. Conclusions: This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study's capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise. [ABSTRACT FROM AUTHOR]

Perfluoroalkyl carboxylic acids (PFCAs) are a significant group of per- and polyfluoroalkyl substances (PFASs). They are persistent organic chemicals manufactured for their resistance to heat, water, and stains. PFCAs are ubiquitous in the environment, particularly in surface water and wastewater, because they are widely used in everyday consumer products. This contamination poses a risk to drinking water supplies and human health, necessitating sensitive and effective analytical methods. Traditional liquid chromatography–tandem mass spectrometry (LC-MS/MS) is commonly used but involves complex sample handling and high costs. In this study, we developed an enhanced stir bar sorptive extraction (SBSE) method coupled with thermal desorption–gas chromatography–mass spectrometry (TD-GC-MS) for the analysis of PFCAs in water. This method demonstrates linearity, with R2 values from 0.9892 to 0.9988, and low limits of detection (LOD) between 21.17 ng/L and 73.96 ng/L. Recovery rates varied from 47 to 97%, suggesting efficient extraction. Compared to traditional methods, the developed SBSE technique requires only a 1 mL sample volume and minimal amounts of solvents, enhancing eco-friendliness and reducing potential contamination and handling errors. This method also demonstrated good precision and robustness across various water matrices. Overall, the developed method offers a precise, eco-friendly, and reliable approach for analyzing PFCAs in diverse water samples. [ABSTRACT FROM AUTHOR]

Early detection of prostate cancer is critical for successful treatment and survival. However, current diagnostic methods such as prostate-specific antigen (PSA) testing and digital rectal examination (DRE) have limitations in accuracy, specificity, and sensitivity. Recent research suggests that urinary volatile organic compounds (VOCs) could serve as potential biomarkers for prostate cancer diagnosis. In this study, urine samples from 337 PCa-positive and 233 PCa-negative patients were collected to develop a diagnosis model. The study involves a high dimensional (HD) classification problem due to the vast number of measured VOCs. Our findings reveal that regularized logistic regression outperforms numerous other classifiers when analyzing the collected data. In particular, we have selected a regularized logistic model with the SCAD (smoothly clipped absolute deviation) penalty as the final model, which attains an AUC (area under the ROC curve) of 0.748, in contrast to a PSA-based AUC of 0.540. These results underscore the potential of VOC-based diagnosis as a clinically feasible approach for PCa screening. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa., Competing Interests: None., (AJCR Copyright © 2024.)