Renal cell carcinoma (RCC) is the most prevalent and lethal malignancy of the kidney. Despite all the efforts made, no tissue biomarker is currently used in the clinical management of patients with kidney cancer. A search for possible biomarkers in urine for clear cell renal cell carcinoma (ccRCC) has been conducted. Non-targeted metabolomic analyses were performed on paired samples of surgically removed renal cancer and normal tissue, as well as on urine samples. Extracts were analyzed by liquid chromatography/high-resolution mass spectrometry (LC-HRMS). Hydroxybutyrylcarnitine, decanoylcarnitine, propanoylcarnitine, carnitine, dodecanoylcarnitine, and norepinephrine sulfate were found in much higher concentrations in both cancer tissues (compared with the paired normal tissue) and in urine of cancer patients (compared with control urine). In contrast, riboflavin and acetylaspartylglutamate (NAAG) were present at significantly higher concentrations both in normal kidney tissue as well as in urine samples of healthy persons. This preliminary study resulted in the identification of several compounds that may be considered potential clear cell renal carcinoma biomarkers. PLS-DA plot based on LC-MS data for normal and cancer human tissue samples. The aim of this work was the identification of up- and downregulated compounds that could potentially serve as renal cancer biomarkers.

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

Despite the awareness that work in the sewage treatment plant is associated with biological hazards, they have not been fully recognised so far. The research aims to comprehensively evaluate microbiological and toxicological hazards in the air and settled dust in workstations in a sewage treatment plant. The number of microorganisms in the air and settled dust was determined using the culture method and the diversity was evaluated using high-throughput sequencing. Endotoxin concentration was assessed with GC-MS (gas chromatography-mass spectrometry) while secondary metabolites with LC-MS/MS (liquid chromatography coupled to tandem mass spectrometry). Moreover, cytotoxicity of settled dust against a human lung epithelial lung cell line was determined with the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay and UHPLC-Q-ToF-UHRMS (ultra-high-performance liquid chromatography-quadrupole time-of-flight ultrahigh-resolution mass spectrometry) analysis was performed to determine the source of cytotoxicity. The total dust concentration in the sewage treatment plant was low and ranged from 0.030 mg m⁻³ to 0.044 mg m⁻³. The highest microbiological contamination was observed in sludge thickening building and screenings storage. Three secondary metabolites were detected in the air and sixteen in the settled dust. They were dominated by compounds typical of lichen and plants and Aspergillus, Penicillium and Fusarium genera mould. The settled dust from the sludge thickening building revealed high cytotoxicity to human lung epithelial cells A-549 (IC₅₀ = 6.98 after 72 h). This effect can be attributed to a biocidal compound—didecyldimethylammonium chloride (DDAC-C10) and seven toxic compounds: 4-hydroxynonenal, carbofuran, cerulenin, diethylphosphate, fenpropimorph, naphthalene and onchidal. The presence of DDAC-C10 and other biocidal substances in the sewage treatment plant environment may bring negative results for biological sewage treatment and the natural environment in the future and contribute to microorganisms’ increasing antibiotics resistance. Therefore, the concentration of antibiotics, pesticides and disinfectants in sewage treatment plant workstations should be monitored.

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

Infrared (IR) Laser Ablation-remote-Electrospray Ionization (LARESI) platform coupled to a tandem mass spectrometer (MS/MS) operated in selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) modes was developed and employed for targeted imaging of selected metabolites in human kidney cancer tissue. The use of SRM or MRM essentially avoids artifacts in single-compound images that are caused by the inability to filter out chemical noise in full scan MS mode. For comparison purposes, images of the same metabolites were obtained with ultraviolet (UV) desorption/ionization mass spectrometry imaging (UV-LDI-MSI) using monoisotopic silver-109 nanoparticle-enhanced target (109AgNPET) in full-scan MS mode. Tissue samples from patients with renal cell carcinoma (RCC) containing both cancerous and non-cancerous regions were imaged. Sixteen endogenous metabolites that were reported in the literature as varying in abundance between cancerous and non-cancerous areas in various tissues were selected for analysis. They included ten amino acids, four nucleosides and nucleobases, as well as lactate and vitamin E. The obtained MS images vividly illustrate differences in abundances of selected metabolites between cancerous and non-cancerous regions of the kidney tissue. Importantly, the two imaging methods offered similar results. This study demonstrates the applicability of the novel ambient LARESI SRM/MRM MSI method to tissue analyses and its potential for investigating and discovering cancer biomarkers.

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite all the efforts made, no serum-specific biomarker is currently used in the clinical management of patients with this tumor. In this study, comprehensive high-resolution proton nuclear magnetic resonance spectroscopy (¹H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry (¹⁰⁹AgNPET LDI MS) approaches were conducted, in conjunction with multivariate data analysis, to discriminate the global serum metabolic profiles of kidney cancer (n = 50) and healthy volunteers (n = 49). Eight potential biomarkers have been identified using ¹H NMR metabolomics and nine mass spectral features which differed significantly (p < 0.05) between kidney cancer patients and healthy volunteers, as observed by LDI MS. A partial least squares discriminant analysis (OPLS-DA) model generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from cancerous samples (Q² > 0.7), area under the receiver operative characteristic curve (ROC) AUC > 0.96. Compared with healthy human serum, kidney cancer serum had higher levels of glucose and lower levels of choline, glycerol, glycine, lactate, leucine, myo-inositol, and 1-methylhistidine. Analysis of differences between these metabolite levels in patients with different types and grades of kidney cancer was undertaken. Our results, derived from the combination of LDI MS and ¹H NMR methods, suggest that serum biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer.