Abstract:The incidence of bladder cancer (BCa) has remained high for many years. Nevertheless, its pathomechanism has not yet been fully understood and is still being studied. Therefore, multiplatform untargeted urinary metabolomics analysis has been performed in order to study differences in the metabolic profiles of urine samples collected at three time points: before transurethral resection of bladder tumor (TURBT), the day after the procedure and two weeks after TURBT. Collected samples were analyzed with the use o… Show more
“…Bca is still a worldwide problem in people over the age of 50, and the incidence differs among races, with a lower morbidity but a poor survival rate in African Americans (1). The mortality rates for men and women are similar, and the development risk is four times higher in men than in women (2). The risk factors for Bca include age, race, sex, body mass index (BMI), smoking, pathogen infections, and socioeconomic status.…”
BackgroundBiomarkers of different stages and grades of bladder cancer (BC) are important in clinical work. The objective of our study was to investigate new biomarkers of early-stage BC with liquid chromatography-high resolution mass spectrometry (LC-HRMS) using serum samples.MethodsA total of 215 cases were included in our study, including 109 healthy adults as the control group and 106 non-muscle invasive bladder cancer (NMIBC) patients as the NMIBC group. Serum samples were collected from BC patients in the early stage, called NMIBC, and healthy people before surgery. We used LC-HRMS to distinguish the NMIBC group from the control group and the low-grade NMIBC group from the control group.ResultsAn apparent difference between the NMIBC group and the control group was visualized by unsupervised principal component analysis (PCA). Metabolite panels for 16-hydroxy-10-oxohexadecanoic acid, PGF2a ethanolamide, sulfoglycolithocholate, and threoninyl-alanine were used to distinguish the two groups. The area under the curve (AUC) of the panels was 0.985, and the sensitivity and specificity were 98.63% and 98.59%, respectively. To distinguish the low-grade NMIBC group from the control group, serum metabolic profiling differences between the low-grade NMIBC group and control group samples were also analyzed. Metabolite panels of L-octanoylcarnitine, PGF2a ethanolamide, and threoninyl-alanine showed good discrimination performance. The AUC of the panels was 0.999, and the sensitivity and specificity were 97.8% and 100%, respectively.ConclusionMetabolomics analysis of serum samples can distinguish the NMIBC group from the control group, particularly the early-stage low-grade NMIBC group.
“…Sphingomyelin and lysophosphatidylethanolamine were identified as putative biomarkers in the time series analysis for discriminating between multiple sclerosis patients and healthy individuals [13]. Jacyna, et al [14] investigated the urine metabolic profiles of bladder cancer patients pre-and post-resection. It was reported that hippuric acid, pentanedioic acid and uridine could potentially be used for sample differentiation [14].…”
Section: Introductionmentioning
confidence: 99%
“…Jacyna, et al [14] investigated the urine metabolic profiles of bladder cancer patients pre-and post-resection. It was reported that hippuric acid, pentanedioic acid and uridine could potentially be used for sample differentiation [14]. Huang, et al [15] analyzed the time-series lipidomics data to study the development of hepatocellular carcinoma in a hepatocarcinogenesis rat model.…”
The global threat of COVID-19 has led to an increased use of metabolomics to study SARS-CoV-2 infections in animals and humans. In spite of these efforts, however, understanding the metabolome of SARS-CoV-2 during an infection remains difficult and incomplete. In this study, metabolic responses to a SAS-CoV-2 challenge experiment were studied in nasal washes collected from an asymptomatic ferret model (n = 20) at different time points before and after infection using an LC-MS-based metabolomics approach. A multivariate analysis of the nasal wash metabolome data revealed several statistically significant features. Despite no effects of sex or interaction between sex and time on the time course of SARS-CoV-2 infection, 16 metabolites were significantly different at all time points post-infection. Among these altered metabolites, the relative abundance of taurine was elevated post-infection, which could be an indication of hepatotoxicity, while the accumulation of sialic acids could indicate SARS-CoV-2 invasion. Enrichment analysis identified several pathways influenced by SARS-CoV-2 infection. Of these, sugar, glycan, and amino acid metabolisms were the key altered pathways in the upper respiratory channel during infection. These findings provide some new insights into the progression of SARS-CoV-2 infection in ferrets at the metabolic level, which could be useful for the development of early clinical diagnosis tools and new or repurposed drug therapies.
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