1,3-Butadiene (BD) is a carcinogenic air pollutant. N-acetyl-S-(4-hydroxy-2-buten-1-yl)-L-cysteine (MHBMA3 or 4HBeMA), an urinary BD metabolite with unspecified configuration, is considered the most sensitive BD biomarker and has been used in routine biomonitoring since 2012. However, two issues remain unaddressed: why its concentrations are unusually high relative to other urinary BD biomarkers and why some authors reported no detection of the biomarker whereas other authors readily quantitated it. To address the issues, we synthesized and structurally characterized the authentic trans- and cis-isomers of MHBMA3 (designated NE and NZ, respectively), developed an isotope-dilution LC-MS/MS method for their quantification, and examined 67 urine samples from barbecue restaurant personnel (n = 47) and hotel administrative staff (n = 20). The restaurant personnel were exposed to barbecue fumes, which contain relatively high concentrations of BD. The results showed that NE and NZ had highly similar NMR spectra, and were difficult to be well separated chromatographically. The NMR data showed that the MHBMA3 isomer investigated in most previous studies was NE. We did not detect NE and NZ in any samples; however, an interfering peak with varying heights was observed in most samples. Notably, under the chromatographic conditions used in the literature, the peak exhibited indistinguishable retention time from that of NE. Thus, it is highly likely that the interfering peak has been mis-identified as NE in previous studies, providing a reasonable explanation for the high MHBMA3 concentration in urine. The contradiction in the presence of MHBMA3 in urine was also caused by the mis-identification, because the researchers who reported the absence of MHBMA3 were actually detecting NZ. Thus, we clarified the confusion on MHBMA3 in previous studies through correctly identifying the two MHBMA3 isomers. The presence of NE and NZ in human urine warrants further investigations.

For IgA nephropathy (IgAN), tubular atrophy/interstitial fibrosis is the most important prognostic pathological indicator in the mesangial and endocapillary hypercellularity, segmental sclerosis, interstitial fibrosis/tubular atrophy, and presence of crescents (MEST-C) score. The identification of non-invasive biomarkers for tubular atrophy/interstitial fibrosis would aid clinical monitoring of IgAN progression and improve patient prognosis.
The study included 188 patients with primary IgAN in separate confirmation and validation cohorts. The associations of miR-92a-3p, miR-425-5p, and miR-185-5p with renal histopathological lesions and prognosis were explored using Spearman correlation analysis and Kaplan-Meier survival curves. Bioinformatics analysis and dual luciferase experiments were used to identify hub genes for miR-185-5p. The fibrotic phenotypes of tubular epithelial cells were evaluated in vivo and in HK-2 cells.
miRNA sequencing and cohort validation revealed that the expression levels of miR-92a-3p, miR-425-5p, and miR-185-5p in urine were significantly increased among patients with IgAN; these levels could predict the extent of tubular atrophy/interstitial fibrosis in such patients. The combination of the three biomarkers resulted in an area under the receiver operating characteristic curve of 0.742. The renal prognosis was significantly worse in the miR-185-5p high expression group than in the low expression group (P=0.003). Renal tissue in situ hybridization, bioinformatics analysis, and dual luciferase experiments confirmed that miR-185-5p affects prognosis in patients with IgAN mainly by influencing expression of the target gene tight junction protein 1 (TJP1) in renal tubular epithelial cells. In vitro experiment revealed that an miR-185-5p mimic could reduce TJP1 expression in HK-2 cells, while increasing the levels of α-smooth muscle actin, fibronectin, collagen I, and collagen III; these changes promoted the transformation of renal tubular epithelial cells to a fibrotic phenotype. An miR-185-5p inhibitor can reverse the fibrotic phenotype in renal tubular epithelial cells. In a unilateral ureteral obstruction model, the inhibition of miR-185-5p expression alleviated tubular atrophy/interstitial fibrosis.
Urinary miR-185-5p, a non-invasive biomarker of tubular atrophy/interstitial fibrosis in IgAN, may promote the transformation of renal tubular epithelial cells to a fibrotic phenotype via TJP1.

BACKGROUND: Previous studies have explored the relationship between serum lead levels and the risk of female breast cancer (FBC). However, it is still uncertain whether urinary lead levels are associated with FBC. This study aimed to investigate the potential association between urinary lead and FBC.
METHODS: A cross-sectional case-control study was conducted using the National Health and Nutrition Examination Survey (NHANES), which is a series of cross-sectional, nationally representative surveys of the United States population consisting of 10 survey waves from 1999 to 2018. This study analyzed a total of 2795 female participants (≥20 years), consisting of 210 participants with FBC and 2585 healthy controls. Urinary lead was detected using Inductively Coupled Plasma-Mass Spectrometry, which was divided into four levels by using quartiles-defining cut points. Multivariate logistic regression was used to analyze the association between urinary lead and FBC.
RESULTS: Multivariate logistic regression revealed that urinary lead was positively correlated with FBC (Odds ratio [OR], 2.16; 95% confidence interval [CI]: [1.18, 3.95], p < 0.05) in a fully adjusted model. There were significantly increased ORs of FBC in quartile 4 (Q4) and quartile 3 (Q3), compared with the lowest quartile 1 (Q1) (Q4, OR = 1.48, 95% CI [0.89, 2.48]; Q3: OR = 1.01, 95% CI [0.59, 1.73], p for trend = 0.021). No significant interaction effects were observed between urinary lead levels and FBC between the subgroups (age, race, educational status, body mass index (BMI), marital status, family income to poverty ratio, hypertension status, diabetes status, renal function status, smoking history, ever been pregnant, oral contraceptive use, occupation classification, etc.) (All interaction p-value > 0.05).
CONCLUSIONS: Urinary lead is likely positively associated with FBC in the US population.

UNASSIGNED: High-grade bladder cancer (HGBC) has a higher malignant potential, recurrence and progression rate compared to low-grade phenotype. Its early symptoms are often vague, making non-invasive diagnosis using urinary biomarkers a promising approach.
UNASSIGNED: The gene expression data from urine samples of patients with HGBC was extracted from the GSE68020 dataset. The clinical information and gene expression data in tumor tissues of HGBC patients were obtained from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis was used to predict the optimal risk model. The protein-protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. Overall survival (OS) was evaluated in the Gene Expression Profiling Interactive Analysis (GEPIA) online platform. Competing endogenous RNA (ceRNA) network was also visualized using Cytoscape. The expression levels of specific genes were assessed through quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Moreover, co-expressed genes and potential biological functions related to specific genes were explored based on the Cancer Cell Line Encyclopedia (CCLE) database.
UNASSIGNED: A total of 560 differentially expressed genes (DEGs) were identified when comparing the urine sediment samples from HGBC patients with the benign ones. Using these urinary DEGs and the clinical information of HGBC patients, we developed an optimal risk model consisting of eight genes to predict the patient outcome. By integrating the node degree values in the PPI network with the expression changes in both urine and tissue samples, eighteen hub genes were selected out. Among them, DKC1 and SNRPG had the most prominent comprehensive values, and EFTUD2, LOR and EBNA1BP2 were relevant to a worse OS in bladder cancer patients. The ceRNA network of hub genes indicated that DKC1 may be directly regulated by miR-150 in HGBC. The upregulation of both SNRPG and DKC1 were detected in HGBC cells, which were also observed in various tumor tissues and malignant cell lines, displaying high correlations with other hub genes.
UNASSIGNED: Our study may provide theoretical basis for the development of effective non-invasive detection and treatment strategies, and further research is necessary to explore the clinical applications of these findings.

High-resolution mass spectrometry is an advanced technique for comprehensive screening of toxic chemicals. In this study, urine samples were collected from both an occupationally exposed population at a coking site and normal inhabitants to identify novel urinary biomarkers for occupational exposure to coking contaminants. A coking-site-appropriate analytical method was developed for unknown chemical screening. Through nontarget screening, 515 differential features were identified, and finally, 32 differential compounds were confirmed as candidates for the current study, including 13 polycyclic aromatic hydrocarbon (PAH) metabolites. Besides monohydroxy-PAHs (such as 1-&2-naphthol, 2-&9-hydroxyfluorene, 2-&4-phenanthrol, and 1-&2-hydroxypyrene), many other PAH metabolites including dihydroxy metabolites, PAH oxide, and sulfate conjugate were detected, suggesting that the quantification based solely on monohydroxy-PAHs significantly underestimated the human exposure to PAHs. Furthermore, several novel compounds were recognized that could be considered as biomarkers for the exposure to coking contaminants, including quinolin-2-ol (1.10 ± 0.44 ng/mL), naphthylmethanols (11.4 ± 5.47 ng/mL), N-hydroxy-1-aminonaphthalene (0.78 ± 0.43 ng/mL), hydroxydibenzofurans (17.4 ± 7.85 ng/mL), hydroxyanthraquinone (0.13 ± 0.053 ng/mL), and hydroxybiphenyl (2.70 ± 1.03 ng/mL). Despite their lower levels compared with hydroxy-PAHs (95.1 ± 30.8 ng/mL), their severe toxicities should not be overlooked. The study provides a nontarget screening approach to identify chemicals in human urine, which is crucial for accurately assessing the health risks of toxic chemicals in the coking industry.

Exposome is the future of next-generation environmental health to establish the association between environmental exposure and diseases. However, due to low concentrations of exposure chemicals, exposome has been hampered by lacking an effective analytical platform to characterize its composition. In this study, by combining the benefit of chemical isotope labeling and pseudo-multiple reaction monitoring (CIL-pseudo-MRM), we have developed one highly sensitive and high-throughput platform (CIL-ExPMRM) by isotope labeling urinary exposure biomarkers. Dansyl chloride (DnsCl), N-methylphenylethylamine (MPEA), and their isotope-labeled forms were used to derivatize polar hydroxyl and carboxyl compounds, respectively. We have programmed a series of scripts to optimize MRM transition parameters, curate the MRM database (>70,000 compounds), predict accurate retention time (RT), and automize dynamic MRMs. This was followed by an automated MRM peak assignment, peak alignment, and statistical analysis. A computational pipeline was eventually incorporated into a user-friendly website interface, named CIL-ExPMRM (http://www.exposomemrm.com/). The performance of this platform has been validated with a relatively low false positive rate (10.7%) across instrumental platforms. CIL-ExPMRM has systematically overcome key bottlenecks of exposome studies to some extent and outperforms previous methods due to its independence of MS/MS availability, accurate RT prediction, and collision energy optimization, as well as the ultrasensitivity and automated robust intensity-based quantification. Overall, CIL-ExPMRM has great potential to advance the exposomic studies based on urinary biomarkers.

Heterocyclic aromatic amines (HAAs) were not only present in cooked foods and cigarette smoke, but also measured in airborne particles and diesel-exhaust particles. Typical HAAs have been reported to induce carcinogenicity and metabolic disturbances, but how these hazardous compounds interfere with metabolic networks by regulating metabolic pathways and fingerprinting signature metabolites as biomarkers remains ambiguous. We developed an advanced strategy that adopted chemical isotope labeling ultrahigh-performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry for urinary nontargeted metabolomics analysis to gain new insight into in vivo physiological responses stimulated by exposure to typical HAAs. Rats were orally administered with a single dose of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) or 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) (1 and 10 mg/kg bw) and their D3-isotopic compounds, respectively, and urine samples were then continuously collected within 36 h. Metabolomics data were acquired and processed by classical multivariate statistical analysis, while urinary metabolites were further identified and characterized according to mass spectrometric fragmentation rules, time- and dose-dependent profiles, and calibration of synthesized standards. We monitored 23 and 37 urinary metabolites as the biotransformation products of PhIP and MeIQx, respectively, and first identified demethylated metabolites of PhIP, tentatively named 2-amino-6-phenylimidazo[4,5-b]pyridine, and dihydroxylation products of classical HAAs as short-term biomarkers of exposure to further unravel the metabolic networks. In addition, our findings revealed that both HAAs significantly disturb histidine metabolism, arginine and proline metabolism, tryptophan metabolism, pyrimidine metabolism, tricarboxylic acid cycle, etc. Furthermore, we found that histamine, methionine, alanine, and 4-guanidinobutanoic acid could be considered potential characteristic biomarkers for the oncogenicity or carcinogenicity of both PhIP and MeIQx and screened their specific key pivotal metabolites. The current metabolomics approach is applicable in mapping updated urinary metabolic fingerprints and identifying potential specific biomarkers for HAAs-induced early tumorigenesis.

Phytoestrogens may have potential effects on hormone-related cancers (HRC) and cancer biomarkers, but the findings have been inconsistent so far. Participants from the National Health and Nutrition Examination Survey 1999-2010 with information on the levels of urinary phytoestrogens, serum cancer biomarkers and cancer history were included. Sampling-weighted logistic regression models examined the association between urinary phytoestrogens concentrations (creatinine-standardised and log-transformed) and HRC, followed by stratified analyses by race/ethnicity, age and menopausal status for different gender. Correlation analyses between phytoestrogens and cancer biomarkers were performed. Of the total 8844 participants, there were 373 with HRC. We observed total isoflavone and enterodiol excretion were positively associated with HRC, especially in non-Hispanic white female subpopulations (Ptrend < 0·05). Similar association also existed in the total isoflavones and enterodiol levels with breast cancer. Whereas the highest concentration of total isoflavones was significantly linked to a reduced prevalence of HRC (OR = 0·40, 95 % CI: 0·19, 0·84) in white males and of prostate cancer (OR = 0·40, 95 % CI: 0·18, 0·86). Among twenty-four participants with HRC, urinary equol concentration was positively correlated with CA15.3. Also, an inverse correlation of total prostate-specific antigens (PSA) and positive correlation of the PSA ratio with urinary enterolactone were detected in thirteen prostate cancer patients. Our findings indicated that higher concentrations of total isoflavones and enterodiol were positively associated with HRC. Urinary certain phytoestrogen excretion may affect serum cancer biomarker levels in cancer patients. But further prospective studies are needed to provide stronger evidence.

UNASSIGNED: The purpose of this study was to conduct a network meta-analysis comparing the diagnostic value of different urinary markers for prostate cancer.
UNASSIGNED: As of June 2022, the literature was retrieved by searching Pubmed, EMBASE, Web of Science databases and other databases. The methodological quality of included studies was assessed using the Cochrane Collaboration\'s risk of bias tool, and publication bias was assessed using funnel plots. The surface under the cumulative ranking curve (SUCRA) values ​​was used to determine the most effective diagnostic method and the data were analyzed accordingly using data analysis software.
UNASSIGNED: A total of 16 articles was included including 9952 patients. The ranking results of network meta-analysis showed that the diagnostic performance of the four urine markers Selectmdx, MIPS, PCA3 and EPI was better than that of PSA. Among them, the specificity, positive predictive value and diagnostic accuracy of Selectmdx ranked first in the SUCRA ranking (SUCRA values: 85.2%, 88.3%, 97.1%), and the sensitivity ranked second in the SUCRA ranking (SUCRA value: 54.4%), and the negative predictive value ranked fourth in SUCRA (SUCRA value: 51.6%). The most sensitive screening tool was MIPS (SUCRA value: 67.1%), and it was also the second screening tool ranked higher in specificity, positive predictive value, negative predictive value and diagnostic accuracy (SUCRA value: 56.5%, respectively)., 57.1%, 67.9%, 74.3%). The high negative predictive value SUCRA ranking is EPI (SUCRA value: 68.0%), its sensitivity ranks third (SUCRA value: 45.6%), and its specificity, positive predictive value and diagnostic accuracy are ranked fourth (SUCRA values are: 45%, 38.2%, 35.8%).
UNASSIGNED: According to the network ranking diagram, we finally concluded that Selectmdx and MIPS can be used as the most suitable urine markers for prostate cancer screening and diagnosis. To further explore the diagnostic value of different urinary markers in the screening of PCa patients.
UNASSIGNED: https://inplasy.com/, identifier INPLASY202290094.

The ubiquitous occurrence of acrylamide in various thermal processing food products poses a potential health risk for the public. An accurate exposure assessment is crucial to the risk evaluation of acrylamide. Machine learning emerging as a powerful computational tool for prediction was employed to establish the association between internal exposure and dietary exposure to acrylamide among a Chinese cohort of middle-aged and elderly population (n = 1,272). Five machine learning regression models were constructed and compared to predict the daily dietary acrylamide exposure based on urinary biomarkers including N-acetyl-S-(2-carbamoylethyl)-L-cysteine (AAMA), N-acetyl-S-(2-carbamoylethyl)-L-cysteine-sulfoxide (AAMA-sul), N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-L-cysteine (GAMA), and N-acetyl-S-(1-carbamoyl-2-hydroxyethyl)-L-cysteine (iso-GAMA). Other important covariates such as age, gender, physical activities, and total energy intake were also considered as predictors in the models. Average dietary intake of acrylamide among Chinese elderly participants was 8.9 μg/day, while average urinary contents of AAMA, AAMA-sul, GAMA, and iso-GAMA were 52.2, 19.1, 4.4, and 1.7 nmol/g Ucr (urine creatinine), respectively. Support vector regression (SVR) model showed the best prediction performance with a R of 0.415, followed by light gradient boosting machine (LightGBM) model (R = 0.396), adjusted multiple linear regression (MLR) model (R = 0.378), neural networks (NN) model (R = 0.365), MLR model (R = 0.363), and extreme gradient boosting (XGBoost) model (R = 0.337). The present study firstly correlated dietary exposure with internal exposure to acrylamide among Chinese elderly population, providing an innovative perspective for the exposure assessment of acrylamide.