Increasing concern is being given to the association between risk of cancer and exposure to low-dose bisphenol A (BPA), especially in young-aged population. In this study, we investigated the effects of repeated oral treatment of low to high dose BPA in juvenile Sprague-Dawley rats. Exposing juvenile rats to BPA (0, 0.5, 5, 50, and 250 mg/kg oral gavage) from post-natal day 9 for 90 days resulted in higher food intakes and increased body weights in biphasic dose-effect relationship. Male mammary glands were atrophied at high dose, which coincided with sexual pre-maturation of females. Notably, proliferative changes with altered cell foci and focal inflammation were observed around bile ducts in the liver of all BPA-dosed groups in males, which achieved statistical significance from 0.5 mg/kg (ANOVA, Dunnett’s test, p<0.05). Toxicokinetic analysis revealed that systemic exposure to BPA was greater at early age (e.g., 210-fold in Cmax, and 26-fold in AUC at 50 mg/kg in male on day 1 over day 90) and in females (e.g., 4-fold in Cmax and 1.6-fold in AUC at 50 mg/kg vs. male on day 1), which might have stemmed from either age- or gender-dependent differences in metabolic capacity. These results may serve as evidence for the association between risk of cancer and exposure to low-dose BPA, especially in young children, as well as for varying toxicity of xenobiotics in different age and gender groups.

This study aimed to apply high-resolution metabolomics to detect compounds that may contribute significantly to prostate cancer (PCa) development. The test population's sera for evaluating the metabolic differences consisted of healthy control (n = 96) and PCa (n = 50) groups. PCa patients were further divided into two groups based on whether their PSA level was >4 (n = 25) or <4 (n = 25). Univariate analysis was performed with the false discovery rate (FDR) at q = 0.05 to determine significantly different metabolites. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) clearly distinguished healthy subjects from PCa groups, while no significant difference was observed in PCa patients with PSA level < 4 or > 4. Mummichog, in combination with the KEGG and MetaboAnalyst, showed that tryptophan metabolism along the kynurenine pathway was most significantly enriched, with −log (p) < 0.05. L-Tryptophan, kynurenine, anthranilate, isophenoxazine, glutaryl-CoA, (S)-3-hydroxybutanoyl-CoA, acetoacetyl-CoA, and acetyl-CoA were upregulated in correlation with the PSA level of PCa patients; in contrast, indoxyl, indolelactate, and indole-3-ethanol, involved in the alternative pathway, were downregulated in the PCa patients. Validation and quantification of potential metabolites by MS/MS further confirmed the disruption of tryptophan, kynurenine, and anthranilate, suggesting that the metabolites of this pathway are potential biomarkers in patients with PCa.

BackgroundThe cancer death rate escalated during 20th century. In South Korea, lung cancer is expected to contribute 12,736 deaths in men, the highest amount among all cancers. Several risk factors may increase the chance to acquiring lung cancer, with mostly related to exogenous compounds found in cigarette smoke and synthetic manufacturing materials. As the mortality rate of lung cancer increases, deeper understanding is necessary to explore risk factors that may lead to this malignancy. In this regard, this study aims to apply high resolution metabolomics (HRM) using LC-MS to detect significant compounds that might contribute in inducing lung cancer and find the correlation of these compounds to the subjects’ smoking habit.MethodsThe comparison was made between healthy control and lung cancer groups for metabolic differences. Further analyses to determine if these differences are related to tobacco-induced lung cancer (past-smoker control vs. past-smoker lung cancer patients (LCPs) and non-smoker control vs. current-smoker LCPs) were selected. The univariate analysis was performed, including a false discovery rate (FDR) of q = 0.05, to determine the significant metabolites between the analyses. Hierarchical clustering analysis (HCA) was done to discriminate metabolites between the control and case subjects. Selected compounds based on significant m/z features of human serum then experienced MS/MS examination, showing that for many m/z, the patterns of ion dissociation matched with standards. Then, the significant metabolites were identified using Metlin database and features were mapped on the human metabolic pathway mapping tool of the Kyoto Encyclopedia of Genes and Genomes (KEGG).ResultsUsing metabolomics-wide association studies, metabolic changes were observed among control group and lung cancer patients. Bisphenol A (211.11, [M + H-H2O]+), retinol (287.23, [M + H]+) and L-proline (116.07, [M + H]+) were among the significant compounds found to have contributed in the discrimination between these groups, suggesting that these compounds might be related in the development of lung cancer. Retinol has been seen to have a correlation with smoking while both bisphenol A and L-proline were found to be unrelated.ConclusionsTwo potential biomarkers, retinol and L-proline, were identified and these findings may create opportunities for the development of new lung cancer diagnostic tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-016-0419-3) contains supplementary material, which is available to authorized users.

The most common type of lung cancer is non-small cell lung cancer (NSCLC), which is frequently characterized by a mutation in the epidermal growth factor receptor (EGFR). Determining the presence of an EGFR mutation in lung cancer is important, as it determines the type of treatment that a patients will receive. Therefore, the aim of the present study was to apply high-resolution metabolomics (HRM) using liquid chromatography-mass spectrometry to identify significant compounds in human plasma samples obtained from South Korean NSCLC patients, as potential biomarkers for providing early detection and diagnosis of minimally-invasive NSCLC. The metabolic differences between lung cancer patients without EGFR mutations were compared with patients harboring EGFR mutations. Univariate analysis was performed, with a false discovery rate of q=0.05, in order to identify significant metabolites between the two groups. In addition, hierarchical clustering analysis was performed to discriminate between the metabolic profiles of the two groups. Furthermore, the significant metabolites were identified and mapped using Mummichog software, in order to generate a potential metabolic network model. Using metabolome-wide association studies, metabolic alterations were identified. Linoleic acid [303.23 m/z, (M+Na)+], 5-methyl tetrahydrofolate [231.10 m/z, (M+2H)+] and N-succinyl-L-glutamate-5 semialdehyde [254.06 m/z, (M+Na)+], were observed to be elevated in patients harboring EGFR mutations, whereas tetradecanoyl carnitine [394.29 m/z, (M+Na)+] was observed to be reduced. This suggests that these compounds may be affected by the EGFR mutation. In conclusion, the present study identified four potential biomarkers in patients with EGFR mutations, using HRM combined with pathway analysis. These results may facilitate the development of novel diagnostic tools for EGFR mutation detection in patients with lung cancer.

Type 2 diabetes mellitus (DM) is a progressive disease and the rate of progression from non-diabetes to DM varies considerably between individuals, ranging from a few months to many years. It is important to understand the mechanisms underlying the progression of diabetes. In the present study, a high-resolution metabolomics (HRM) analysis was performed to detect potential biomarkers and pathways regulating the mode of onset by comparing subjects who developed and did not develop type 2 DM at the second year in a 3-year prospective cohort study. Metabolic profiles correlated with progression to DM were examined. The subjects (n=98) were classified into four groups: Control (did not develop DM for 3 years), DM (diagnosed with DM at the start of the study), DM onset at the third year and DM onset at the second year. The focus was on the comparison of serum samples of the DM groups with onset at the second and third year from the first year, where these two groups had not developed DM, yet. Analyses involved sample examination using liquid chromatography-mass spectrometry-based HRM and multivariate statistical analysis of the data. Metabolic differences were identified across all analyses with the affected pathways involved in metabolism associated with steroid biosynthesis and bile acid biosynthesis. In the first year, higher levels of cholesterol {mass-to charge ratio (m/z) 369.35, (M+H-H2O)+}, 25-hydroxycholesterol [m/z 403.36, (M+H)+], 3α,7α-dihydroxy-5β-cholestane [m/z 443.33, (M+K)+], 4α-methylzymosterol-4-carboxylate [m/z 425.34, (M+H‑H2O)+], and lower levels of 24,25-dihydrolanosterol [m/z 429.40, (M+H)+] were evident in the group with DM onset at the second year compared with those in the group with DM onset at the third year. These results, with a focus on the cholesterol biosynthesis pathway, point to important aspects in the development of DM and may aid in the development of more effective means of treatment and prevention.