METHODS: The case-control portion of the study was conducted in nine UK centers with men ages 50-69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium.
RESULTS: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal.
CONCLUSIONS: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk.
IMPACT: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.
METHODS: The internalization of type II FIPV WSU 79-1146 in Crandell-Rees Feline Kidney (CrFK) cells was visualized using a fluorescence microscope, and optimization prior to phenotype microarray (PM) study was performed. Then, four types of Biolog Phenotype MicroArray™ plates (PM-M1 to PM-M4) precoated with different carbon and nitrogen sources were used to determine the metabolic profiles in FIPV-infected cells.
RESULTS: The utilization of palatinose was significantly low in FIPV-infected cells; however, there were significant increases in utilizing melibionic acid, L-glutamine, L-glutamic acid and alanyl-glutamine (Ala-Gln) compared to non-infected cells.
CONCLUSION: This study has provided the first insights into the metabolic profiling of a feline coronavirus infection in vitro using PMs and deduced that glutamine metabolism is one of the essential metabolic pathways for FIPV infection and replication. Further studies are necessary to develop strategies to target the glutamine metabolic pathway in FIPV infection.
AIM OF THE STUDY: This study aimed to investigate the effect of ionic liquid-Graviola fruit pulp extract (IL-GPE) on the metabolomics behavior of colon cancer (HT29) by using an untargeted GC-TOFMS-based metabolic profiling.
MATERIALS AND METHODS: Multivariate data analysis was used to determine the metabolic profiling, and the ingenuity pathway analysis (IPA) was used to predict the altered canonical pathways after treating the HT29 cells with crude IL-GPE and Taxol (positive control).
RESULTS: The principal components analysis (PCA) identified 44 metabolites with the most reliable factor loading, and the cluster analysis (CA) separated three groups of metabolites: metabolites specific to the non-treated HT29 cells, metabolites specific to the treated HT29 cells with the crude IL-GPE and metabolites specific to Taxol treatment. Pathway analysis of metabolomic profiles revealed an alteration of many metabolic pathways, including amino acid metabolism, aerobic glycolysis, urea cycle and ketone bodies metabolism that contribute to energy metabolism and cancer cell proliferation.
CONCLUSION: The crude IL-GPE can be one of the promising anticancer agents due to its selective inhibition of energy metabolism and cancer cell proliferation.