Metabolomic profile of pancreatic cancer cell co-cultures with pancreatic cystic tumour associated-bacterial strains
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ABSTRACT:
ORGANISM(S): Homo sapiens (human) Stenotrophomonas maltophilia Enterococcus faecalis Enterococcus faecium Klebsiella oxytoca Enterobacter cloacae Klebsiella aerogenes Granulicatella adiacens Streptococcus anginosus Citrobacter freundii Klebsiella pneumoniae
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PROVIDER: S-BSST1747 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:BackgroundMetabolomics is the newest -omics methodology and allows for a functional snapshot of the biochemical activity and cellular state. The goal of this study is to characterize metabolomic profiles associated with cancer-related fatigue, a debilitating symptom commonly reported by oncology patients.MethodsUntargeted ultrahigh performance liquid chromatography/mass spectrometry metabolomics approach was used to identify metabolites in plasma samples collected from a total of 197 participants with or without cancer. Partial least squares-discriminant analysis (PLS-DA) was used to identify discriminant metabolite features, and diagnostic performance of selected classifiers was quantified using area under the receiver operating characteristics (AUROC) curve analysis. Pathway enrichment analysis was performed using Fisher's exact test and the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway database.FindingsThe global metabolomics approach yielded a total of 1120 compounds of known identity. Significant metabolic pathways unique to fatigued cancer versus control groups included sphingolipid metabolism, histidine metabolism, and cysteine and methionine metabolism. Significant pathways unique to non-fatigued cancer versus control groups included inositol phosphate metabolism, primary bile acid biosynthesis, ascorbate and aldarate metabolism, starch and sucrose metabolism, and pentose and glucuronate interconversions. Pathways shared between the two comparisons included caffeine metabolism, tyrosine metabolism, steroid hormone biosynthesis, sulfur metabolism, and phenylalanine metabolism.ConclusionsWe found significant metabolomic profile differences associated with cancer-related fatigue. By comparing metabolic signatures unique to fatigued cancer patients with metabolites associated with, but not unique to, fatigued cancer individuals (overlap pathways) and metabolites associated with cancer but not fatigue, we provided a broad view of the metabolic phenotype of cancer-related fatigue.
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