Metabolomics and transcriptomics identify pathway differences between visceral and subcutaneous adipose tissue in colorectal cancer patients: the ColoCare study.

BACKGROUND Metabolic and transcriptomic differences between visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) compartments, particularly in the context of obesity, may play a role in colorectal carcinogenesis. We investigated the differential functions of their metabolic compositions. OBJECTIVES Biochemical differences between adipose tissues (VAT compared with SAT) in patients with colorectal carcinoma (CRC) were investigated by using mass spectrometry metabolomics and gene expression profiling. Metabolite compositions were compared between VAT, SAT, and serum metabolites. The relation between patients' tumor stage and metabolic profiles was assessed. DESIGN Presurgery blood and paired VAT and SAT samples during tumor surgery were obtained from 59 CRC patients (tumor stages I-IV) of the ColoCare cohort. Gas chromatography time-of-flight mass spectrometry and liquid chromatography quadrupole time-of-flight mass spectrometry were used to measure 1065 metabolites in adipose tissue (333 identified compounds) and 1810 metabolites in serum (467 identified compounds). Adipose tissue gene expression was measured by using Illumina's HumanHT-12 Expression BeadChips. RESULTS Compared with SAT, VAT displayed elevated markers of inflammatory lipid metabolism, free arachidonic acid, phospholipases (PLA2G10), and prostaglandin synthesis-related enzymes (PTGD/PTGS2S). Plasmalogen concentrations were lower in VAT than in SAT, which was supported by lower gene expression of FAR1, the rate-limiting enzyme for ether-lipid synthesis in VAT. Serum sphingomyelin concentrations were inversely correlated (P = 0.0001) with SAT adipose triglycerides. Logistic regression identified lipids in patients' adipose tissues, which were associated with CRC tumor stage. CONCLUSIONS As one of the first studies, we comprehensively assessed differences in metabolic, lipidomic, and transcriptomic profiles between paired human VAT and SAT and their association with CRC tumor stage. We identified markers of inflammation in VAT, which supports prior evidence regarding the role of visceral adiposity and cancer.

[1]  Andrew B. Nobel,et al.  Significance analysis of functional categories in gene expression studies: a structured permutation approach , 2005, Bioinform..

[2]  Oliver Fiehn,et al.  A comprehensive urinary metabolomic approach for identifying kidney cancerr. , 2007, Analytical biochemistry.

[3]  A. Moser,et al.  Functions of plasmalogen lipids in health and disease. , 2012, Biochimica et biophysica acta.

[4]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[5]  G. Schmitz,et al.  Plasmalogens the neglected regulatory and scavenging lipid species. , 2011, Chemistry and physics of lipids.

[6]  Oliver Fiehn,et al.  Quality control for plant metabolomics: reporting MSI-compliant studies. , 2008, The Plant journal : for cell and molecular biology.

[7]  M. IbrahimM.,et al.  Subcutaneous and visceral adipose tissue: structural and functional differences , 2010 .

[8]  Udo Hoffmann,et al.  Abdominal Visceral and Subcutaneous Adipose Tissue Compartments: Association With Metabolic Risk Factors in the Framingham Heart Study , 2007, Circulation.

[9]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[10]  C. Ulrich,et al.  The effects of obesity and obesity-related conditions on colorectal cancer prognosis. , 2010, Cancer control : journal of the Moffitt Cancer Center.

[11]  Jacqueline Capeau,et al.  Recent advances in the relationship between obesity, inflammation, and insulin resistance. , 2006, European cytokine network.

[12]  Scarlet F. Brockmöller,et al.  Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery , 2012, Genome Medicine.

[13]  L. Basso,et al.  Adipose tissue fatty acid composition and colon cancer: a case–control study , 2014, European Journal of Nutrition.

[14]  D. Piomelli.  Arachidonic Acid in Cell Signaling , 1993, Molecular Biology Intelligence Unit.

[15]  F. Wright,et al.  Significance Analysis of Function and Expression , 2005 .

[16]  Susumu Goto,et al.  KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..

[17]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[18]  A. Greenberg,et al.  Obesity and the role of adipose tissue in inflammation and metabolism. , 2006, The American journal of clinical nutrition.

[19]  Shuangjie Wu,et al.  Association of obesity and overweight with overall survival in colorectal cancer patients: a meta-analysis of 29 studies , 2014, Cancer Causes & Control.

[20]  Matej Oresic,et al.  MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data , 2010, BMC Bioinformatics.

[21]  O. Fiehn,et al.  Diabetes associated metabolomic perturbations in NOD mice , 2014, Metabolomics.

[22]  Martin Scholz,et al.  Pacific Symposium on Biocomputing 12:169-180(2007) SETUP X – A PUBLIC STUDY DESIGN DATABASE FOR METABOLOMIC PROJECTS , 2022 .

[23]  Augustin Scalbert,et al.  Review of Mass Spectrometry–Based Metabolomics in Cancer Research , 2013, Cancer Epidemiology, Biomarkers & Prevention.

[24]  F. Visseren,et al.  Obesity and Cancer: The Role of Dysfunctional Adipose Tissue , 2009, Cancer Epidemiology, Biomarkers & Prevention.

[25]  Martin Scholz,et al.  Setup and Annotation of Metabolomic Experiments by Integrating Biological and Mass Spectrometric Metadata , 2005, DILS.

[26]  Oliver Fiehn,et al.  Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry. , 2008, Trends in analytical chemistry : TRAC.

[27]  P. Arner,et al.  Lipolysis during abdominal surgery. , 1994, The Journal of clinical endocrinology and metabolism.

[28]  David S. Wishart,et al.  MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis , 2012, Nucleic Acids Res..

[29]  Pan Du,et al.  lumi: a pipeline for processing Illumina microarray , 2008, Bioinform..

[30]  O. Fiehn,et al.  Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co‐regulation in biochemical networks , 2004, Proteomics.

[31]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[32]  Y. Fujiki,et al.  Posttranslational Regulation of Fatty Acyl-CoA Reductase 1, Far1, Controls Ether Glycerophospholipid Synthesis*♦ , 2010, The Journal of Biological Chemistry.

[33]  J. Eberwine,et al.  Analysis of gene expression in single live neurons. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[34]  R. Busse,et al.  Macrophages in human visceral adipose tissue: increased accumulation in obesity and a source of resistin and visfatin , 2006, Diabetologia.

[35]  Yair Lotan,et al.  Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. , 2011, Cancer research.

[36]  Caroline,et al.  Patterns of Abdominal Fat Distribution: The Framingham Heart Study , 2008 .