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

Abstract

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.

Trial registration: ClinicalTrials.gov NCT02328677.

Keywords: adipose tissue; colorectal cancer; inflammation; metabolomics; obesity; visceral adiposity.

FIGURE 1

Biochemical and structural similarity network displaying metabolic differences between SAT and VAT. Network nodes representing metabolites (shape displaying the biochemical domain) are connected based on biochemical (precursor to product, blue lines) or structural similarity (Tanimoto > 0.07, gray lines) or complex lipid class–specific relations (gray lines). The direction of the metabolic differences in VAT relative to SAT tissue (green, lower in VAT; red, higher in VAT, gray; inconclusive or FDR-adjusted P ≥ 0.05) is defined based on the number of matched samples (≥30) displaying the trend. The multivariate importance of the metabolomic differences for discriminating between VAT and SAT tissues is displayed based on the color brightness, which encodes the quartile for the partial least squares discriminant analysis VIP, with thick black borders displaying species with the top 5% VIP values. Node size is proportional to the number of higher/lower pairs (30 times out of 59 pairs lower/higher = small size, 59 times or 0 times higher/lower = big size). Cer, ceramide; DG, diacylglycerol; FDR, false discovery rate; MG, monoacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; SAT, subcutaneous adipose tissue; SM, sphingomyelin; TG, triacylglycerol; VAT, visceral adipose tissue; VIP, variable importance in projection.

FIGURE 2

Association between the adipose tissue metabolome with tumor stage and BMI. (A) Box-whisker plots for normalized peak heights of unknown lipid species in VAT for which a trend was observed with increasing tumor stage. P values are based on Kendall’s τ correlation with 1000-fold permutation analysis of the stage variable to account for ties. Detected lipids are marked by accurate mass and chromatographic retention time. (B) Positive association between the ratio of triglycerides/membrane lipids and patients’ BMI in both SAT (left) and VAT (right) indicating (C) increased adipocyte diameter with increasing BMI. (D) Representative microscopic images of hematoxylin and eosin–stained cryosections of VAT from a patient with a high triglyceride/membrane lipid ratio (left) and a low triglyceride/membrane lipid ratio (right). (E) Box-whisker plot representing the adipocyte area in those patients with the top 5 highest triglyceride/membrane lipid ratios compared with those patients with the lowest 5 triglyceride/membrane lipid ratios; P value derived from Wilcoxon signed rank test. SAT, subcutaneous adipose tissue; TG, triacylglycerol; VAT, visceral adipose tissue.

FIGURE 3

Biochemical network of arachidonic acid metabolism. Combined transcriptomic and metabolic network analysis of differences in arachidonic acid metabolism between VAT and SAT adipose tissues. Metabolites are illustrated as circles; genes are illustrated as squares. Significant (FDR <0.05) members of the network are colored red or green (higher or lower in VAT, respectively). Node size is proportional to fold change. No oxylipins, prostaglandins, or leukotrienes were measured in this study, but orange indicates a predicted alteration in abundances in VAT based on the combined measured metabolic network. Blue indicates a group of multiple metabolites being accepted as substrate/product. FDR, false discovery rate; LPE, lysophosphatidylethanolamine; PE, phosphatidylethanolamine; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue.