Targeted UPLC-MS Metabolic Analysis of Human Faeces Reveals Novel Low-Invasive Candidate Markers for Colorectal Cancer

Abstract

Low invasive tests with high sensitivity for colorectal cancer and advanced precancerous lesions will increase adherence rates, and improve clinical outcomes. We have performed an ultra-performance liquid chromatography/time-of-flight mass spectrometry (UPLC-(TOF) MS)-based metabolomics study to identify faecal biomarkers for the detection of patients with advanced neoplasia. A cohort of 80 patients with advanced neoplasia (40 advanced adenomas and 40 colorectal cancers) and 49 healthy subjects were analysed in the study. We evaluated the faecal levels of 105 metabolites including glycerolipids, glycerophospholipids, sterol lipids and sphingolipids. We found 18 metabolites that were significantly altered in patients with advanced neoplasia compared to controls. The combinations of seven metabolites including ChoE(18:1), ChoE(18:2), ChoE(20:4), PE(16:0/18:1), SM(d18:1/23:0), SM(42:3) and TG(54:1), discriminated advanced neoplasia patients from healthy controls. These seven metabolites were employed to construct a predictive model that provides an area under the curve (AUC) median value of 0.821. The inclusion of faecal haemoglobin concentration in the metabolomics signature improved the predictive model to an AUC of 0.885. In silico gene expression analysis of tumour tissue supports our results and puts the differentially expressed metabolites into biological context, showing that glycerolipids and sphingolipids metabolism and GPI-anchor biosynthesis pathways may play a role in tumour progression.

Keywords: biomarkers; colorectal cancer; faecal samples; metabolomics.

Figure 1

PCA scores plot of healthy individuals and patients with advanced neoplasia. (t[1]: R2X = 0.26 and Q2 = 0.22, t[2]: R2X = 0.16 and Q2 = 0.18): CRC and AD patients (n = 80), filled circles; healthy individuals (n = 49), open circles.

Figure 2

Volcano plot representation of metabolic changes in stools from control, CRC and AD sample groups. [log10 (p-value) vs. log2 (fold-change)] for the comparison between healthy individuals and patients with advanced neoplasia (CRC and AD). The shape and colour of the points indicates metabolite family, while the size is determined by the absolute value of the log2 Fold Change (A). Heatmap of metabolites altered in stools from control, CRC and AD sample groups (B).

Figure 3

ROC curve of the predictive model constructed with the seven specified metabolites, including the value of the median AUC (A). Distribution of the model’s features (AUC, sensitivity, specificity and accuracy) obtained from the 10,000 iterations done (B). Distribution of AUC measurements for the combination of our model with age, sex and the age + sex combination (C).

Figure 4

Gene networks of enzymes related with metabolism of stool CRC-altered lipids. Three major pathways could be observed: Sphingolipid and glycerophospholipid metabolisms, and GPI-anchor biosynthesis (A). Gene expression in silico analysis of CRC tumoral tissue. The expression of gene-encoding enzymes involved in the metabolism of stool-altered lipids was analysed in publicly available GEO dataset GSE37364 that compared tumoral versus healthy tissue of the same individual. All displayed genes were highly significant (p-value < 0.001) except PLPP1 (p-value = 0.05) and PIGK (p-value = 0.02) (B).