Metabolomic analysis reveals key changes in amino acid metabolism in colorectal cancer patients

Abstract

The number of colorectal cancer (CRC) patients is steadily growing worldwide, particularly in developing nations. Nonetheless, recent advances in early detection studies and therapy alternatives have reduced CRC mortality in affluent countries, despite rising incidence. Gut microbiota and their metabolites may contribute to tumor growth and reduced therapeutic efficacy. This preliminary study sought to uncover metabolic fingerprints in colorectal cancer patients. It also emphasizes the correlation between the gut microbiome, microbial metabolism, and altered metabolites in CRC. In this study, stool samples from 20 CRC patients and matched healthy controls were enrolled. Untargeted metabolomics approach based on an ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-MS/MS) were applied. Statistical approaches, pathway enrichment analysis, and network analysis were employed to unleash CRC perturbed metabolic pathways and putative biomarkers. The study identified a distinct manually curated metabolite profile that is substantially linked to CRC. The steroidogenesis, aspartate, tryptophan (Trp), and urea cycle were the most significant pathways that concurrently contributed to CRC.Prominently, among other pathways, Trp metabolism was identified as a critical pathway, indicating a possible connection between the development of CRC and gut microbiota. In a nutshell the notable resulted metabolites reveal auspicious biomarkers for the initial diagnosis as well as surveilling of CRC progression. This preliminary study highlights the potential involvement that gut bacteria may contribute in CRC patients. Further investigation into the composition of the gut microbiome associated with this metabolic profile may lead to the identification of novel biomarkers for early detection and possible targets for treatment.

Keywords: Colorectal cancer; Gut microbiota; LC–MS; Metabolomics; Microbial tryptophan metabolism.

Fig. 1

Bioinformatics analysis for the fecal metabolomics profile of the CRC cohort compared to healthy controls. a, b Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) for the metabolomics profile, respectively. Metabolite abundances were auto-scaled before the analysis. c Variable importance in projection (VIP) score plot for the CRC and healthy control cohorts displays the top 15 most important metabolite features identified by PLS-DA. Colored boxes on right indicate relative concentration of corresponding metabolite for the CRC and healthy controls. The red highlighted metabolite features are significantly down-regulated DEMs (FDR ≤ 0.05, log2 (FC) ≥ 1.5). d Volcano plot showing the log2 (fold change) between the CRC and healthy controls (y-axis), and – log10 (FDR) calculated by Wilcoxn rank-sum test to show the significant DEMs. The blue color represents significant down-regulated metabolites, while the red color represents significant up-regulated metabolites (FDR ≤ 0.05, log2 (FC) ≥ 1.5). e Hierarchical cluster analysis (HCA) heatmap plot for the profiled metabolites between the CRC and healthy control groups. f Hierarchical cluster analysis (HCA) heatmap plot highlights the significant DEMs (FDR ≤ 0.05, log₂ (FC) ≥ 1.5). g Box plot representation for the significant DEMs (FDR ≤ 0.05, log₂ (FC) ≥ 1.5)

Fig. 2

Pathway enrichment analysis for the metabolomics profile using Metaboanalyst 6.0. a Quantitative enrichment analysis (MSEA) for the whole profile, the pathway name (x-axis) and the –log10 FDR (y-axis). All the represented pathways had passed FDR cut-off (FDR > 0.05). The color of the dot corresponds to the value of FDR, while the size of the dot corresponds to the ratio of hit metabolites per total metabolites in the pathway. b Sankey diagram linking the pathway (on the left side) to the hits/metabolites (on the right side). The red highlighted metabolite features are significantly down-regulated DEMs, while the green highlighted metabolite features are significantly up-regulated DEMs (FDR > 0.05)

Fig. 3

Network analysis for the fecal metabolomics profile of CRC and healthy controls. a Cluster dendrogram of metabolite features determined from the WGCNA R package. Metabolites were clustered based on the average linkage calculation from the dissimilarity of topology overlap matrix (TOM). A total of 216 metabolites were assigned to four modules (turquoise, blue, brown, and grey). The assigned modules color are represented at the bottom of the graph. b Module-condition correlation between the assigned modules, CRC, and control groups. Each row corresponds to a module eigenmetabolite and each column to a condition/group. Each cell contains the corresponding correlation and p-value. The cells are color-coded by the correlation coefficient value according to the color legend. c Metscape network for the MEblue module eigenmetabolites and the uniquely identified metabolites in CRC group. d Metscape network for the significant DEMs, showing only the direct interaction between L-Argininosuccinic acid and Aspartic Acid. The dark red metabolites corresponding to metabolites in the input of the analysis, while light red metabolites corresponding to intermediate metabolites (not in the input of the analysis)