The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

Abstract

Colorectal cancer (CRC) is a highly prevalent disease with poor prognostic outcomes if not diagnosed in early stages. Current diagnosis techniques are either highly invasive or lack sufficient sensitivity. Thus, identifying diagnostic biomarkers of CRC with high sensitivity and specificity is desirable. Metabolomics represents an analytical profiling technique with great promise in identifying such biomarkers and typically represents a close tie with the phenotype of a specific disease. We thus conducted a systematic review of studies reported from January 2012 to July 2021 relating to the detection of CRC biomarkers through metabolomics to provide a collection of knowledge for future diagnostic development. We identified thirty-seven metabolomics studies characterizing CRC, many of which provided metabolites/metabolic profile-based diagnostic models with high sensitivity and specificity. These studies demonstrated that a great number of metabolites can be differentially regulated in CRC patients compared to healthy controls, adenomatous polyps, or across stages of CRC. Among these metabolite biomarkers, especially dysregulated were certain amino acids, fatty acids, and lysophosphatidylcholines. Additionally, we discussed the contribution of the gut bacterial population to pathogenesis of CRC through their modulation to fecal metabolite pools and summarized the established links in the literature between certain microbial genera and altered metabolite levels in CRC patients. Taken together, we conclude that metabolomics presents itself as a promising and effective method of CRC biomarker detection.

Keywords: GC-MS; LC-MS; colorectal cancers; metabolite biomarker; metabolomics.

Figure 1

PRISMA flow diagram detailing the literature review process. After filtration, 37 relevant articles were included.

Figure 2

(a) Breakdown of the number of studies by specimen type. (b) Breakdown of the number of studies by platform of analysis.

Figure 3

(a) Summary of the areas under the receiver operating curve (AUCs) for CRC diagnostic models created using identified metabolite biomarkers in each reference. References are stratified by specimen type. (b) Sensitivity and Specificity of diagnostic models created using metabolite biomarkers when listed. References are stratified by specimen type. DBS = Dried Blood Spot.

Figure 4

Diagram depicting a metabolite–metabolite interaction network for major metabolites identified to be differentially regulated across studies. Map was created using MetaboAnalyst’s network analysis feature, using a degree filter of 5.0 and betweenness cutoff of 2.0. Color of metabolite represents directionality of difference between CRC and control in literature (Difference was calculated by number of papers identifying metabolite as upregulated—number of papers identifying metabolite as downregulated). Nodes are connected utilizing the KEGG database of metabolic pathways, with larger nodes being implicated in more pathways and thus having more connections. Outline of metabolite represents pathways that are up or downregulated in CRC, analyzed using the KEGG database, with blue-outlined metabolites representing pathways downregulated in CRC and red-outlined metabolites representing pathways upregulated in CRC.

Figure 5

(a) Graphical representation of bacterial genera identified to be differentially regulated in multiple studies over four studies reporting that data [33,37,44,48]. A bar above the x axis indicates upregulation of that bacterial genus in CRC fecal tissue, while a bar below the x axis indicates downregulation. (b) Heatmap demonstrating the identified Pearson correlation of bacterial genus identified as differentially regulated in multiple sources and identified metabolites in five studies [33,34,37,44,48], using stool as the primary specimen. A positive (>0) value on the heatmap implies a positive correlation between bacterial genus and metabolite, while a negative (<0) value implies negative correlation between the genus and that metabolite. A 0 indicates no reported correlation for that metabolite.