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. 2024 Feb 21;14(1):4294.
doi: 10.1038/s41598-024-54560-5.

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer

Affiliations

Plasma metabolomic differences in early-onset compared to average-onset colorectal cancer

Thejus Jayakrishnan et al. Sci Rep. .

Abstract

Deleterious effects of environmental exposures may contribute to the rising incidence of early-onset colorectal cancer (eoCRC). We assessed the metabolomic differences between patients with eoCRC, average-onset CRC (aoCRC), and non-CRC controls, to understand pathogenic mechanisms. Patients with stage I-IV CRC and non-CRC controls were categorized based on age ≤ 50 years (eoCRC or young non-CRC controls) or ≥ 60 years (aoCRC or older non-CRC controls). Differential metabolite abundance and metabolic pathway analyses were performed on plasma samples. Multivariate Cox proportional hazards modeling was used for survival analyses. All P values were adjusted for multiple testing (false discovery rate, FDR P < 0.15 considered significant). The study population comprised 170 patients with CRC (66 eoCRC and 104 aoCRC) and 49 non-CRC controls (34 young and 15 older). Citrate was differentially abundant in aoCRC vs. eoCRC in adjusted analysis (Odds Ratio = 21.8, FDR P = 0.04). Metabolic pathways altered in patients with aoCRC versus eoCRC included arginine biosynthesis, FDR P = 0.02; glyoxylate and dicarboxylate metabolism, FDR P = 0.005; citrate cycle, FDR P = 0.04; alanine, aspartate, and glutamate metabolism, FDR P = 0.01; glycine, serine, and threonine metabolism, FDR P = 0.14; and amino-acid t-RNA biosynthesis, FDR P = 0.01. 4-hydroxyhippuric acid was significantly associated with overall survival in all patients with CRC (Hazards ratio, HR = 0.4, 95% CI 0.3-0.7, FDR P = 0.05). We identified several unique metabolic alterations, particularly the significant differential abundance of citrate in aoCRC versus eoCRC. Arginine biosynthesis was the most enriched by the differentially altered metabolites. The findings hold promise in developing strategies for early detection and novel therapies.

Keywords: Arginine biosynthesis pathway; Citric acid cycle; Early onset colorectal cancer; Metabolomics; Pathway analysis; Synthetic lethality; Translational research.

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Conflict of interest statement

Thejus Jayakrishnan – No disclosures Nicole Farha – No disclosures Arshiya Mariam – No disclosures Daniel M. Rotroff – Equity stake in Clarified Precision Medicine, LLC. Received research support from Novo Nordisk, consulting honoraria from Pharmazaam, LLC, and Interpares Biomedicine owns intellectual property related to the detection of type-2 diabetes, chronic liver disease, and liver cancer Federico Aucejo – No disclosures Shimoli V. Barot – No disclosures Madison Conces – No disclosures Kanika G. Nair – No disclosures Smitha S. Krishnamurthi -- Consulting or Advisory Role––Array BioPharma, Research funding- Bristol Myers Squibb, Pfizer, Aravive, Natera Stephanie L. Schmit – No disclosures David Liska – Consulting or Advisory Role- Olympus Medical Systems, Research funding – Merck, Freenome Suneel D. Kamath – Consulting or advisory role: Exelixis, Guardant Health. Tempus, SeaGen, Foundation Medicine. Alok A. Khorana has been paid honoraria - Janssen, Sanofi, BMS, Anthos, Pfizer, Bayer, Nektar, and Medscape. Consulting or advisory role: Janssen, Sanofi, BMS, Anthos, Pfizer, Bayer, Nektar, and Medscape

Figures

Figure 1
Figure 1
Volcano plot representing results of unadjusted and adjusted metabolomic analyses in comparing eoCRC and aoCRC. FDR, false discovery rate; corresponding values are described in Table 1.
Figure 2
Figure 2
Bubble plot of pathway impact analyses. The impact factor topology analysis measures the impact of each metabolite in a pathway on a set of other metabolites in a pathway. Topology analysis was performed on the differentially expressed metabolites to estimate their cumulative impact on the pathways, corresponding values are described in Table 1.
Figure 3
Figure 3
Results for pathway enrichment analyses. Enrichment analyses test whether more significant metabolites are involved in a pathway than expected by chance. The metabolites involved in the pathways are shown in the next figure (Fig. 4). FDR, false discovery rate.
Figure 4
Figure 4
Heatmap outlining the metabolites significantly upregulated or downregulated in patients with eoCRC and aoCRC impacting the corresponding pathways. The heatmap was generated using R package ggplot2, *Indicate significantly altered metabolites. The numbers within parenthesis indicates the fraction of samples that exhibit the specific altered metabolite. eoCRC, early-onset colorectal cancer; aoCRC, average-onset colorectal cancer.
Figure 5
Figure 5
Illustration mapping the metabolic alterations in eoCRC and aoCRC to the interlinked arginine biosynthesis and TCA cycles. Arginine biosynthesis: ADI, arginine deiminase; ASL, argininosuccinate lyase, ASS, argininosuccinate synthetase; NOS, nitric oxide synthase, NO, nitric oxide, ODC, ornithine decarboxylase, OTC, ornithine transcarbamylase. Citrate (TCA) Cycle: CS, citrate synthase, ACLY, adenosine triphosphate citrate lyase, AH, aconitase, IDH, isocitrate dehydrogenase, KGDHC, α-ketoglutarate dehydrogenase complex, SCS, succinyl-CoA synthase; SDH, succinate dehydrogenase; FH, fumarate hydratase; MDH, malate dehydrogenase; GLS, glutaminase; GLUD, glutamate dehydrogenase. eoCRC, Early-Onset Colorectal Cancer; aoCRC, average-onset colorectal cancer.

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