Pre-Diagnostic Circulating Metabolites and Colorectal Cancer Risk in the Cancer Prevention Study-II Nutrition Cohort

Metabolomic signatures of inflammation and metabolic dysregulation in relation to colorectal cancer risk

BACKGROUND

Inflammation and metabolic dysregulation are associated with increased risk of colorectal cancer (CRC); the underlying mechanisms are not fully understood. We characterized metabolomic signatures of inflammation and metabolic dysregulation and evaluated the association of the signatures and individual metabolites with CRC risk.

METHODS

Among 684 incident CRC cases and 684 age-matched controls in the Nurses' Health Study (n = 818 women) and Health Professionals Follow-up Study (n = 550 men), we applied reduced rank and elastic net regression to 277 metabolites for markers of inflammation (C-reactive protein, interleukin 6, tumor necrosis factor receptor superfamily member 1B, and growth differentiation factor 15) or metabolic dysregulation (body mass index, waist circumference, C-peptide, and adiponectin) to derive metabolomic signatures. We evaluated the association of the signatures and individual metabolites with CRC using multivariable conditional logistic regression. All statistical tests were 2-sided.

RESULTS

We derived a signature of 100 metabolites that explained 24% of variation in markers of inflammation and a signature of 73 metabolites that explained 27% of variation in markers of metabolic dysregulation. Among men, both signatures were associated with CRC (odds ratio [OR] = 1.34, 95% confidence interval [CI] = 1.07 to 1.68 per 1-standard deviation increase, inflammation; OR = 1.25, 95% CI = 1.00 to 1.55 metabolic dysregulation); neither signature was associated with CRC in women. A total of 11 metabolites were individually associated with CRC and biomarkers of inflammation or metabolic dysregulation among either men or women.

CONCLUSION

We derived metabolomic signatures and identified individual metabolites associated with inflammation, metabolic dysregulation, and CRC, highlighting several metabolites as promising candidates involved in the inflammatory and metabolic dysregulation pathways for CRC incidence.

Colorectal Cancer Detection via Metabolites and Machine Learning

Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.

Association between pre-diagnostic circulating lipid metabolites and colorectal cancer risk: a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC)

BACKGROUND

Altered lipid metabolism is a hallmark of cancer development. However, the role of specific lipid metabolites in colorectal cancer development is uncertain.

METHODS

In a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined associations between pre-diagnostic circulating concentrations of 97 lipid metabolites (acylcarnitines, glycerophospholipids and sphingolipids) and colorectal cancer risk. Circulating lipids were measured using targeted mass spectrometry in 1591 incident colorectal cancer cases (55% women) and 1591 matched controls. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between concentrations of individual lipid metabolites and metabolite patterns with colorectal cancer risk.

FINDINGS

Of the 97 assayed lipids, 24 were inversely associated (nominally p < 0.05) with colorectal cancer risk. Hydroxysphingomyelin (SM (OH)) C22:2 (ORper doubling 0.60, 95% CI 0.47-0.77) and acylakyl-phosphatidylcholine (PC ae) C34:3 (ORper doubling 0.71, 95% CI 0.59-0.87) remained associated after multiple comparisons correction. These associations were unaltered after excluding the first 5 years of follow-up after blood collection and were consistent according to sex, age at diagnosis, BMI, and colorectal subsite. Two lipid patterns, one including 26 phosphatidylcholines and all sphingolipids, and another 30 phosphatidylcholines, were weakly inversely associated with colorectal cancer.

INTERPRETATION

Elevated pre-diagnostic circulating levels of SM (OH) C22:2 and PC ae C34:3 and lipid patterns including phosphatidylcholines and sphingolipids were associated with lower colorectal cancer risk. This study may provide insight into potential links between specific lipids and colorectal cancer development. Additional prospective studies are needed to validate the observed associations.

FUNDING

World Cancer Research Fund (reference: 2013/1002); European Commission (FP7: BBMRI-LPC; reference: 313010).

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

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.

Dietary patterns, untargeted metabolite profiles and their association with colorectal cancer risk

We investigated data-driven and hypothesis-driven dietary patterns and their association to plasma metabolite profiles and subsequent colorectal cancer (CRC) risk in 680 CRC cases and individually matched controls. Dietary patterns were identified from combined exploratory/confirmatory factor analysis. We assessed association to LC-MS metabolic profiles by random forest regression and to CRC risk by multivariable conditional logistic regression. Principal component analysis was used on metabolite features selected to reflect dietary exposures. Component scores were associated to CRC risk and dietary exposures using partial Spearman correlation. We identified 12 data-driven dietary patterns, of which a breakfast food pattern showed an inverse association with CRC risk (OR per standard deviation increase 0.89, 95% CI 0.80-1.00, p = 0.04). This pattern was also inversely associated with risk of distal colon cancer (0.75, 0.61-0.96, p = 0.01) and was more pronounced in women (0.69, 0.49-0.96, p = 0.03). Associations between meat, fast-food, fruit soup/rice patterns and CRC risk were modified by tumor location in women. Alcohol as well as fruit and vegetables associated with metabolite profiles (Q2 0.22 and 0.26, respectively). One metabolite reflecting alcohol intake associated with increased CRC risk, whereas three metabolites reflecting fiber, wholegrain, and fruit and vegetables associated with decreased CRC risk.

Correlation between pre-treatment serum total blood bilirubin and unconjugated bilirubin and prognosis in patients with colorectal cancer

BACKGROUND

Epidemiological studies have found that unconjugated bilirubin (UCB) levels are positively correlated with the incidence of colorectal cancer (CRC). Therefore, bilirubin may also play an important role in the prognosis of CRC.

AIM

To investigate the predictive value of total bilirubin (TBIL) and UCB in the prognosis of patients with CRC.

METHODS

A total of 142 CRC patients were selected as the research subjects in Jingxian Hospital, from October 2014 to May 2021. General and tumour-related clinical data at admission and the overall survival at 3 years after surgery were collected. The optimal cut-off values of TBIL and UCB were determined by receiver operating characteristic curve analysis. Univariate and multivariate Cox regression were used to analyse the effect of bilirubin level on the survival of CRC patients. The Kaplan-Meier method was used to assess the survival time.

RESULTS

The 3-year overall survival rate of CRC patients was significantly higher in the high TBIL (> 13.45 μmol/L) group than in the low TBIL (≤ 13.45 μmol/L) group (76.4% vs 37.1%; P < 0.05). The 3-year overall survival rate of CRC patients in the high UCB (> 10.75 μmol/L) group was significantly higher than that in the low UCB (≤ 10.75 μmol/L) group (83.3% vs 34.2%; P < 0.05). Multivariate Cox regression analysis showed that higher TBIL levels were an independent predictor of better prognosis in CRC patients (hazard ratio = 0.360, 95% confidence interval: 0.159-0.812, P = 0.014).

CONCLUSION

TBIL levels can be used as a prognostic indicator for CRC patients.

Untargeted plasma metabolomics and risk of colorectal cancer-an analysis nested within a large-scale prospective cohort

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, but if discovered at an early stage, the survival rate is high. The aim of this study was to identify novel markers predictive of future CRC risk using untargeted metabolomics.

METHODS

This study included prospectively collected plasma samples from 902 CRC cases and 902 matched cancer-free control participants from the population-based Northern Sweden Health and Disease Study (NSHDS), which were obtained up to 26 years prior to CRC diagnosis. Using reverse-phase liquid chromatography-mass spectrometry (LC-MS), data comprising 5015 metabolic features were obtained. Conditional logistic regression was applied to identify potentially important metabolic features associated with CRC risk. In addition, we investigated if previously reported metabolite biomarkers of CRC risk could be validated in this study population.

RESULTS

In the univariable analysis, seven metabolic features were associated with CRC risk (using a false discovery rate cutoff of 0.25). Two of these could be annotated, one as pyroglutamic acid (odds ratio per one standard deviation increase = 0.79, 95% confidence interval, 0.70-0.89) and another as hydroxytigecycline (odds ratio per one standard deviation increase = 0.77, 95% confidence interval, 0.67-0.89). Associations with CRC risk were also found for six previously reported metabolic biomarkers of prevalent and/or incident CRC: sebacic acid (inverse association) and L-tryptophan, 3-hydroxybutyric acid, 9,12,13-TriHOME, valine, and 13-OxoODE (positive associations).

CONCLUSIONS

These findings suggest that although the circulating metabolome may provide new etiological insights into the underlying causes of CRC development, its potential application for the identification of individuals at higher risk of developing CRC is limited.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Interaction among biofilter microbiome, fecal metabolome and water quality and regulation of sewage discharge in the recirculating aquaculture system of Apostichopus japonicus

With the aggravation of environmental pollution caused by traditional culture of Apostichopus japonicus, the concept of A. japonicus recirculating aquaculture system (RAS) came into being. To plan the sewage discharge time reasonably, we explored the temporal variation of water quality, biofilter microbe and fecal metabolome in RAS and relevant mechanism. The results showed that monitored water quality in RAS were within the safe living range of A. japonicus. Proteobacteria and Desulfobacterota were dominant bacteria in biofilter. The RDA results and correlation heatmap showed that NH₄-N and NO₂-N significantly affected the microbial community composition. The expression pattern of fecal metabolites changed with the passage of time after feeding. And ROC curve analysis and VIP bar chart showed that there were inter group biomarkers with predictive performance, which could help to remind timely sewage discharge. Topological analysis of KEGG pathway enrichment showed that metabolic pathways such as alanine, aspartate and glutamate metabolism changed significantly after feeding (P < 0.01). Additionally, the correlation analysis results showed that biofilter microbe and fecal metabolites were related to water quality (P < 0.05). Combined with the above research results, this study concluded that the RAS could discharge sewage 25-30 h after feeding. These findings were of direct significance to the management of RAS environment and the protection of A. japonicus healthy growth.

Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts

BACKGROUND

Amino acid metabolism is dysregulated in colorectal cancer patients; however, it is not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk of colorectal cancer. We investigated circulating levels of amino acids in relation to colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts.

METHODS

Concentrations of 13-21 amino acids were determined in baseline fasting plasma or serum samples in 654 incident colorectal cancer cases and 654 matched controls in EPIC. Amino acids associated with colorectal cancer risk following adjustment for the false discovery rate (FDR) were then tested for associations in the UK Biobank, for which measurements of 9 amino acids were available in 111,323 participants, of which 1221 were incident colorectal cancer cases.

RESULTS

Histidine levels were inversely associated with colorectal cancer risk in EPIC (odds ratio [OR] 0.80 per standard deviation [SD], 95% confidence interval [CI] 0.69-0.92, FDR P-value=0.03) and in UK Biobank (HR 0.93 per SD, 95% CI 0.87-0.99, P-value=0.03). Glutamine levels were borderline inversely associated with colorectal cancer risk in EPIC (OR 0.85 per SD, 95% CI 0.75-0.97, FDR P-value=0.08) and similarly in UK Biobank (HR 0.95, 95% CI 0.89-1.01, P=0.09) In both cohorts, associations changed only minimally when cases diagnosed within 2 or 5 years of follow-up were excluded.

CONCLUSIONS

Higher circulating levels of histidine were associated with a lower risk of colorectal cancer in two large prospective cohorts. Further research to ascertain the role of histidine metabolism and potentially that of glutamine in colorectal cancer development is warranted.

The underexplored links between cancer and the internal body climate: Implications for cancer prevention and treatment

In order to effectively manage and cure cancer we should move beyond the general view of cancer as a random process of genetic alterations leading to uncontrolled cell proliferation or simply a predictable evolutionary process involving selection for traits that increase cell fitness. In our view, cancer is a systemic disease that involves multiple interactions not only among cells within tumors or between tumors and surrounding tissues but also with the entire organism and its internal "milieu". We define the internal body climate as an emergent property resulting from spatial and temporal interactions among internal components themselves and with the external environment. The body climate itself can either prevent, promote or support cancer initiation and progression (top-down effect; i.e., body climate-induced effects on cancer), as well as be perturbed by cancer (bottom-up effect; i.e., cancer-induced body climate changes) to further favor cancer progression and spread. This positive feedback loop can move the system towards a "cancerized" organism and ultimately results in its demise. In our view, cancer not only affects the entire system; it is a reflection of an imbalance of the entire system. This model provides an integrated framework to study all aspects of cancer as a systemic disease, and also highlights unexplored links that can be altered to both prevent body climate changes that favor cancer initiation, progression and dissemination as well as manipulate or restore the body internal climate to hinder the success of cancer inception, progression and metastasis or improve therapy outcomes. To do so, we need to (i) identify cancer-relevant factors that affect specific climate components, (ii) develop 'body climate biomarkers', (iii) define 'body climate scores', and (iv) develop strategies to prevent climate changes, stop or slow the changes, or even revert the changes (climate restoration).