Prospective study of blood metabolites associated with colorectal cancer risk

Blood metabolic biomarkers and colorectal cancer risk: results from large prospective cohort and Mendelian randomisation analyses

BACKGROUND

Emerging evidence suggests metabolic dysregulation may contribute to colorectal cancer (CRC) aetiology. We aimed to identify pre-diagnostic metabolic biomarkers for CRC risk in 230,420 UK Biobank participants.

METHODS

Nuclear magnetic resonance spectroscopy was used to quantify 249 metabolic biomarkers in plasma samples collected at baseline. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals (CIs) for associations of metabolic biomarkers with CRC risk after adjusting for potential confounders. To infer the potential causality of biomarkers that were associated with CRC independent of the others, we performed genome-wide association analyses among 199,732 UK Biobank participants of European ancestry to identify biomarker-associated genetic variants, followed by two-sample Mendelian randomization (MR) analyses using summary statistics of 78,473 CRC cases and 107,143 controls of European ancestry.

RESULTS

During a median follow-up time of 9.7 years, 2,410 incident primary CRC cases were identified. Among 43 CRC-associated (P-value < 0.001) metabolic biomarkers, ten biomarkers including fatty acids (FAs), inflammation, ketone bodies, and lipoprotein lipids were associated with CRC risk after mutual adjustment. MR analyses provided strong evidence for potential causal associations of CRC risk with percentages of linolic acid [odds ratio (OR) = 0.89, 95% CI = 0.83-0.96, P-value = 3 × 10-3] and saturated FAs (OR = 1.14, 95% CI = 1.03-1.25, P-value = 9  ×  10-3) to total FAs.

CONCLUSIONS

We identified multiple CRC-associated metabolic biomarkers. Perturbed lipid and lipoprotein metabolism may promote colorectal carcinogenesis.

Targeted plasma lipidomic profiles associated with colorectal cancer risk and effect modifications by common risk factors and particulate matter exposure

The role of specific lipids in colorectal cancer (CRC) incidence remains unclear. We aimed to evaluate associations of plasma lipids with CRC risk and the modification effects of common risk factors and ambient particulate matter (PM) exposure. We conducted a nested case-control study within the Dongfeng-Tongji cohort, including 218 cases and 436 matched controls. Plasma levels of 155 lipids were determined by UPLC-MS/MS. The conditional logistic regression and LASSO regression identified nine lipids as potential CRC risk biomarkers. Specifically, triacylglycerol (TAG) 56:7 [20:4], cholesterol ester (CE) 20:2 (1), CE 22:5, lysophosphatidylethanolamine (LPE) 18:2, and sphingomyelin (SM) 32:2 were associated with decreased CRC risk (adjusted ORs per SD: 0.05-0.68), while LPE 18:0, diglycerol (DAG) 16:1/17:1, SM 38:1, and SM 34:0 were associated with increased CRC risk (ORs per SD: 1.60-6.19). Compared to the traditional factors, these lipids exerted improvement of 35.4% in area under the curve (AUC) discriminations for CRC (AUC = 0.972 vs. 0.618). Notably, the associations between specific lipids and CRC risk were modified by BMI (TAG 56:7 [20:4]), smoking (LPE 18:0, DAG 16:1/17:1), alcohol consumption (SM 32:2), healthy diet (TAG 56:7 [20:4]), and PM exposure (TAG 56:7 [20:4], DAG 16:1/17:1, SM 38:1, LPE 18:2) (all Pinteraction <0.05). Our study identified 9 plasma lipids with significant associations with CRC risk and underscored the effect modifications of common risk factors and PM exposure on these associations. These findings provide new insights into the links between specific lipids and CRC development.

Novel metabolomic predictors of incident colorectal cancer in men and women

BACKGROUND

Metabolomic profiles may influence colorectal cancer (CRC) development. Few studies have performed prediagnostic metabolome-wide analyses with CRC risk.

METHODS

We conducted a nested case-control study among women (Nurses' Health Study) and men (Health Professionals Follow-Up Study) who provided blood between 1989 and 1995. Over 22.9 years, 684 (409 Nurses' Health Study, 275 Health Professionals Follow-Up Study) individuals developed CRC and were matched 1:1 to unaffected participants. Liquid chromatography-mass spectrometry identified 255 plasma metabolites after quality control. Cohort-specific and combined metabolite association analyses were performed using conditional logistic regression. Metabolite set enrichment analysis was used to identify differential abundance in metabolite classes. The R Weighted Correlation Network Analysis package provided modules of covarying metabolites, which were tested for CRC association.

RESULTS

Metabolite set enrichment analysis identified specific acylcarnitines associated with higher CRC risk and triacylglycerols with lower CRC risk among women and men. Further, phosphatidylcholines were associated with a higher risk of CRC among men. In an analysis restricted to CRC diagnosed 2 years after blood draw, myristoleic acid (odds ratio = 1.37 [95% CI = 1.15 to 1.62]; false discovery rate = 0.072) and C60:12 triacylglycerol (odds ratio = 0.75 [95% CI = 0.64 to 0.88]; false discovery rate = 0.072) were associated with CRC risk in women. Weighted correlation network analysis identified amino acids associated with CRC in men, fatty acid esters (carnitines) with distal CRC in men, and triradylcglycerols inversely associated with CRC in women.

CONCLUSIONS

We identified prediagnostic CRC-associated metabolites with distinct sex-specific profiles. These results provide insight into CRC etiopathogenesis and have implications for risk prediction strategies.

Serum metabolic characteristics associated with the deterioration of colorectal adenomas

Colorectal cancer (CRC) can evolve from colorectal adenomas, which can be further classified into non-advanced adenomas (NAAs) and advanced adenomas (AAs) based on their clinical characteristics. Their prognoses are vastly different, with patients with NAAs having significantly lower recurrence and CRC-related mortality rates than those with AA or CRC. Although serum metabolomics has shown promise for the early diagnosis of CRC, the differences in serum metabolite composition between NAA and AA still need to be further elucidated. This study aimed to explore the mechanism of CRC occurrence and development based on the unique serum metabolic fingerprints of different stages of CRC and to discover a new non-invasive diagnostic method based on serum metabolomics. A clinical CRC progression cohort containing healthy control (NC; n = 40), NAA (n = 40), AA (n = 40), and CRC (n = 22) groups was constructed, and untargeted metabolomic analysis based on liquid chromatography/mass spectrometry was performed to analyze the serum metabolite characteristics of each group. A semi-quantitative analysis of intergroup metabolite differences was conducted, focusing on specific metabolites that differed in the NAA and AA groups. Finally, variable metabolites were selected based on least absolute shrinkage and selection operator (LASSO) regression analysis, and receiver operating characteristic curves were plotted to evaluate the efficacy of the serum metabolite-based diagnostic model in distinguishing NC/NAA populations from AA/CRC populations. Metabolomic analysis revealed significant differences in the composition of metabolites in the NC and NAA groups compared to the CRC group, whereas the serum metabolites of the AA group were similar to those of the CRC group. The levels of 33 metabolites were significantly different in the serum of AA/CRC patients compared to that of NAA patients, and their functions included glycerophospholipid, sphingolipid, and caffeine metabolism. LASSO regression analysis identified 57 differential metabolite variables between the NC/NAA and AA/CRC groups. The diagnostic model constructed using the random forest algorithm had the best discrimination effect, with areas under the curve of 1.000 (95% confidence interval [CI] 1.000-1.000) and 0.685 (95% CI 0.540-0.830) for the training and testing sets, respectively. The composition of serum metabolites is specific to the different stages of CRC development. The serum metabolite composition of patients with AAs was similar to that of patients with CRC. Auxiliary diagnostic measures based on serum metabolites have the potential to guide the follow-up and treatment of patients with adenoma.

Potential of pre-diagnostic metabolomics for colorectal cancer risk assessment or early detection

This systematic review investigates the efficacy of metabolite biomarkers for risk assessment or early detection of colorectal cancer (CRC) and its precursors, focusing on pre-diagnostic biospecimens. Searches in PubMed, Web of Science, and SCOPUS through December 2023 identified relevant prospective studies. Relevant data were extracted, and the risk of bias was assessed with the QUADAS-2 tool. Among the 26 studies included, significant heterogeneity existed for case numbers, metabolite identification, and validation approaches. Thirteen studies evaluated individual metabolites, mainly lipids, while eleven studies derived metabolite panels, and two studies did both. Nine panels were internally validated, resulting in an area under the curve (AUC) ranging from 0.69 to 0.95 for CRC precursors and 0.72 to 1.0 for CRC. External validation was limited to one panel (AUC = 0.72). Metabolite panels and lipid-based biomarkers show promise for CRC risk assessment and early detection but require standardization and extensive validation for clinical use.

Metabolomic Profiling of Pulmonary Neuroendocrine Neoplasms

BACKGROUND/OBJECTIVES

Pulmonary neuroendocrine neoplasms (NENs) account for 20% of malignant lung tumors. Their management is challenging due to their diverse clinical features and aggressive nature. Currently, metabolomics offers a range of potential cancer biomarkers for diagnosis, monitoring tumor progression, and assessing therapeutic response. However, a specific metabolomic profile for early diagnosis of lung NENs has yet to be identified. This study aims to identify specific metabolomic profiles that can serve as biomarkers for early diagnosis of lung NENs.

METHODS

We measured 153 metabolites using liquid chromatography combined with mass spectrometry (LC-MS) in the plasma of 120 NEN patients and compared them with those of 71 healthy individuals. Additionally, we compared these profiles with those of 466 patients with non-small-cell lung cancers (NSCLCs) to ensure clinical relevance.

RESULTS

We identified 21 metabolites with consistently altered plasma concentrations in NENs. Compared to healthy controls, 18 metabolites were specific to carcinoid tumors, 5 to small-cell lung carcinomas (SCLCs), and 10 to large-cell neuroendocrine carcinomas (LCNECs). These findings revealed alterations in various metabolic pathways, such as fatty acid biosynthesis and beta-oxidation, the Warburg effect, and the citric acid cycle.

CONCLUSIONS

Our study identified biomarker metabolites in the plasma of patients with each subtype of lung NENs and demonstrated significant alterations in several metabolic pathways. These metabolomic profiles could potentially serve as biomarkers for early diagnosis and better management of lung NENs.

Metabolite Predictors of Breast and Colorectal Cancer Risk in the Women's Health Initiative

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N-acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N-acetyl-glutamate, allantoin, N-acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine-N-oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

UHPLC-HRMS-based Multiomics to Explore the Potential Mechanisms and Biomarkers for Colorectal Cancer

BACKGROUND

Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum.

METHODS

Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards.

RESULTS

Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00).

CONCLUSIONS

To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention.

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).

Current Research on Molecular Biomarkers for Colorectal Cancer in Stool Samples

Colorectal cancer (CRC) is one of the most diagnosed cancers worldwide, with a high incidence and mortality rate when diagnosed late. Currently, the methods used in healthcare to diagnose CRC are the fecal occult blood test, flexible sigmoidoscopy, and colonoscopy. However, the lack of sensitivity and specificity and low population adherence are driving the need to implement other technologies that can identify biomarkers that not only help with early CRC detection but allow for the selection of more personalized treatment options. In this regard, the implementation of omics technologies, which can screen large pools of biological molecules, coupled with molecular validation, stands out as a promising tool for the discovery of new biomarkers from biopsied tissues or body fluids. This review delves into the current state of the art in the identification of novel CRC biomarkers that can distinguish cancerous tissue, specifically from fecal samples, as this could be the least invasive approach.

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.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

UHPLC-HRMS-based serum untargeted lipidomics: Phosphatidylcholines and sphingomyelins are the main disturbed lipid markers to distinguish colorectal advanced adenoma from cancer

Colorectal advanced adenoma (CAA) is a key precancerous lesion of colorectal cancer (CRC), and early diagnosis can lessen CRC morbidity and mortality. Although abnormal lipid metabolism is associated with the development of CRC, there are no studies on the biomarkers and mechanism of lipid metabolism linked to CAA carcinogenesis. Hence, we performed a lipidomics study of serum samples from 46 CAA, and 50 CRC patients by the ultra high-performance liquid chromatography tandem high resolution mass spectrometry (UHPLC-HRMS) in both electrospray ionization (ESI) modes. Differential lipids were selected by univariate and multivariate statistics analysis, and their diagnostic performance was evaluated using a receiver operating characteristic curve (ROC) analysis. Combining P < 0.05 and variable importance in projection (VIP) > 1, 59 differential lipids were obtained totally. Ten of them showed good discriminant ability for CAA and CRC (AUC > 0.900). Especially, the lipid panel consisting of PC 44:5, PC 35:6e, and SM d40:3 showed the highest selection frequency and outperformed (AUC = 0.952). Additionally, phosphatidylcholine (PC) and sphingomyelin (SM) were the main differential and high-performance lipids. In short, this is the first study to explore the biomarkers and mechanism for CAA-CRC sequence with large-scale serum lipidomics. The findings should provide valuable reference and new clues for the development of diagnostic and therapeutic strategies of CRC.

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.

Comprehensive Insight into Colorectal Cancer Metabolites and Lipids for Human Serum: A Proof-of-Concept Study

Colorectal cancer (CRC) ranks as the third most frequently diagnosed cancer and the second leading cause of cancer-related deaths. The current endoscopic-based or stool-based diagnostic techniques are either highly invasive or lack sufficient sensitivity. Thus, there is a need for less invasive and more sensitive screening approaches. We, therefore, conducted a study on 64 human serum samples representing three different groups (adenocarcinoma, adenoma, and control) using cutting-edge GC×GC-LR/HR-TOFMS (comprehensive two-dimensional gas chromatography coupled with low/high-resolution time-of-flight mass spectrometry). We analyzed samples with two different specifically tailored sample preparation approaches for lipidomics (fatty acids) (25 μL serum) and metabolomics (50 μL serum). In-depth chemometric screening with supervised and unsupervised approaches and metabolic pathway analysis were applied to both datasets. A lipidomics study revealed that specific PUFA (ω-3) molecules are inversely associated with increased odds of CRC, while some PUFA (ω-6) analytes show a positive correlation. The metabolomics approach revealed downregulation of amino acids (alanine, glutamate, methionine, threonine, tyrosine, and valine) and myo-inositol in CRC, while 3-hydroxybutyrate levels were increased. This unique study provides comprehensive insight into molecular-level changes associated with CRC and allows for a comparison of the efficiency of two different analytical approaches for CRC screening using same serum samples and single instrumentation.

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.

Systematic Review: Contribution of the Gut Microbiome to the Volatile Metabolic Fingerprint of Colorectal Neoplasia

Colorectal cancer (CRC) has been associated with changes in volatile metabolic profiles in several human biological matrices. This enables its non-invasive detection, but the origin of these volatile organic compounds (VOCs) and their relation to the gut microbiome are not yet fully understood. This systematic review provides an overview of the current understanding of this topic. A systematic search using PubMed, Embase, Medline, Cochrane Library, and the Web of Science according to PRISMA guidelines resulted in seventy-one included studies. In addition, a systematic search was conducted that identified five systematic reviews from which CRC-associated gut microbiota data were extracted. The included studies analyzed VOCs in feces, urine, breath, blood, tissue, and saliva. Eight studies performed microbiota analysis in addition to VOC analysis. The most frequently reported dysregulations over all matrices included short-chain fatty acids, amino acids, proteolytic fermentation products, and products related to the tricarboxylic acid cycle and Warburg metabolism. Many of these dysregulations could be related to the shifts in CRC-associated microbiota, and thus the gut microbiota presumably contributes to the metabolic fingerprint of VOC in CRC. Future research involving VOCs analysis should include simultaneous gut microbiota analysis.

Progress with Metabolomic Blood Tests for Gastrointestinal Cancer Diagnosis-An Assessment of Biomarker Translation

There is an urgent need for cost-effective, non-invasive tools to detect early stages of gastrointestinal cancer (colorectal, gastric, and esophageal cancers). Despite many publications suggesting circulating metabolites acting as accurate cancer biomarkers, few have reached the clinic. In upper gastrointestinal cancer this is critically important, as there is no test to complement gold-standard endoscopic evaluation in patients with mild symptoms that do not meet referral criteria. Therefore, this study aimed to describe and solve this translational gap. Studies reporting diagnostic accuracy of metabolomic blood-based gastrointestinal cancer biomarkers from 2007 to 2020 were systematically reviewed and progress of each biomarker along the discovery-validation-adoption pathway was mapped. Successful biomarker translation was defined as a composite endpoint, including patent protection/FDA approval/recommendation in national guidelines. The review found 77 biomarker panels of gastrointestinal cancer, including 25 with an AUROC >0.9. All but one was stalled at the discovery phase, 9.09% were patented and none were clinically approved, confirming the extent of biomarker translational gap. In addition, there were numerous "re-discoveries," including histidine, discovered in 7 colorectal studies. Finally, this study quantitatively supports the presence of a translational gap between discovery and clinical adoption, despite clear evidence of highly performing biomarkers with significant potential clinical value.

Early weaning leads to the remodeling of lipid profile in piglet jejunal crypt cells during post-weaning days

Reportedly, proteins involved in lipid metabolism change significantly in the jejunal crypt cells of early-weaned piglets, but the exact lipid profile change remains uncertain. In the present study, 32 piglets weaned at 21 d of age were randomly divided into 4 groups with 8 replicates. The jejunal crypt cells of a group of piglets on the post-weaning day (PWD) 1, 3, 7, and 14 were isolated per time point. Crypt cell lipid profiles were analyzed using ultra-high-performance liquid chromatography coupled with hybrid quadrupole time-of-flight mass spectrometry. This study showed that piglets suffered the greatest weaning stress on PWD 3 in terms of the lowest relative weight of the small intestine, the highest relative weight of the spleen, and the highest levels of malondialdehyde, cholesterol, and low-density lipoprotein cholesterol. The lipid profile of jejunal crypt cells including carnitine, sulfatide, sphingomyelin, hexosylceramide, and ceramide greatly changed after weaning, especially between PWD 3 and 14 (P < 0.05). The differential lipid species between these 2 d were mainly involved in the glycerophospholipid metabolism pathway. In addition, potential lipid biomarkers for weaning stress in crypt cells such as phosphatidylcholine (PC) (9:0/26:1), PC (17:0/18:2), carnitine (24:0), carnitine (22:0), sphingomyelin (d14:1/22:0), PC (P-18:0/18:4), phosphatidylethanolamine (P-16:0/20:4), phosphatidylinositol (15:1/24:4), and dihexosylceramide (d14:1/26:1) were identified. The changes in lipid profile might be related to the inflammation caused by early weaning. These findings might provide new therapeutical targets for intestinal dysfunctions caused by weaning stress.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

METHODS

We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

RESULTS

Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

CONCLUSIONS

These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Metabolic-Dysregulation-Based iEESI-MS Reveals Potential Biomarkers Associated with Early-Stage and Progressive Colorectal Cancer

The application of rapid and accurate diagnostic methods can improve colorectal cancer (CRC) survival rates dramatically. Here, we used a non-targeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry (iEESI-MS) to detect metabolite ions associated with the progression of CRC from 172 tissues (45 stage I/II CRC, 41 stage III/IV CRC, and 86 well-matched normal tissues). A support vector machine (SVM) model based on 10 differential metabolite ions for differentiating early-stage CRC from normal tissues was built with a good prediction accuracy of 92.6%. The biomarker panel consisting of lysophosphatidylcholine (LPC) (18:0) has good diagnostic potential in differentiating early-stage CRC from advanced-stage CRC. We showed that the down-regulation of LPC (18:0) in tumor tissues is associated with CRC progression and related to the regulation of the epidermal growth factor receptor. Pathway analysis showed that metabolic pathways in CRC are related to glycerophospholipid metabolism and purine metabolism. In conclusion, we built an SVM model with good performance to distinguish between early-stage CRC and normal groups based on iEESI-MS and found that LPC (18:0) is associated with the progression of CRC.

Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk

BACKGROUND

The etiology of colorectal cancer is not fully understood.

METHODS

Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis.

RESULTS

Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60-2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99-1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer.

CONCLUSIONS

This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data.

IMPACT

The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.

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

Simple Summary

Colorectal Cancer (CRC) is one of the leading causes of cancer-related death in the United States. Current diagnosis techniques are either highly invasive or lack sensitivity, suggesting the need for alternative techniques for biomarker detection. Metabolomics represents one such technique with great promise in identifying CRC biomarkers with high sensitivity and specificity, but thus far is rarely employed in a clinical setting. In order to provide a framework for future clinical usage, we characterized dysregulated metabolites across recent literature, identifying metabolites dysregulated across a variety of biospecimens. We additionally put special focus on the interplay of the gut microbiome and perturbed metabolites in CRC. We were able to identify many metabolites showing consistent dysregulation in CRC, demonstrating the value of metabolomics as a promising diagnostic technique.

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.

Mendelian Randomization Identifies the Potential Causal Impact of Dietary Patterns on Circulating Blood Metabolites

Nutrition plays an important role in the development and progress of several health conditions, but the exact mechanism is often still unclear. Blood metabolites are likely candidates to be mediating these relationships, as their levels are strongly dependent on the frequency of consumption of several foods/drinks. Understanding the causal effect of food on metabolites is thus of extreme importance. To establish these effects, we utilized two-sample Mendelian randomization using the genetic variants associated with dietary traits as instrumental variables. The estimates of single-nucleotide polymorphisms' effects on exposures were obtained from a recent genome-wide association study (GWAS) of 25 individual and 15 principal-component dietary traits, whereas the ones for outcomes were obtained from a GWAS of 123 blood metabolites measured by nuclear magnetic resonance spectroscopy. We identified 413 potentially causal links between food and metabolites, replicating previous findings, such as the association between increased oily fish consumption and higher DHA, and highlighting several novel associations. Most of the associations were related to very-low-density, intermediate-density (IDL), and low-density lipoproteins (LDL). For example, we found that constituents of IDL particles and large LDL particles were raised by coffee and alcohol while lowered by an overall healthier diet and fruit consumption. Our findings provide a strong base of evidence for planning future RCTs aimed at understanding the role of diet in determining blood metabolite levels.

The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium

BACKGROUND

Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI).

METHODS AND FINDINGS

We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds.

CONCLUSIONS

This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

Risk-Predictive and Diagnostic Biomarkers for Colorectal Cancer; a Systematic Review of Studies Using Pre-Diagnostic Blood Samples Collected in Prospective Cohorts and Screening Settings

This systematic review summarizes the evidence for blood-based colorectal cancer biomarkers from studies conducted in pre-diagnostic, asymptomatic settings. Of 1372 studies initially identified, the final selection included 30 studies from prospective cohorts and 23 studies from general screening settings. Overall, the investigations had high quality but considerable variability in data analysis and presentation of results, and few biomarkers demonstrated a clinically relevant discriminatory ability. One of the most promising biomarkers was the anti-p53 antibody, with consistent findings in one screening cohort and in the 3-4 years prior to diagnosis in two prospective cohort studies. Proteins were the most common type of biomarker assessed, particularly carcinoembryonic antigen (CEA) and C-reactive protein (CRP), with modest results. Other potentially promising biomarkers included proteins, such as AREG, MIC-1/GDF15, LRG1 and FGF-21, metabolites and/or metabolite profiles, non-coding RNAs and DNA methylation, as well as re-purposed routine lab tests, such as ferritin and the triglyceride-glucose index. Biomarker panels generally achieved higher discriminatory performance than single markers. In conclusion, this systematic review highlighted anti-p53 antibodies as a promising blood-based biomarker for use in colorectal cancer screening panels, together with other specific proteins. It also underscores the need for validation of promising biomarkers in independent pre-diagnostic settings.

Lipidomic Signatures for Colorectal Cancer Diagnosis and Progression Using UPLC-QTOF-ESI+MS

Metabolomics coupled with bioinformatics may identify relevant biomolecules such as putative biomarkers of specific metabolic pathways related to colorectal diagnosis, classification and prognosis. This study performed an integrated metabolomic profiling of blood serum from 25 colorectal cancer (CRC) cases previously classified (Stage I to IV) compared with 16 controls (disease-free, non-CRC patients), using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI⁺ MS). More than 400 metabolites were separated and identified, then all data were processed by the advanced Metaboanalyst 5.0 online software, using multi- and univariate analysis, including specificity/sensitivity relationships (area under the curve (AUC) values), enrichment and pathway analysis, identifying the specific pathways affected by cancer progression in the different stages. Several sub-classes of lipids including phosphatidylglycerols (phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and PAs), fatty acids and sterol esters as well as ceramides confirmed the “lipogenic phenotype” specific to CRC development, namely the upregulated lipogenesis associated with tumor progression. Both multivariate and univariate bioinformatics confirmed the relevance of some putative lipid biomarkers to be responsible for the altered metabolic pathways in colorectal cancer.

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

Untargeted metabolomic studies have identified potential biomarkers of colorectal cancer risk, but evidence is still limited and broadly inconsistent. Among 39,239 Cancer Prevention Study II Nutrition cohort participants who provided a blood sample between 1998–2001, 517 newly diagnosed colorectal cancers were identified through 30 June 2015. In this nested case–control study, controls were matched 1:1 to cases on age, sex, race and date of blood draw. Mass spectroscopy-based metabolomic analyses of pre-diagnostic plasma identified 886 named metabolites, after quality control exclusions. Conditional logistic regression models estimated multivariable-adjusted odds ratios (OR) and 95% confidence intervals (CI) for 1 standard deviation (SD) increase in each metabolite with risk of colorectal cancer. Six metabolites were associated with colorectal cancer risk at a false discovery rate < 0.20. These metabolites were of several classes, including cofactors and vitamins, nucleotides, xenobiotics, lipids and amino acids. Five metabolites (guanidinoacetate, 2’-O-methylcytidine, vanillylmandelate, bilirubin (E,E) and N-palmitoylglycine) were positively associated (OR per 1 SD = 1.29 to 1.32), and one (3-methylxanthine) was inversely associated with CRC risk (OR = 0.79, 95% CI, 0.69–0.89). We did not replicate findings from two earlier prospective studies of 250 cases each after adjusting for multiple comparisons. Large pooled prospective analyses are warranted to confirm or refute these findings and to discover and replicate metabolites associated with colorectal cancer risk.

Flurbiprofen suppresses the inflammation, proliferation, invasion and migration of colorectal cancer cells via COX2

Colorectal cancer is an aggressive disease with a poor prognosis and low survival rate at the advanced stage, therefore new innovative targets are urgently required. Flurbiprofen has been reported to exhibit therapeutic effects in other types of cancer, such as esophageal cancer, breast cancer and colorectal cancer. Therefore, the present study aimed to investigate the function of flurbiprofen in colorectal cancer. SW620 colorectal cancer cells were treated with different concentrations of flurbiprofen to determine the optimum concentration. Subsequently, COX2 expression affected by flurbiprofen was tested using western blotting, reverse transcription-quantitative PCR and immunofluorescence. Enzyme-linked immunosorbent assay was used to determine the levels of tumor necrosis factor-α, interleukin (IL)-6 and IL-1β. Cell Counting Kit-8, colony formation and flow cytometry assays were used to assess the proliferation and apoptosis of SW620 cells in various groups. Western blotting was performed to investigate the expression of proliferation-, apoptosis- and migration-related proteins after different treatments. Wound healing and Transwell assays were performed to measure the invasion and migration of colorectal cancer cells, respectively. The results demonstrated that flurbiprofen inhibited colorectal cancer cell proliferation. Furthermore, it was identified that flurbiprofen inhibited the expression of COX2. Notably, flurbiprofen suppressed the expression of inflammatory factors by inhibiting COX2. Moreover, flurbiprofen inhibited the proliferation, invasion and migration of colorectal cancer cells by inhibiting COX2. In conclusion, the present study revealed that flurbiprofen inhibited COX2 expression in colorectal cancer, and affected the proliferation, invasion, migration and apoptosis of colorectal cancer cells. These results expand the understanding of the function of COX2 in colorectal cancer and the effect of flurbiprofen on COX2 expression.

A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

An increasing number of studies have shown that abnormal metabolism processes are closely correlated with the genesis and progression of colorectal cancer (CRC). In this study, we systematically explored the prognostic value of metabolism-related genes (MRGs) for CRC patients. A total of 289 differentially expressed MRGs were screened based on The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), and 72 differentially expressed transcription factors (TFs) were obtained from TCGA and the Cistrome Project database. The clinical samples obtained from TCGA were randomly divided at a ratio of 7 : 3 to obtain the training group (n = 306) and the test group (n = 128). After univariate and multivariate Cox regression analyses, we constructed a prognostic model based on 6 MRGs (AOC2, ENPP2, ADA, GPD1L, ACADL, and CPT2). Kaplan-Meier survival analysis of the training group, validation group, and overall samples proved that the model had statistical significance in predicting the outcomes of patients. Independent prognosis analysis suggested that this risk score might serve as an independent prognosis factor for CRC patients. Moreover, we combined the prognostic model and the clinical characteristics in a nomogram to predict the overall survival of CRC patients. Furthermore, gene set enrichment analysis (GSEA) was conducted to identify the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in the high- and low-risk groups, which might provide novel therapeutic targets for CRC patients. We discovered through the protein-protein interaction (PPI) network and TF-MRG regulatory network that 7 hub genes were retrieved from the PPI network and 4 kinds of differentially expressed TFs (NR3C1, MYH11, MAF, and CBX7) positively regulated 4 prognosis-associated MRGs (GSTM5, PTGIS, ENPP2, and P4HA3).

Integrating Two-Dimensional Gas and Liquid Chromatography-Mass Spectrometry for Untargeted Colorectal Cancer Metabolomics: A Proof-of-Principle Study

Untargeted metabolomics is expected to lead to a better mechanistic understanding of diseases and thus applications of precision medicine and personalized intervention. To further increase metabolite coverage and achieve high accuracy of metabolite quantification, the present proof-of-principle study was to explore the applicability of integration of two-dimensional gas and liquid chromatography-mass spectrometry (GC × GC-MS and 2DLC-MS) platforms to characterizing circulating polar metabolome extracted from plasma collected from 29 individuals with colorectal cancer in comparison with 29 who remained cancer-free. After adjustment of multiple comparisons, 20 metabolites were found to be up-regulated and 8 metabolites were found to be down-regulated, which pointed to the dysregulation in energy metabolism and protein synthesis. While integrating the GC × GC-MS and 2DLC-MS data can dramatically increase the metabolite coverage, this study had a limitation in analyzing the non-polar metabolites. Given the small sample size, these results need to be validated with a larger sample size and with samples collected prior to diagnostic and treatment. Nevertheless, this proof-of-principle study demonstrates the potential applicability of integration of these advanced analytical platforms to improve discrimination between colorectal cancer cases and controls based on metabolite profiles in future studies.

Increased Blood Lipid Level is Associated with Cancer-Specific Mortality and All-Cause Mortality in Patients with Colorectal Cancer (≥65 Years): A Population-Based Prospective Cohort Study

Background

Hyperlipidaemia is related to the development of many cancers. The aim of this study was to explore whether blood lipid levels were associated with increased rates of cancer-specific mortality and all-cause mortality in patients with colorectal cancer (CRC).

Methods

Data on 8504 participants from The Irish Longitudinal Study on Ageing (TILDA) were analysed. A total of 304 participants with CRC who had experienced curative surgery were included. Logistic regression analysis was performed to analyse the relationship between blood lipid levels and CRC severity. Cox regression analysis was performed to assess the association between blood lipid levels and cancer-specific mortality and all-cause mortality in patients with CRC.

Results

In 304 patients with CRC, the average age was 67.8±5.4 years. The logistic regression analysis indicated that elevated levels of total cholesterol (2.104 [1.358–3.650]; P-trend<0.001), triglycerides (1.665 [1.337–2.076]; P-trend=0.005) and LDL (2.127 [1.446–4.099]; P-trend<0.001) but not HDL (0.688 [0.409–1.252]; P-trend=0.124) were associated with an increased risk of higher CRC stage after adjustments were made for age, sex, marital status, BMI, drinking status, smoking status, education, physical activity, antilipidaemic medications and self-reported CVDs (≥2). Cox proportional hazard analysis showed that higher blood lipid levels of total cholesterol, triglycerides and LDL were independently associated with higher rates of cancer-specific mortality and all-cause mortality. Similar results persisted in the sensitivity analysis using antilipidaemic medications as an additional covariate and the stratification analysis using antilipidaemic medications as a stratified variable.

Conclusion

Increased blood lipid levels were associated with an increased risk of cancer-specific mortality and all-cause mortality in patients with CRC after adjusting for potential confounding factors. Clinicians should pay more attention to the prognostic value of increased blood lipids in patients with CRC for the risk of death.

Integrated transcriptomic and metabolomic analyses to characterize the anti-cancer effects of (-)-epigallocatechin-3-gallate in human colon cancer cells

(-)-Epigallocatechin-3-gallate (EGCG) is the main bioactive component in tea (Camellia sinensis) catechins, and exhibits potential antitumor activity against colorectal cancer (CRC). However, the underlying mechanisms are largely unclear. We investigated the effects of EGCG on activities of CRC cells and the exact molecular mechanism. We used human colon cancer cells (HT-29) and exposed them to EGCG at various concentrations. The MTT assay, flow cytometry, and TUNEL staining were used to study the underlying mechanisms of EGCG (proliferation, apoptosis, autophagy). Western blotting was used to measure expression of marker proteins of the cell cycle, apoptosis, and autophagy. Using a combined microarray-based transcriptomic and ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight tandem mass spectrometry (UHPLC-QTOF/MS)-based metabolomic approach, we investigated the perturbed pathways induced by EGCG treatment at transcript and metabolite levels. Transcriptomic analyses showed that 486 genes were differentially expressed between untreated and EGCG-treated cells. Also, 88 differentially expressed metabolites were identified between untreated and EGCG-treated cells. The altered metabolites were involved in the metabolism of glutathione, glycerophospholipids, starch, sucrose, amino sugars, and nucleotide sugars. There was substantial agreement between the results of transcriptomics and metabolomics analyses. Our data indicate that the anticancer activity of EGCG against HT-29 cells is mediated by induction of cell-cycle arrest, apoptosis, and autophagy. EGCG modulates cancer-cell metabolic pathways. These results provide a platform for future molecular mechanistic studies of EGCG.

Identification of F5 as a Prognostic Biomarker in Patients with Gastric Cancer

Association of Coagulation factor V (F5) polymorphisms with the occurrence of many types of cancers has been widely reported, but whether it is of prognostic relevance in some cancers remain to be resolved. The RNA-sequencing dataset was downloaded from The Cancer Genome Atlas (TCGA). The potential of F5 genes to predict the survival time of gastric cancer (GC) patients was investigated using univariate and multivariate survival analysis whereas "Kaplan-Meier plotter" (KM-plotter) online tools were employed to validate the outcomes. TCGA data revealed that F5 mRNA levels were significantly upregulated in gastric cancer samples. Survival analysis confirmed that high levels of F5 mRNA correlated with short overall survival (OS) in gastric cancer patients, and the area under the curve (AUC) values of 1-, 2-, and 5-year OS rate were 0.554, 0.593, and 0.603, respectively. Survival analysis by KM-plotter indicated that the high expression of F5 mRNA was significantly associated with a shorter OS compared with the low expression level in all patients with GC, and this was also the case for patients in stage III (hazard ratio (HR) = 1.78, P = 0.017). These findings suggest that the F5 gene is significantly upregulated in GC tumour tissues, and may be a potential prognostic biomarker for GC.

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case-control sets from EPIC

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR1SD ) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR1SD  = 0.77, 95% confidence interval 0.66-0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR1SD  = 0.72, 0.57-0.90), or lysophosphatidylcholines (OR1SD  = 0.81, 0.69-0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR1SD  = 0.77, 0.61-0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer.

STUDY OF LIPID BIOMARKERS OF PATIENTS WITH POLYPS AND COLORECTAL CÂNCER

BACKGROUND

Colorectal cancer (CRC) is one of the leading causes of cancer worldwide. Early diagnostic methods using serum biomarkers are required. The study of omics, most recently lipidomics, has the purpose of analyzing lipids for a better understanding of human lipidoma. The evolution of mass spectrometry methods, such as MALDI-MS technology, has enabled the detection and identification of a wide variety of lipids with great potential to open new avenues for predictive and preventive medicine.

OBJECTIVE

To determine the lipid profile of patients with colorectal cancer and polyps.

METHODS

Patients with stage I-III CRC, adenomatous polyps and individuals with normal colonoscopy were selected. All patients underwent peripheral blood collection for lipid extraction. The samples were analyzed by MALDI-MS technique for lipid identification.

STATISTICAL ANALYSIS

Univariate and multivariate (principal component analysis [PCA] and discriminant analysis by partial least squares [PLS-DA]) analyses workflows were applied to the dataset, using MetaboAnalyst 3.0 software. The ions were identified according to the class of lipids using the online database Lipid Maps (http://www.lipidmaps.org).

RESULTS

We included 88 individuals, 40 with CRC, 12 with polyps and 32 controls. Boxplot analysis showed eight VIP ions in the three groups. Differences were observed between the cancer and control groups, as well as between cancer and polyp, but not between polyps and control. The polyketide (810.1) was the lipid represented in cancer and overrepresented in polyp and control. Among the patients with CRC we observed differences between lipids with lymph node invasion (N1-2) compared to those without lymph node invasion (N).

CONCLUSION

Possible lipid biomarkers were identified among cancer patients compared to control and polyp groups. The polyketide lipid (810.1) was the best biomarker to differentiate the cancer group from control and polyp. We found no difference between the biomarkers in the polyp group in relation to the control.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Plasma metabolites associated with colorectal cancer: A discovery-replication strategy

Colorectal cancer is known to arise from multiple tumorigenic pathways; however, the underlying mechanisms remain not completely understood. Metabolomics is becoming an increasingly popular tool in assessing biological processes. Previous metabolomics research focusing on colorectal cancer is limited by sample size and did not replicate findings in independent study populations to verify robustness of reported findings. Here, we performed a ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) screening on EDTA plasma from 268 colorectal cancer patients and 353 controls using independent discovery and replication sets from two European cohorts (ColoCare Study: n = 180 patients/n = 153 controls; the Colorectal Cancer Study of Austria (CORSA) n = 88 patients/n = 200 controls), aiming to identify circulating plasma metabolites associated with colorectal cancer and to improve knowledge regarding colorectal cancer etiology. Multiple logistic regression models were used to test the association between disease state and metabolic features. Statistically significant associated features in the discovery set were taken forward and tested in the replication set to assure robustness of our findings. All models were adjusted for sex, age, BMI and smoking status and corrected for multiple testing using False Discovery Rate. Demographic and clinical data were abstracted from questionnaires and medical records.

Global serum metabolomics profiling of colorectal cancer

Accurate diagnosis of colorectal cancer (CRC) relies on the use of invasive tools such as colonoscopy and sigmoidoscopy. Non-invasive tools are less sensitive in detecting the disease, particularly in the early stage. A number of researchers have used metabolomics analyses on serum/plasma samples of patients with CRC compared with normal healthy individuals in an effort to identify biomarkers for CRC. The aim of the present review is to compare reported serum metabolomics profiles of CRC and to identify common metabolites affected among these studies. A literature search was performed to include any experimental studies on global metabolomics profile of CRC using serum/plasma samples published up to March 2018. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was used to assess the quality of the studies reviewed. In total, nine studies were included. The studies used various analytical platforms and were performed on different populations. A pathway enrichment analysis was performed using the data from all the studies under review. The most affected pathways identified were protein biosynthesis, urea cycle, ammonia recycling, alanine metabolism, glutathione metabolism and citric acid cycle. The metabolomics analysis revealed levels of metabolites of glycolysis, tricarboxylic acid cycle, anaerobic respiration, protein, lipid and glutathione metabolism were significantly different between cancer and control samples. Although the majority of differentiating metabolites identified were different in the different studies, there were several metabolites that were common. These metabolites include pyruvic acid, glucose, lactic acid, malic acid, fumaric acid, 3-hydroxybutyric acid, tryptophan, phenylalanine, tyrosine, creatinine and ornithine. The consistent dysregulation of these metabolites among the different studies suggest the possibility of common diagnostic biomarkers for CRC.

Global Plasma Profiling for Colorectal Cancer-Associated Volatile Organic Compounds: a Proof-of-Principle Study

Volatile organic compounds (VOCs) could reflect changes resulting from ongoing pathophysiological processes and altered body metabolisms, and thus have been studied for various types of cancers. We aimed to test an advanced global metabolomic technique to characterize circulating VOCs in patients diagnosed with colorectal cancer (CRC). We employed solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography mass-spectrometry (GC × GC-MS). We analyzed 30 random plasma samples from incident cases of CRC. The 30 samples were from population controls enrolled in a large population-based case-control study. The number of metabolite peaks detected in the cases was significantly lower than that detected in the controls (median 1530 vs. 1694, P = 0.02). Partial least squares-discriminant analysis showed clear VOC profile differences between the CRC and the controls. After adjustment for multiple comparisons at the 5% false discovery rate level, five VOCs were differentially expressed between the cases and the controls. Among these five VOCs, 2,3,4-trimethyl-hexane (decreased) and 2,4-dimethylhept-1-ene (increased) were both lipid peroxidation products but not previously reported for CRC. In summary, this study pointed to an intriguing observation that the richness of volatile metabolites may be reduced in CRC cases and demonstrated the utility of SPME GC × GC-MS in discovery of candidate markers for further validation.

Meeting report from the joint IARC-NCI international cancer seminar series: a focus on colorectal cancer

Despite significant progress in our understanding of the etiology, biology and genetics of colorectal cancer, as well as important clinical advances, it remains the third most frequently diagnosed cancer worldwide and is the second leading cause of cancer death. Based on demographic projections, the global burden of colorectal cancer would be expected to rise by 72% from 1.8 million new cases in 2018 to over 3 million in 2040 with substantial increases anticipated in low- and middle-income countries. In this meeting report, we summarize the content of a joint workshop led by the National Cancer Institute and the International Agency for Research on Cancer, which was held to summarize the important achievements that have been made in our understanding of colorectal cancer etiology, genetics, early detection and treatment and to identify key research questions that remain to be addressed.

Prospective metabolomics study identifies potential novel blood metabolites associated with pancreatic cancer risk

Using a metabolomics approach, we systematically searched for circulating metabolite biomarkers for pancreatic cancer risk in a case-control study nested within two prospective Shanghai cohorts. Included in our study were 226 incident pancreatic cancer cases and their individually-matched controls. Untargeted mass spectrometry platforms were used to measure metabolites in blood samples collected prior to cancer diagnosis. Conditional logistic regression was performed to assess the associations of metabolites with pancreatic cancer risk. We identified 10 metabolites associated with pancreatic cancer, after accounting for multiple comparisons (the Benjamini-Hochberg false discovery rate <0.05). The majority of the identified metabolites were glycerophospholipids (ORs per SD increase: 0.44-2.32; p values: 7.2 × 10^-4 to 1.0 × 10^-6 ), six of which were associated with decreased risk and one with increased risk. Additionally, levels of coumarin (OR = 1.96, p = 3.7 × 10^-6 ) and picolinic acid (OR = 2.53, p = 5.0 × 10^-5 ) were positively associated with pancreatic cancer risk, while tetracosanoic acid was inversely associated with risk (OR = 0.48, p = 7.16 × 10^-7 ). Four metabolites remained statistically significant after mutual adjustment. Our study provides novel evidence that the dysregulation of glycerophospholipids may play an important role in pancreatic cancer development.