Metabolomics Biomarkers for Detection of Colorectal Neoplasms: A Systematic Review

Omics technologies as powerful approaches to unravel colorectal cancer complexity and improve its management

Colorectal cancer (CRC) continues to rank among the deadliest and most prevalent cancers worldwide, necessitating an innovative and comprehensive approach that addresses this serious health challenge at various stages, from screening and diagnosis to treatment and prognosis. As CRC research progresses, the adoption of an omics-centered approach holds transformative potential to revolutionize the management of this disease. Advances in omics technologies encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics allow to unravel the oncogenic alterations at these levels elucidating the intricacies and the heterogeneous nature of CRC. By providing a comprehensive molecular landscape of CRC, omics technologies enable the discovery of potential biomarkers for early non-invasive detection of CRC, definition of CRC subtypes, prediction of its staging, prognosis, and overall survival of CRC patients. They also allow the identification of potential therapeutic targets, prediction of drug response, tracking treatment efficacy, detection of residual disease and cancer relapse, and deciphering the mechanisms of drug resistance. Moreover, they allow the distinction of non-metastatic CRC patients from the metastatic ones as well as the stratification of metastatic risk. Importantly, omics technologies open up new opportunities to establish molecular-based criteria to guide selection of effective treatment paving the way for the personalization of therapy for CRC patients. This review consolidates current knowledge on the omics-based preclinical discoveries in CRC research emphasizing the significant potential of these technologies to improve CRC screening, diagnosis, prognosis and promote the implementation of personalized medicine to ultimately reduce CRC prevalence and mortality.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Metabolomic Exploration of Colorectal Cancer Through Amino Acids and Acylcarnitines Profiling of Serum Samples

BACKGROUND/OBJECTIVES

Colorectal cancer (CRC) represents one of the most prevalent forms of cancer, with high mortality rates. The aim of this study was to observe and understand the metabolic changes in CRC through targeted metabolomics.

METHODS

Samples collected from 58 CRC patients and 35 healthy individuals have been analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), targeting two classes of metabolites: amino acids and acylcarnitines.

RESULTS

Statistical analysis revealed 26 significantly modified (p-value < 0.01; |FC| > 1.2) metabolites in CRC patients compared to the control group and 22 between colon cancer and control, whereas 8 metabolites differed only significantly between rectal cancer and healthy patients. Some of these significantly modified metabolites characterize cancer-specific adaptations, such as increased energy demand, increased tumor invasiveness, capabilities to promote amino acid synthesis, and tumor resistance against acute immune response. Moreover, receiver operator characteristic (ROC) analysis revealed that a set of two acylcarnitines (C6DC and C4-OH) can differentiate between CRC patients and healthy individuals with a high degree of confidence (AUC 0.837).

CONCLUSIONS

By implementing a metabolomics approach targeting amino acids and acylcarnitines, several metabolic alterations induced by CRC have been highlighted. Even though these modifications are not specific enough to act as disease markers, they might prove useful for evaluating patient status.

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.

Exploration of the application potential of serum multi-biomarker model in colorectal cancer screening

Analyzing blood lipid and bile acid profile changes in colorectal cancer (CRC) patients. Evaluating the integrated model's diagnostic significance for CRC. Ninety-one individuals with colorectal cancer (CRC group) and 120 healthy volunteers (HC group) were selected for comparison. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoproteins (Apo) A1, ApoA2, ApoB, ApoC2, and ApoC3 were measured using immunoturbidimetric and colorimetric methods. Additionally, LC-MS/MS was employed to detect fifteen bile acids in the serum, along with six tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 125, CA19-9, CA242, CA50, and CA72-4. Group comparisons utilized independent sample t-tests and Mann-Whitney U tests. A binary logistic regression algorithm was applied to fit the indicators and establish a screening model; the diagnostic accuracy of individual Indicators and the model was analyzed using receiver operating characteristic (ROC) curves. The CRC group showed significantly lower levels in eight serum lipid indicators and eleven bile acids compared to the HC group (P < 0.05). Conversely, serum levels of TG, CA19-9, and CEA were elevated (P < 0.05). Among the measured parameters, ApoA2 stands out for its strong correlation with the presence of CRC, showcasing exceptional screening efficacy with an area under the curve (AUC) of 0.957, a sensitivity of 85.71%, and a specificity of 93.33%. The screening model, integrating ApoA1, ApoA2, lithocholic acid (LCA), and CEA, attained an impressive AUC of 0.995, surpassing the diagnostic accuracy of individual lipids, bile acids, and tumor markers. CRC patients manifest noteworthy alterations in both blood lipids and bile acid profiles. A screening model incorporating ApoA1, ApoA2, LCA, and CEA provides valuable insights for detecting CRC.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care.

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.

Tumor circulating biomarkers in colorectal cancer

CRC is a major global health concern and is responsible for a significant number of cancer-related deaths each year. The successful treatment of CRC becomes more difficult when it goes undetected until it has advanced to a later stage. Diagnostic biomarkers can play a critical role in the early detection of CRC, which leads to improved patient outcomes and increased survival rates. It is important to develop reliable biomarkers for the early detection of CRC to enable timely diagnosis and treatment. To date, CRC detection methods such as endoscopy, blood, and stool tests are imperfect and often only identify cases in the later stages of the disease. To overcome these limitations, researchers are turning to molecular biomarkers as a promising avenue for improving CRC detection. Diagnostic information can be provided more reliably through a noninvasive approach using biomarkers such as mRNA, circulating cell-free DNA, micro-RNA, long non-coding RNA, and proteins. These biomarkers can be found in blood, tissue, feces, and volatile organic compounds. The identification of molecular biomarkers with high sensitivity and specificity for early detection of CRC that are safe, cost-effective, and easily measurable remains a significant challenge for researchers. In this article, we will explore the latest advancements in blood-based diagnostic biomarkers for CRC and their potential impact on improving patient survival rates.

Metabolomics analyses of cancer tissue from patients with colorectal cancer

The alteration of metabolism is essential for the initiation and progression of numerous types of cancer, including colorectal cancer (CRC). Metabolomics has been used to study CRC. At present, the reprogramming of the metabolism in CRC remains to be fully elucidated. In the present study, comprehensive untargeted metabolomics analysis was performed on the paired CRC tissues and adjacent normal tissues from patients with CRC (n=35) using ultra‑high‑performance liquid chromatography‑mass spectrometry. Subsequently, bioinformatic analysis was performed on the differentially expressed metabolites. The changes in these differential metabolites were compared among groups of patients based on sex, anatomical tumor location, grade of tumor differentiation and stage of disease. A total of 927 metabolites were detected in the tissue samples, and 24 metabolites in the CRC tissue were significantly different compared with the adjacent normal tissue. The present study revealed that the levels of three amino acid metabolites were increased in the CRC tissue, specifically, N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, cyclo(Glu‑Glu) and cyclo(Phe‑Glu). The metabolites with decreased levels in the CRC tissue included quinaldic acid (also referred to as quinoline‑2‑carboxilic acid), 17α‑ and 17β‑estradiol, which are associated with tumor suppression activities, as well as other metabolites such as, anhydro‑β‑glucose, Asp‑Arg, lysophosphatidylcholine, lysophosphatidylethanolamine (lysoPE), lysophosphatidylinositol, carnitine, 5'‑deoxy‑5'‑(methylthio) adenosine, 2'‑deoxyinosine‑5'‑monophosphate and thiamine monophosphate. There was no difference in the levels of the differential metabolites between male and female patients. The differentiation of CRC also showed no impact on the levels of the differential metabolites. The levels of lysoPE were increased in the right side of the colon compared with the left side of the colon and rectum. Analysis of the different tumor stages indicated that 2‑aminobenzenesulfonic acid, P‑sulfanilic acid and quinoline‑4‑carboxylic acid were decreased in stage I CRC tissue compared with stage II, III and IV CRC tissue. The levels of N‑α‑acetyl‑ε‑(2‑propenal)‑Lys, methylcysteine and 5'‑deoxy‑5'‑(methylthio) adenosine varied at different stages of tumorigenesis. These differential metabolites were implicated in multiple metabolism pathways, including carbohydrate, amino acid, lipid, nucleotide and hormone. In conclusion, the present study demonstrated that CRC tumors had altered metabolites compared with normal tissue. The data from the metabolic profile of CRC tissues in the present study provided supportive evidence to understand tumorigenesis.

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.

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.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm^-1, 1004 cm^-1, 1449 cm^-1, 1089-1128 cm^-1, 1311-1367 cm^-1 and 1585-1665 cm^-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm^-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolite analyses in 10 CRC (TCRC) and normal paired tissues (THC) along with 10 matched fecal samples (FCRC) and 10 healthy controls (FHC). The highest microbial phyla abundance from THC and TCRC were Firmicutes, while the dominant phyla from FHC and FCRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in THC than in TCRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in FHC than in FCRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between THC and TCRC groups and 700 metabolites different between FHC and FCRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Metabolites as extracellular vesicle cargo in health, cancer, pleural effusion, and cardiovascular diseases: An emerging field of study to diagnostic and therapeutic purposes

Extracellular vesicles (EVs) are highly diverse nanoscale membrane-bound structures released from different cell types into the extracellular environment. They play essential functions in cell signaling by transporting their cargo, such as proteins, RNA, DNA, lipids, metabolites, and small molecules, to recipient cells. It has recently been shown that EVs might modulate carcinogenesis by delivering cargo to recipient cells. Furthermore, recent discoveries revealed that changes in plasma-derived EV levels and cargo in subjects with metabolic diseases were documented by many researchers, suggesting that EVs might be a promising source of disease biomarkers. One of the cargos of EVs that has recently attracted the most attention is metabolites. The metabolome of these vesicles introduces a plethora of disease indicators; hence, examining the metabolomics of EVs detected in human biofluids would be an effective approach. On the other hand, metabolites have various roles in biological systems, including the production of energies, synthesizing macromolecules, and serving as signaling molecules and hormones. Metabolome rewiring in cancer and stromal cells is a characteristic of malignancy, but the current understanding of how this affects the metabolite composition and activity of tumor-derived EVs remains in its infancy. Since new findings and studies in the field of exosome biology and metabolism are constantly being published, it is likely that diagnostic and treatment techniques, including the use of exosome metabolites, will be launched in the coming years. Recent years have seen increased interest in the EV metabolome as a possible source for biomarker development. However, our understanding of the role of these molecules in health and disease is still immature. In this work, we have provided the latest findings regarding the role of metabolites as EV cargoes in the pathophysiology of diseases, including cancer, pleural effusion (PE), and cardiovascular disease (CVD). We also discussed the significance of metabolites as EV cargoes of microbiota and their role in host-microbe interaction. In addition, the latest findings on metabolites in the form of EV cargoes as biomarkers for disease diagnosis and treatment are presented in this study.

A systematic review of microbiome-derived biomarkers for early colorectal cancer detection

Increasing evidence suggests a role of the gut microbiome in the development of colorectal cancer (CRC) and that it can serve as a biomarker for early diagnosis. This review aims to give an overview of the current status of published studies regarding the microbiome as a screening tool for early CRC detection. A literature search was conducted using PubMed and EMBASE in August 2022. Studies assessing the efficacy of microbiome-derived biomarkers based on noninvasive derived samples were included. Not relevant studies or studies not specifying the stage of CRC or grouping them together in the analysis were excluded. The risk of bias for screening tools was performed using the QUADAS-2 checklist. A total of 28 studies were included, ranging from 2 to 462 for CRC and 18 to 665 advanced adenoma patient inclusions, of which only two investigated the co-metabolome as biomarker. The diagnostic performance of faecal bacteria-derived biomarkers had an AUC ranging from 0.28-0.98 for precursor lesions such as advanced adenomas and 0.54-0.89 for early CRC. Diagnostic performance based on the co-metabolome showed an AUC ranging from 0.69 - 0.84 for precursor lesions and 0.65 - 0.93 for early CRC. All models improved when combined with established clinical early detection markers such as gFOBT. A high level of heterogeneity was seen in the number of inclusions and methodology used in the studies. The faecal and oral gut microbiome has the potential to complement existing CRC screening tools, however current evidence suggests that this is not yet ready for routine clinical use.

Altered acetyl-CoA metabolism presents a new potential immunotherapy target in the obese lung microenvironment

Contrary to the "obesity paradox," which arises from retrospective studies relying on body mass index to define obesity, epidemiologic evidence suggests central or visceral obesity is associated with a higher risk for the development of lung cancer. About 60% of individuals at high risk for developing lung cancer or those already with early-stage disease are either overweight or obese. Findings from resected patient tumors and mouse lung tumor models show obesity dampens immune activity in the tumor microenvironment (TME) encouraging disease progression. In line with this, we have observed a marked, obesity-specific enhancement in the presence and phenotype of immunosuppressive regulatory T (Treg) cells in murine tumors as well as the airways of both humans and mice. Leveraging direct metabolomic measurements and robust inferred analyses from RNA-sequencing data, we here demonstrate for the first time that visceral adiposity alters the lung microenvironment via dysregulated acetyl-CoA metabolism in a direction that facilitates immune suppression and lung carcinogenesis.

Altered gut metabolites and microbiota interactions are implicated in colorectal carcinogenesis and can be non-invasive diagnostic biomarkers

BACKGROUND

Gut microbiota contributes to colorectal cancer (CRC) pathogenesis through microbes and their metabolites. The importance of microbiota-associated metabolites in colorectal carcinogenesis highlights the need to investigate the gut metabolome along the adenoma-carcinoma sequence to determine their mechanistic implications in the pathogenesis of CRC. To date, how and which microbes and metabolites interactively promote early events of CRC development are still largely unclear. We aim to determine gut microbiota-associated metabolites and their linkage to colorectal carcinogenesis.

RESULTS

We performed metabolomics and metagenomics profiling on fecal samples from 386 subjects including 118 CRC patients, 140 colorectal adenomas (CRA) patients and 128 healthy subjects as normal controls (NC). We identified differences in the gut metabolite profiles among NC, CRA and CRC groups by partial least squares-discriminant and principal component analyses. Among the altered metabolites, norvaline and myristic acid showed increasing trends from NC, through CRA, to CRC. CRC-associated metabolites were enriched in branched-chain amino acids, aromatic amino acids and aminoacyl-tRNA biosynthesis pathways. Moreover, metabolites marker signature (twenty metabolites) classified CRC from NC subjects with an area under the curve (AUC) of 0.80, and CRC from CRA with an AUC of 0.79. Integrative analyses of metabolomics and metagenomics profiles demonstrated that the relationships among CRC-associated metabolites and bacteria were altered across CRC stages; certain associations exhibited increasing or decreasing strengths while some were reversed from negative to positive or vice versa. Combinations of gut bacteria with the metabolite markers improved their diagnostic performances; CRC vs NC, AUC: 0.94; CRC vs CRA, AUC 0.92; and CRA vs NC, AUC: 0.86, indicating a potential for early diagnosis of colorectal neoplasia.

CONCLUSIONS

This study underscores potential early-driver metabolites in stages of colorectal tumorigenesis. The Integrated metabolite and microbiome analysis demonstrates that gut metabolites and their association with gut microbiota are perturbed along colorectal carcinogenesis. Fecal metabolites can be utilized, in addition to bacteria, for non-invasive diagnosis of colorectal neoplasia. Video Abstract.

Metabolomics and the Multi-Omics View of Cancer

Cancer is widely regarded to be a genetic disease. Indeed, over the past five decades, the genomic perspective on cancer has come to almost completely dominate the field. However, this genome-only view is incomplete and tends to portray cancer as a disease that is highly heritable, driven by hundreds of complex genetic interactions and, consequently, difficult to prevent or treat. New evidence suggests that cancer is not as heritable or purely genetic as once thought and that it really is a multi-omics disease. As highlighted in this review, the genome, the exposome, and the metabolome all play roles in cancer’s development and manifestation. The data presented here show that >90% of cancers are initiated by environmental exposures (the exposome) which lead to cancer-inducing genetic changes. The resulting genetic changes are, then, propagated through the altered DNA of the proliferating cancer cells (the genome). Finally, the dividing cancer cells are nourished and sustained by genetically reprogrammed, cancer-specific metabolism (the metabolome). As shown in this review, all three “omes” play roles in initiating cancer. Likewise, all three “omes” interact closely, often providing feedback to each other to sustain or enhance tumor development. Thanks to metabolomics, these multi-omics feedback loops are now much more evident and their roles in explaining the hallmarks of cancer are much better understood. Importantly, this more holistic, multi-omics view portrays cancer as a disease that is much more preventable, easier to understand, and potentially, far more treatable.

Metabolomic Comparison of Patients With Colorectal Cancer at Different Anticancer Treatment Stages

BACKGROUND

The difficulties of early diagnosis of colorectal cancer (CRC) result in a high mortality rate. The ability to predict the response of a patient to surgical resection or chemotherapy may be of great value for clinicians when planning CRC treatments. Metabolomics is an emerging tool for biomarker discovery in cancer research. Previous reports have indicated that the metabolic profile of individuals can be significantly altered between CRC patients and healthy controls. However, metabolic changes in CRC patients at different treatment stages have not been explored.

METHODS

To this end, we performed nuclear magnetic resonance (NMR)-based metabolomic analysis to determine metabolite aberrations in CRC patients before and after surgical resection or chemotherapy. In general, a total of 106 urine samples from four clinical groups, namely, healthy volunteers (n = 31), presurgery CRC patients (n = 25), postsurgery CRC patients (n = 25), and postchemotherapy CRC patients (n = 25), were collected and subjected to further analysis.

RESULTS

In the present study, we identified five candidate metabolites, namely, N-phenylacetylglycine, succinate, 4-hydroxyphenylacetate, acetate, and arabinose, in CRC patients compared with healthy individuals, three of which were reported for the first time. Furthermore, approximately ten metabolites were uniquely identified at each stage of CRC treatment, serving as good candidates for biomarker panel selection.

CONCLUSION

In summary, these potential metabolite candidates may provide promising early diagnostic and monitoring approaches for CRC patients at different anticancer treatment stages.

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.

Biomarkers to Detect Early-Stage Colorectal Cancer

Colorectal cancer is a leading cause of mortality worldwide. The high incidence and the acceleration of incidence in younger people reinforces the need for better techniques of early detection. The use of noninvasive biomarkers has potential to more accurately inform how patients are prioritised for clinical investigation, which, in turn, may ultimately translate into improved survival for those subsequently found to have curable-stage CRC. This review surveys a wide range of CRC biomarkers that may (alone or in combination) identify symptomatic patients presenting in primary care who should be progressed for clinical investigation.

Metabolomics in cancer research and emerging applications in clinical oncology

Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.

Sphingomyelin is involved in multisite musculoskeletal pain: evidence from metabolomic analysis in 2 independent cohorts

ABSTRACT

Metabolic dysfunction has been suggested to be involved in musculoskeletal pain; however, few studies have identified metabolic markers associated with multisite musculoskeletal pain (MSMP). This study sought to identify metabolic marker(s) for MSMP by metabolomic analysis. The Tasmanian Older Adult Cohort Study (TASOAC) provided the discovery cohort with the Newfoundland Osteoarthritis Study (NFOAS) providing the replication cohort. Multisite musculoskeletal pain was assessed by a self-reported pain questionnaire and defined as painful sites ≥4 in both the TASOAC and the NFOAS. Furthermore, MSMP was also defined as painful sites ≥7, whereas non-MSMP was defined as either painful sites <7 or ≤1 in the NFOAS. Serum samples of the TASOAC received metabolic profiling using The Metabolomics Innovation Centre Prime Metabolomics Profiling Assay. The data on the identified metabolites were retrieved from NFOAS metabolomic database for the purpose of replication. A total of 409 participants were included in the TASOAC, 38% of them had MSMP. Among the 143 metabolites assessed, 129 passed quality control and were included in the analysis. Sphingomyelin (SM) C18:1 was significantly associated with MSMP (odds ratio [OR] per log µM increase = 3.96, 95% confidence interval, 1.95-8.22; P = 0.0002). The significance remained in multivariable analysis (OR per log µM increase = 2.70, 95% confidence interval, 1.25-5.95). A total of 610 participants were included in the NFOAS, and the association with SM C18:1 was successfully replicated with 3 MSMP definitions (OR ranging from 1.89 to 2.82; all P < 0.03). Our findings suggest that sphingomyelin metabolism is involved in the pathogenesis of MSMP, and the circulating level of SM C18:1 could serve as a potential marker in the management of MSMP.

The plasma metabolome of Atlantic salmon as studied by 1H NMR spectroscopy using standard operating procedures: effect of aquaculture location and growth stage

INTRODUCTION

Metabolomics applications to the aquaculture research are increasing steadily. The use of standardized proton nuclear magnetic resonance (¹H NMR) spectroscopy can provide the aquaculture industry with an unbiased, reproducible, and high-throughput screening tool, which can help to diagnose nutritional and disease-related metabolic disorders in farmed fish.

OBJECTIVE

Standard operating procedures developed for analysing (human) plasma by ¹H NMR were applied to fingerprint the metabolome in plasma samples collected from Atlantic salmon. The aim was to explore the metabolome of salmon plasma in relation to growth stage and sampling site.

METHODS

A total of 72 salmon were collected from three aquaculture sites in Norway (Lat. 65, 67, and 70 °N) and over two sampling events (December 2017 and November 2018). Plasma drawn from each salmon was measured by ¹H NMR and metabolites were quantified using the SigMa software. The NMR data was analysed by principal component analysis (PCA) and ANOVA-simultaneous component analysis (ASCA).

RESULTS

Important metabolic differences were evidenced, with adult salmon having a much higher content of very low-density lipoproteins and cholesterol in their plasma, while smolts displayed significantly higher levels of propylene glycol. Overall, 24% of the metabolite variation was due to the growth stage, whereas 12% of the metabolite variation was related to the aquaculture site and practice (p < 0.001).

CONCLUSION

This study provides a baseline investigation of the plasma metabolome of the Atlantic salmon and demonstrates how ¹H NMR metabolomics can be used in future investigations for comparing aquaculture practices and their influence on the fish metabolome.

Microbiome and colorectal cancer: A review of the past, present, and future

The gastrointestinal tract is home to diverse and abundant microorganisms, collectively referred to as the microbiome. This ecosystem typically contains trillions of microbial cells that play an important role in regulation of human health. The microbiome has been implicated in host immunity, nutrient absorption, digestion, and metabolism. In recent years, researchers have shown that alteration of the microbiome is associated with disease development, such as obesity, inflammatory bowel disease, and cancer. This review discusses the five decades of research into the human microbiome and the development of colorectal cancer - the historical context including experiments that sparked interest, the explosion of research that has occurred in the last decade, and finally the future of testing and treatment.

Comparing Metabolomics Profiles in Various Types of Liquid Biopsies among Screening Participants with and without Advanced Colorectal Neoplasms

Analysis of metabolomics has been suggested as a promising approach for early detection of colorectal cancer and advanced adenomas. We investigated and compared the metabolomics profile in blood, stool, and urine samples of screening colonoscopy participants and aimed to evaluate differences in metabolite concentrations between people with advanced colorectal neoplasms and those without neoplasms. Various types of bio-samples (plasma, feces, and urine) from 400 participants of screening colonoscopy were investigated using the MxP® Quant 500 kit (Biocrates, Innsbruck, Austria). We detected a broad range of metabolites in blood, stool, and urine samples (504, 331, and 131, respectively). Significant correlations were found between concentrations in blood and stool, blood and urine, and stool and urine for 93, 154, and 102 metabolites, of which 68 (73%), 126 (82%), and 39 (38%) were positive correlations. We found significant differences between participants with and without advanced colorectal neoplasms for concentrations of 123, 49, and 28 metabolites in blood, stool and urine samples, respectively. We detected mostly positive correlations between metabolite concentrations in blood samples and urine or stool samples, and mostly negative correlations between urine and stool samples. Differences between subjects with and without advanced colorectal neoplasms were found for metabolite concentrations in each of the three bio-fluids.

Targeted Plasma Metabolic Profiles and Risk of Recurrence in Stage II and III Colorectal Cancer Patients: Results from an International Cohort Consortium

The identification of patients at high-risk for colorectal cancer (CRC) recurrence remains an unmet clinical need. The aim of this study was to investigate associations of metabolites with risk of recurrence in stage II/III CRC patients. A targeted metabolomics assay (128 metabolites measured) was performed on pre-surgery collected EDTA plasma samples from n = 440 newly diagnosed stage II/III CRC patients. Patients have been recruited from four prospective cohort studies as part of an international consortium: Metabolomic profiles throughout the continuum of CRC (MetaboCCC). Cox proportional hazard models were computed to investigate associations of metabolites with recurrence, adjusted for age, sex, tumor stage, tumor site, body mass index, and cohort; false discovery rate (FDR) was used to account for multiple testing. Sixty-nine patients (15%) had a recurrence after a median follow-up time of 20 months. We identified 13 metabolites that were nominally associated with a reduced risk of recurrence. None of the associations were statistically significant after controlling for multiple testing. Pathway topology analyses did not reveal statistically significant associations between recurrence and alterations in metabolic pathways (e.g., sphingolipid metabolism p = 0.04; pFDR = 1.00). To conclude, we did not observe statistically significant associations between metabolites and CRC recurrence using a well-established metabolomics assay. The observed results require follow-up in larger studies.

Comparison of Proteomic Technologies for Blood-Based Detection of Colorectal Cancer

Blood-based protein biomarkers are increasingly being explored as supplementary or efficient alternatives for population-based screening of colorectal cancer (CRC). The objective of the current study was to compare the diagnostic potential of proteins measured with different proteomic technologies. The concentrations of protein biomarkers were measured using proximity extension assays (PEAs), liquid chromatography/multiple reaction monitoring-mass spectrometry (LC/MRM-MS) and quantibody microarrays (QMAs) in plasma samples of 56 CRC patients and 99 participants free of neoplasms. In another approach, proteins were measured in serum samples of 30 CRC cases and 30 participants free of neoplasm using immunome full-length functional protein arrays (IpAs). From all the measurements, 9, 6, 35 and 14 protein biomarkers overlapped for comparative evaluation of (a) PEA and LC/MRM-MS, (b) PEA and QMA, (c) PEA and IpA, and (d) LC/MRM-MS and IpA measurements, respectively. Correlation analysis was performed, along with calculation of the area under the curve (AUC) for assessing the diagnostic potential of each biomarker. DeLong's test was performed to assess the differences in AUC. Evaluation of the nine biomarkers measured with PEA and LC/MRM-MS displayed correlation coefficients >+0.6, similar AUCs and DeLong's p-values indicating no differences in AUCs for biomarkers like insulin-like growth factor binding protein 2 (IGFBP2), matrix metalloproteinase 9 (MMP9) and serum paraoxonase lactonase 3 (PON3). Comparing six proteins measured with PEA and QMA showed good correlation and similar diagnostic performance for only one protein, growth differentiation factor 15 (GDF15). The comparison of 35 proteins measured with IpA and PEA and 14 proteins analyzed with IpA and LC/MRM-MS revealed poor concordance and comparatively better AUCs when measured with PEA and LC/MRM-MS. The comparison of different proteomic technologies suggests the superior performance of novel technologies like PEA and LC/MRM-MS over the assessed array-based technologies in blood-protein-based early detection of CRC.

Urinary charged metabolite profiling of colorectal cancer using capillary electrophoresis-mass spectrometry

Colorectal cancer (CRC) has increasing global prevalence and poor prognostic outcomes, and the development of low- or less invasive screening tests is urgently required. Urine is an ideal biofluid that can be collected non-invasively and contains various metabolite biomarkers. To understand the metabolomic profiles of different stages of CRC, we conducted metabolomic profiling of urinary samples. Capillary electrophoresis-time-of-flight mass spectrometry was used to quantify hydrophilic metabolites in 247 subjects with stage 0 to IV CRC or polyps, and healthy controls. The 154 identified and quantified metabolites included metabolites of glycolysis, TCA cycle, amino acids, urea cycle, and polyamine pathways. The concentrations of these metabolites gradually increased with the stage, and samples of CRC stage IV especially showed a large difference compared to other stages. Polyps and CRC also showed different concentration patterns. We also assessed the differentiation ability of these metabolites. A multiple logistic regression model using three metabolites was developed with a randomly designated training dataset and validated using the remaining data to differentiate CRC and polys from healthy controls based on a panel of urinary metabolites. These data highlight the changes in metabolites from early to late stage of CRC and also the differences between CRC and polyps.

Tumor Tissue-Specific Biomarkers of Colorectal Cancer by Anatomic Location and Stage

The progress in the discovery and validation of metabolite biomarkers for the detection of colorectal cancer (CRC) has been hampered by the lack of reproducibility between study cohorts. The majority of discovery-phase biomarker studies have used patient blood samples to identify disease-related metabolites, but this pre-validation phase is confounded by non-specific disease influences on the metabolome. We therefore propose that metabolite biomarker discovery would have greater success and higher reproducibility for CRC if the discovery phase was conducted in tumor tissues, to find metabolites that have higher specificity to the metabolic consequences of the disease, that are then validated in blood samples. This would thereby eliminate any non-tumor and/or body response effects to the disease. In this study, we performed comprehensive untargeted metabolomics analyses on normal (adjacent) colon and tumor tissues from CRC patients, revealing tumor tissue-specific biomarkers (n = 39/group). We identified 28 highly discriminatory tumor tissue metabolite biomarkers of CRC by orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate analyses (VIP > 1.5, p < 0.05). A stepwise selection procedure was used to identify nine metabolites that were the most predictive of CRC with areas under the curve (AUCs) of >0.96, using various models. We further identified five biomarkers that were specific to the anatomic location of tumors in the colon (n = 236). The combination of these five metabolites (S-adenosyl-L-homocysteine, formylmethionine, fucose 1-phosphate, lactate, and phenylalanine) demonstrated high differentiative capability for left- and right-sided colon cancers at stage I by internal cross-validation (AUC = 0.804, 95% confidence interval, CI 0.670–0.940). This study thus revealed nine discriminatory biomarkers of CRC that are now poised for external validation in a future independent cohort of samples. We also discovered a discrete metabolic signature to determine the anatomic location of the tumor at the earliest stage, thus potentially providing clinicians a means to identify individuals that could be triaged for additional screening regimens.

Diagnostic Value of Combinatorial Markers in Colorectal Carcinoma

Objectives: Blood-based tests have been shown to be an effective strategy for colorectal cancer (CRC) detection in screening programs. This study was aimed to test the performance of 20 blood markers including tumor antigens, inflammatory markers, and apolipoproteins as well as their combinations.

Methods: In total 203 healthy volunteers and 102 patients with CRC were enrolled into the study. Differences between healthy and cancer subjects were evaluated using Wilcoxon rank-sum test. Several multivariate classification algorithms were employed using information about different combinations of biomarkers altered in CRC patients as well as age and gender of the subjects; random sub-sampling cross-validation was done to overcome overfitting problem. Diagnostic performance of single biomarkers and multivariate classification models was evaluated by receiver operating characteristic (ROC) analysis.

Results: Of 20 biomarkers, 16 were significantly different between the groups (p-value ≤ 0.001); ApoA1, ApoA2 and ApoA4 levels were decreased, whereas levels of tumor antigens (e.g. carcinoembriogenic antigen) and inflammatory markers (e.g., C-reactive protein) were increased in CRC patients vs. healthy subjects. Combinatorial markers including information about all 16 significant analytes, age and gender of patients, demonstrated better performance over single biomarkers with average accuracy on test datasets ≥95% and area under ROC curve (AUROC) ≥98%.

Conclusions: Combinatorial approach was shown to be a valid strategy to improve performance of blood-based CRC diagnostics. Further evaluation of the proposed models in screening programs will be performed to gain a better understanding of their diagnostic value.

Biomarkers for detecting colorectal cancer non-invasively: DNA, RNA or proteins?

Colorectal cancer (CRC) is a global problem affecting millions of people worldwide. This disease is unique because of its slow progress that makes it preventable and often curable. CRC symptoms usually emerge only at advanced stages of the disease, consequently its early detection can be achieved only through active population screening, which markedly reduces mortality due to this cancer. CRC screening tests that employ non-invasively detectable biomarkers are currently being actively developed and, in most cases, samples of either stool or blood are used. However, alternative biological substances that can be collected non-invasively (colorectal mucus, urine, saliva, exhaled air) have now emerged as new sources of diagnostic biomarkers. The main categories of currently explored CRC biomarkers are: (1) Proteins (comprising widely used haemoglobin); (2) DNA (including mutations and methylation markers); (3) RNA (in particular microRNAs); (4) Low molecular weight metabolites (comprising volatile organic compounds) detectable by metabolomic techniques; and (5) Shifts in gut microbiome composition. Numerous tests for early CRC detection employing such non-invasive biomarkers have been proposed and clinically studied. While some of these studies generated promising early results, very few of the proposed tests have been transformed into clinically validated diagnostic/screening techniques. Such DNA-based tests as Food and Drug Administration-approved multitarget stool test (marketed as Cologuard®) or blood test for methylated septin 9 (marketed as Epi proColon® 2.0 CE) show good diagnostic performance but remain too expensive and technically complex to become effective CRC screening tools. It can be concluded that, despite its deficiencies, the protein (haemoglobin) detection-based faecal immunochemical test (FIT) today presents the most cost-effective option for non-invasive CRC screening. The combination of non-invasive FIT and confirmatory invasive colonoscopy is the current strategy of choice for CRC screening. However, continuing intense research in the area promises the emergence of new superior non-invasive CRC screening tests that will allow the development of improved disease prevention strategies.

Cancer metabolomic markers in urine: evidence, techniques and recommendations

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.

Early Detection and Recurrence of Colorectal Adenomas by Combination of Eight Cancer-Associated Biomarkers in Plasma

INTRODUCTION

Plasma levels of eight combined proteins have shown value as biomarkers for detection of colorectal cancer (CRC). However, their value in identifying colorectal adenoma needs further evaluation. The aim was to evaluate the eight proteins (AFP, CA19-9, CEA, CyFra21-1, Ferritin, Galectin-3, hs-CRP and TIMP-1) in detection of high-risk adenoma (HRA) and in prediction of recurrence of adenoma. Furthermore, the discrimination between HRA and low-risk adenoma (LRA) or CRC lesions was evaluated.

METHODS

The study included 4698 individuals undergoing diagnostic colonoscopy. Automated ELISA platforms were used in the determination of protein levels in samples collected just before colonoscopy.

RESULTS

Univariably, five proteins (AFP, CEA, CyFra21-1, hs-CRP and TIMP-1), respectively, significantly discriminated individuals with HRA from individuals with non-malignant findings. Multivariably, the combination of CEA and hs-CRP improved performance; AUC= 0.63 (sensitivity=0.19 at specificity=0.90). CyFra21-1, Ferritin and TIMP-1 demonstrated significant discrimination between individuals with HRA and LRA in univariable analyses, respectively. Performance was improved in multivariable analysis; AUC=0.61 (sensitivity=0.13 at specificity=0.90). Discrimination between individuals with colorectal adenomas and healthy individuals was significant for CA19-9, CEA, hs-CRP and TIMP-1, respectively, in univariable analyses. Multivariable analysis improved performance; AUC=0.63 (sensitivity=0.17 at specificity=0.90). All proteins except AFP demonstrated significant discrimination between individuals with HRA and CRC. Combination of CEA, CyFra21-1, Ferritin, hs-CRP and TIMP-1 in multivariable analysis improved discrimination; AUC=0.78 (sensitivity=0.34 at specificity=0.90). Association between plasma levels of any of the eight proteins and recurrence of colorectal adenomas after endoscopic removal could not be demonstrated.

DISCUSSION

The protein panel shows a promising potential in detection of colorectal adenomas in general, but specifically of HRA. However, improvements are needed for the panel to be valuable as a screening test. Finally, plasma levels of the eight proteins were not predictive of recurrence of colorectal adenomas.

Untargeted LC-HRMS-based metabolomics to identify novel biomarkers of metastatic colorectal cancer

Colorectal cancer is one of the main causes of cancer death worldwide, and novel biomarkers are urgently needed for its early diagnosis and treatment. The utilization of metabolomics to identify and quantify metabolites in body fluids may allow the detection of changes in their concentrations that could serve as diagnostic markers for colorectal cancer and may also represent new therapeutic targets. Metabolomics generates a pathophysiological ‘fingerprint’ that is unique to each individual. The purpose of our study was to identify a differential metabolomic signature for metastatic colorectal cancer. Serum samples from 60 healthy controls and 65 patients with metastatic colorectal cancer were studied by liquid chromatography coupled to high-resolution mass spectrometry in an untargeted metabolomic approach. Multivariate analysis revealed a separation between patients with metastatic colorectal cancer and healthy controls, who significantly differed in serum concentrations of one endocannabinoid, two glycerophospholipids, and two sphingolipids. These findings demonstrate that metabolomics using liquid-chromatography coupled to high-resolution mass spectrometry offers a potent diagnostic tool for metastatic colorectal cancer.

Circulating Tumour Cells, Circulating Tumour DNA and Circulating Tumour miRNA in Blood Assays in the Different Steps of Colorectal Cancer Management, a Review of the Evidence in 2019

Despite many advances in the diagnosis and treatment of colorectal cancer (CRC), its incidence and mortality rates continue to make an impact worldwide and in some countries rates are mounting. Over the past decade, liquid biopsies have been the object of fundamental and clinical research with regard to the different steps of CRC patient care such as screening, diagnosis, prognosis, follow-up, and therapeutic response. They are attractive because they are considered to encompass both the cellular and molecular heterogeneity of tumours. They are easily accessible and can be applied to large-scale settings despite the cost. However, liquid biopsies face drawbacks in detection regardless of whether we are testing for circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), or miRNA. This review highlights the different advantages and disadvantages of each type of blood-based biopsy and underlines which specific one may be the most useful and informative for each step of CRC patient care.

A gas chromatography-mass spectrometry (GC-MS) metabolomic approach in human colorectal cancer (CRC): the emerging role of monosaccharides and amino acids

Background

Colorectal cancer (CRC) has been confirmed to be the third most commonly diagnosed cancer in males and the second in females. We investigated the blood plasma metabolome in CRC patients and in healthy adults to elucidate the role of monosaccharides, amino acids, and their respective metabolic pathways as prognostic factors in patients with CRC.

Methods

Fifteen patients with CRC and nine healthy adults were enrolled in the study and their blood plasma samples analyzed by gas chromatography-mass spectrometry (GC-MS). Univariate Student's t-test, multivariate principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were conducted on MetaboAnalyst 4.0. The analysis of metabolic profiles was carried out by the web-based extension Metabolite Sets Enrichment Analysis (MSEA).

Results

Overall, 125 metabolites were identified in plasma samples by GC-MS. In CRC patient samples, nine metabolites, including D-mannose and fructose, were significantly more abundant than in controls; conversely, eleven amino derivatives were less abundant, including methionine, valine, lysine, and proline. Methionine was significantly less abundant in died patients compared with survivors. The most significantly altered metabolic pathways in CRC patients are those involving monosaccharides (primarily the catabolic pathway of fructose and D-mannose), and amino acids (primarily methionine, valine, leucine, and isoleucine).

Conclusions

The abundance of D-mannose in CRC patient samples contributes to inhibiting the growth of cancer cells, while the abundance of fructose may be consistent either with low consumption of fructose by aerobic glycolysis within cancer cells or with a high bioavailability of fructose from diet. The reduction in methionine concentration may be related to increased activity of the threonine and methionine catabolic pathways, confirmed by high levels of α-hydroxybutyrate.

Steroidomics for the Prevention, Assessment, and Management of Cancers: A Systematic Review and Functional Analysis

Steroidomics, an analytical technique for steroid biomarker mining, has received much attention in recent years. This systematic review and functional analysis, following the PRISMA statement, aims to provide a comprehensive review and an appraisal of the developments and fundamental issues in steroid high-throughput analysis, with a focus on cancer research. We also discuss potential pitfalls and proposed recommendations for steroidomics-based clinical research. Forty-five studies met our inclusion criteria, with a focus on 12 types of cancer. Most studies focused on cancer risk prediction, followed by diagnosis, prognosis, and therapy monitoring. Prostate cancer was the most frequently studied cancer. Estradiol, dehydroepiandrosterone, and cortisol were mostly reported and altered in at least four types of cancer. Estrogen and estrogen metabolites were highly reported to associate with women-related cancers. Pathway enrichment analysis revealed that steroidogenesis; androgen and estrogen metabolism; and androstenedione metabolism were significantly altered in cancers. Our findings indicated that estradiol, dehydroepiandrosterone, cortisol, and estrogen metabolites, among others, could be considered oncosteroids. Despite noble achievements, significant shortcomings among the investigated studies were small sample sizes, cross-sectional designs, potential confounding factors, and problematic statistical approaches. More efforts are required to establish standardized procedures regarding study design, analytical procedures, and statistical inference.

Urinary Metabolomics to Identify a Unique Biomarker Panel for Detecting Colorectal Cancer: A Multicenter Study

BACKGROUND

Population-based screening programs are credited with earlier colorectal cancer diagnoses and treatment initiation, which reduce mortality rates and improve patient health outcomes. However, recommended screening methods are unsatisfactory as they are invasive, are resource intensive, suffer from low uptake, or have poor diagnostic performance. Our goal was to identify a urine metabolomic-based biomarker panel for the detection of colorectal cancer that has the potential for global population-based screening.

METHODS

Prospective urine samples were collected from study participants. Based upon colonoscopy and histopathology results, 342 participants (colorectal cancer, 171; healthy controls, 171) from two study sites (Canada, United States) were included in the analyses. Targeted liquid chromatography-mass spectrometry (LC-MS) was performed to quantify 140 highly valuable metabolites in each urine sample. Potential biomarkers for colorectal cancer were identified by comparing the metabolomic profiles from colorectal cancer versus controls. Multiple models were constructed leading to a good separation of colorectal cancer from controls.

RESULTS

A panel of 17 metabolites was identified as possible biomarkers for colorectal cancer. Using only two of the selected metabolites, namely diacetylspermine and kynurenine, a predictor for detecting colorectal cancer was developed with an AUC of 0.864, a specificity of 80.0%, and a sensitivity of 80.0%.

CONCLUSIONS

We present a potentially "universal" metabolomic biomarker panel for colorectal cancer independent of cohort clinical features based on a North American population. Further research is needed to confirm the utility of the profile in a prospective, population-based colorectal cancer screening trial.

IMPACT

A urinary metabolomic biomarker panel was identified for colorectal cancer with the potential of clinical application.

Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review

Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.

Metabolome of Exosomes: Focus on Vesicles Released by Cancer Cells and Present in Human Body Fluids

Exosomes and other classes of extracellular vesicles (EVs) have gained interest due to their role in cell-to-cell communication. Knowledge of the molecular content of EVs may provide important information on features of parental cells and mechanisms of cross-talk between cells. To study functions of EVs it is essential to know their composition, that includes proteins, nucleic acids, and other classes biomolecules. The metabolome, set of molecules the most directly related to the cell phenotype, is the least researched component of EVs. However, the metabolome of EVs circulating in human blood and other bio-fluids is of particular interest because of its potential diagnostic value in cancer and other health conditions. On the other hand, the metabolome of EVs released to culture media in controlled conditions in vitro could shed light on important aspects of communication between cells in model systems. This paper summarizes the most common approaches implemented in EV metabolomics and integrates currently available data on the composition of the metabolome of EVs obtained in different models with particular focus on human body fluids and cancer cells.

Determination of Urinary Pterins by Capillary Electrophoresis Coupled with LED-Induced Fluorescence Detector

Urinary pterins have been found as potential biomarkers in many pathophysiological conditions including inflammation, viral infections, and cancer. However, pterins determination in biological samples is difficult due to their degradation under exposure to air, light, and heat. Besides, they occur at shallow concentration levels, and thus, standard UV detectors cannot be used without additional sample preconcentration. On the other hand, ultra-sensitive laser-induced fluorescence (LIF) detection can be used since pterins exhibit native fluorescence. The main factor that limits an everyday use of LIF detectors is its high price. Here, an alternative detector, i.e., light-emitted diode induced fluorescence (LEDIF) detector, was evaluated for the determination of pterins in urine samples after capillary electrophoresis (CE) separation. An optimized method was validated in terms of linearity range, limit of detection (LOD), limit of quantification (LOQ), intra- and interday precision and accuracy, sample stability in the autosampler, and sample stability during the freezing/thawing cycle. The obtained LOD (0.1 µM) and LOQ (0.3 µM) values were three-order of magnitude lower compared to UV detector, and two orders of magnitude higher compared to previously reported house-built LIF detector. The applicability of the validated method was demonstrated in the analysis of urine samples from healthy individuals and cancer patients.