Metabolomics Analysis in Serum from Patients with Colorectal Polyp and Colorectal Cancer by 1H-NMR Spectrometry. Dis Markers. 2019;2019:3491852. [PMID: 31089393 PMCID: PMC6476004 DOI: 10.1155/2019/3491852]

Emerging biomarkers for non-invasive diagnosis and treatment of cancer: a systematic review

Cancer remains a global health challenge, necessitating continuous advancements in diagnostic and treatment strategies. This review focuses on the utility of non-invasive biomarkers in cancer diagnosis and treatment, their role in early detection, disease monitoring, and personalized therapeutic interventions. Through a systematic review of the literature, we identified 45 relevant studies that highlight the potential of these biomarkers across various cancer types, such as breast, prostate, lung, and colorectal cancers. The non-invasive biomarkers discussed include liquid biopsies, epigenetic markers, non-coding RNAs, exosomal cargo, and metabolites. Notably, liquid biopsies, particularly those based on circulating tumour DNA (ctDNA), have emerged as the most promising method for early, non-invasive cancer detection due to their ability to provide comprehensive genetic and epigenetic information from easily accessible blood samples. This review demonstrates how non-invasive biomarkers can facilitate early cancer detection, accurate subtyping, and tailored treatment strategies, thereby improving patient outcomes. It underscores the transformative potential of non-invasive biomarkers in oncology, highlighting their application for enhancing early detection, survival rates, and treatment precision in cancer care.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023474749 PROSPERO, identifier CRD42023474749.

Effects of diets on risks of cancer and the mediating role of metabolites

Research on the association between dietary adherence and cancer risk is limited, particularly concerning overall cancer risk and its underlying mechanisms. Using the UK Biobank data, we prospectively investigate the associations between adherence to a Mediterranean diet (MedDiet) or a Mediterranean-Dietary Approaches to Stop Hypertension Diet Intervention for Neurodegenerative Delay diet (MINDDiet) and the risk of overall and 22 specific cancers, as well as the mediating effects of metabolites. Here we show significant negative associations of MedDiet and MINDDiet adherence with overall cancer risk. These associations remain robust across 14 and 13 specific cancers, respectively. Then, a sequential analysis, incorporating Cox regression, elastic net and gradient boost models, identify 10 metabolites associated with overall cancer risk. Mediation results indicate that these metabolites play a crucial role in the association between adherence to a MedDiet or a MINDDiet and cancer risk, independently and cumulatively. These findings deepen our understanding of the intricate connections between diet, metabolites, and cancer development.

Diagnosis and classification of intestinal diseases with urine by surface-enhanced Raman spectroscopy

Intestinal Disease (ID) is often characterized by clinical symptoms such as malabsorption, intestinal dysfunction, and injury. If treatment is not timely, it will increase the risk of cancer. Early diagnosis of ID is the key to cure it. There are certain limitations of the conventional diagnostic methods, such as low sensitivity and specificity. Therefore, development of a highly sensitive, non-invasive diagnostic method for ID is extremely important. Urine samples are easier to collect and more sensitive to changes in biomolecules than other pathological diagnostic samples such as tissue and blood. In this paper, a diagnostic method of ID with urine by surface-enhanced Raman spectroscopy (SERS) is proposed. A classification model between ID patients and healthy controls (HC) and a classification model between different pathological types of ID (i.e., benign intestinal disease (BID) and colorectal cancer (CRC)) are established. Here, 830 urine samples, including 100 HC, 443 BID, and 287 CRC, were investigated by SERS. The ID/HC classification model was developed by analyzing the SERS spectra of 150 ID and 100 HC, while BID/CRC classification model was built with 300 BID and 150 CRC patients by principal component analysis (PCA)-support vector machines (SVM). The two established models were internally verified by leave-one-out-cross-validation (LOOCV). Finally, the BID/CRC classification model was further evaluated by 143 BID and 137 CRC patients as an external test set. It shows that the accuracy of the classification model validated by the LOOCV for ID/HC and BID/CRC is 86.4% and 85.56%, respectively. And the accuracy of the BID/CRC classification model with external test set is 82.14%. It shows that high accuracy can be achieved with these two established classification models. It indicates that ID patients in the general population can be identified and BID and CRC patients can be further classified with measuring urine by SERS. It shows that the proposed diagnostic method and established classification models provide valuable information for clinicians to early diagnose ID patients and analyze different stages of ID.

Colorectal Cancer Detection via Metabolites and Machine Learning

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

Integrative Metabolomic Analysis of Serum and Selected Serum Exosomal microRNA in Metastatic Castration-Resistant Prostate Cancer

Metastatic castration-resistant prostate cancer (mCRPC) remains a lethal disease due to the absence of effective therapies. A more comprehensive understanding of molecular events, encompassing the dysregulation of microRNAs (miRs) and metabolic reprogramming, holds the potential to unveil precise mechanisms underlying mCRPC. This study aims to assess the expression of selected serum exosomal miRs (miR-15a, miR-16, miR-19a-3p, miR-21, and miR-141a-3p) alongside serum metabolomic profiling and their correlation in patients with mCRPC and benign prostate hyperplasia (BPH). Blood serum samples from mCRPC patients (n = 51) and BPH patients (n = 48) underwent metabolome analysis through 1H-NMR spectroscopy. The expression levels of serum exosomal miRs in mCRPC and BPH patients were evaluated using a quantitative real-time polymerase chain reaction (qRT-PCR). The 1H-NMR metabolomics analysis revealed significant alterations in lactate, acetate, citrate, 3-hydroxybutyrate, and branched-chain amino acids (BCAAs, including valine, leucine, and isoleucine) in mCRPC patients compared to BPH patients. MiR-15a, miR-16, miR-19a-3p, and miR-21 exhibited a downregulation of more than twofold in the mCRPC group. Significant correlations were predominantly observed between lactate, citrate, acetate, and miR-15a, miR-16, miR-19a-3p, and miR-21. The importance of integrating metabolome analysis of serum with selected serum exosomal miRs in mCRPC patients has been confirmed, suggesting their potential utility for distinguishing of mCRPC from BPH.

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

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

Metabolic signatures and potential biomarkers for the diagnosis and treatment of colon cancer cachexia

Cancer cachexia (CAC) is a debilitating condition that often arises from noncachexia cancer (NCAC), with distinct metabolic characteristics and medical treatments. However, the metabolic changes and underlying molecular mechanisms during cachexia progression remain poorly understood. Understanding the progression of CAC is crucial for developing diagnostic approaches to distinguish between CAC and NCAC stages, facilitating appropriate treatment for cancer patients. In this study, we establish a mouse model of colon CAC and categorize the mice into three groups: CAC, NCAC and normal control (NOR). By performing nuclear magnetic resonance (NMR)-based metabolomic profiling on mouse sera, we elucidate the metabolic properties of these groups. Our findings unveil significant differences in the metabolic profiles among the CAC, NCAC and NOR groups, highlighting significant impairments in energy metabolism and amino acid metabolism during cachexia progression. Additionally, we observe the elevated serum levels of lysine and acetate during the transition from the NCAC to CAC stages. Using multivariate ROC analysis, we identify lysine and acetate as potential biomarkers for distinguishing between CAC and NCAC stages. These biomarkers hold promise for the diagnosis of CAC from noncachexia cancer. Our study provides novel insights into the metabolic mechanisms underlying cachexia progression and offers valuable avenues for the diagnosis and treatment of CAC in clinical settings.

Amino Acid Profiles in the Biological Fluids and Tumor Tissue of CRC Patients

Amino acids are the building blocks of proteins and essential players in pathways such as the citric acid and urea cycle, purine and pyrimidine biosynthesis, and redox cell signaling. Therefore, it is unsurprising that these molecules have a significant role in cancer metabolism and its metabolic plasticity. As one of the most prevalent malign diseases, colorectal cancer needs biomarkers for its early detection, prognostic, and prediction of response to therapy. However, the available biomarkers for this disease must be more powerful and present several drawbacks, such as high costs and complex laboratory procedures. Metabolomics has gathered substantial attention in the past two decades as a screening platform to study new metabolites, partly due to the development of techniques, such as mass spectrometry or liquid chromatography, which have become standard practice in diagnostic procedures for other diseases. Extensive metabolomic studies have been performed in colorectal cancer (CRC) patients in the past years, and several exciting results concerning amino acid metabolism have been found. This review aims to gather and present findings concerning alterations in the amino acid plasma pool of colorectal cancer patients.

Metabolomic Signatures of Exposure to Nitrate and Trihalomethanes in Drinking Water and Colorectal Cancer Risk in a Spanish Multicentric Study (MCC-Spain)

We investigated the metabolomic profile associated with exposure to trihalomethanes (THMs) and nitrate in drinking water and with colorectal cancer risk in 296 cases and 295 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.

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.

The association of serum serine levels with the risk of incident cancer: results from a nested case-control study

Background: Cancer is associated with the dysregulation of serum serine levels, and tumor growth is supported by increased serine biosynthesis. This study aims to explore the association of serum serine levels with incident cancer risk in Chinese hypertensive adults. Materials and methods: 1391 patients with incident cancer and 1391 matched controls in terms of age, sex, and residence with cases in a 1 : 1 ratio were included in this nested case-control study. The serum serine concentrations were determined by liquid chromatography with tandem quadrupole mass spectrometry (LC-MS/MS) at the baseline. The associations of serum serine levels with the risk of overall, digestive system, non-digestive system, and lung cancers (the most common type) were assessed by conditional logistic regression. Results: When serum serine concentration was assessed as quartiles, a significantly higher risk of total cancer (OR = 1.32; 95% CI: 1.01-1.71; P = 0.038) was found in participants in the highest quartile (≥17.68 μg mL-1) compared with participants in the lowest quartile (<13.27 μg mL-1). Similar results were also observed for non-digestive system and lung cancers, but not for digestive system cancers. Significant associations of serum with overall cancer risk were found among all age subgroups, men, non-smokers, non-drinkers, and individuals with lower folic acid levels. Conclusion: High serum serine concentrations were associated with an increased risk of overall, non-digestive system, and lung cancers among Chinese hypertensive adult patients.

Feasibility of Raman spectroscopic identification of gall bladder cancer using extracellular vesicles extracted from bile

Extracellular vesicles (EVs), which are heterogeneous membrane-based vesicles with bilayer cell membrane structures, could be versatile biomarkers for the identification of diverse diseases including cancers. With this potential, this study has attempted the Raman spectroscopic identification of gall bladder (GB) cancer by directly measuring the EV solution extracted from human bile without further sample drying. For this purpose, bile samples were obtained from four normal individuals and 21 GB polyp, eight hepatocellular carcinoma (HCC), and five GB cancer patients, and EVs were extracted from each of the bile samples. The Raman peak shapes of the EVs extracted from the GB cancer samples, especially the relative intensities of peaks in the 1560-1340 cm-1 range, were dissimilar to those of the samples from the normal, GB polyp, and HCC groups. The intensity ratios of peaks at 1537 and 1453 cm-1 and at 1395 and 1359 cm-1 of the GB cancer samples were lower and higher, respectively, than those of the samples of the remaining three groups. The differences of peak intensity ratios were statistically significant based on the Mann-Whitney U test. DNA/RNA bases, amino acids, and bile salts contributed to the spectra of EVs, and their relative abundances seemed to vary according to the occurrence of GB cancer. The varied metabolite compositions and/or structures of EVs were successfully demonstrated by the dissimilar peak intensity ratios in the Raman spectra, thereby enabling the discrimination of GB cancer.

Metabolomic analysis of gut metabolites in patients with colorectal cancer: Association with disease development and outcome

Colorectal cancer (CRC) is one of the leading global malignancies with low 5-year survival and high mortality rates. Despite extensive research, the precise role of gut metabolites in CRC development and clinical outcomes remains unclear, while its elucidation may aid the development of improved clinical diagnosis and treatment options. In the present study, targeted metabolomic analysis was conducted on fecal samples from 35 patients with CRC, 37 patients with colorectal adenoma and 30 healthy controls (HC) to identify metabolite biomarkers. Using orthogonal partial least squares discriminant analysis, metabolomic features distinguishing the three groups were identified. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic utility for distinguishing CRC from HC. The association of gut metabolites with survival in patients with CRC was also analyzed by comparing short-term survivors (STS) and long-term survivors (LTS), and the prognostic ability of metabolites was predicted using Cox regression and Kaplan-Meier analysis. The results of the current study showed that the enriched pathways in CRC included 'caffeine metabolism', 'thiamine metabolism', 'phenylalanine, tyrosine and tryptophan biosynthesis' and 'phenylalanine metabolism'. ROC analysis found that 9,10-dihydroxy-12-octadecenoic acid, cholesterol ester (18:2) and lipoxinA4 distinguished CRC from HC. Joint quantification of these three metabolites resulted in an area under the ROC curve of 0.969 in the diagnosis of CRC. The analysis of the current study also showed that the expression of metabolites involved in 'sphingolipid metabolism' was mainly dysregulated in LTS and STS, while N-acetylmannosamine and 2,5-dihydroxybenzaldehyde were associated with better overall survival. In conclusion, the present study provided preliminary insight into the metabolic changes associated with CRC and may have important implications for the development of future diagnostic and treatment strategies.

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.

Plasma metabolomics, lipidomics and cytokinomics profiling predict disease recurrence in metastatic colorectal cancer patients undergoing liver resection

Purpose

In metastatic colorectal cancer (mCRC) patients (pts), treatment strategies integrating liver resection with induction chemotherapy offer better 5-year survival rates than chemotherapy alone. However, liver resection is a complex and costly procedure, and recurrence occurs in almost 2/3rds of pts, suggesting the need to identify those at higher risk. The aim of this work was to evaluate whether the integration of plasma metabolomics and lipidomics combined with the multiplex analysis of a large panel of plasma cytokines can be used to predict the risk of relapse and other patient outcomes after liver surgery, beyond or in combination with clinical morphovolumetric criteria.

Experimental design

Peripheral blood metabolomics and lipidomics were performed by 600 MHz NMR spectroscopy on plasma from 30 unresectable mCRC pts treated with bevacizumab plus oxaliplatin-based regimens within the Obelics trial (NCT01718873) and subdivided into responder (R) and non-R (NR) according to 1-year disease-free survival (DFS): ≥ 1-year (R, n = 12) and < 1-year (NR, n = 18). A large panel of cytokines, chemokines, and growth factors was evaluated on the same plasma using Luminex xMAP-based multiplex bead-based immunoassay technology. A multiple biomarkers model was built using a support vector machine (SVM) classifier.

Results

Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing metabolomics profiles of samples collected at the time of response evaluation when resectability was established showed significantly different levels of metabolites between the two groups. Two metabolites, 3-hydroxybutyrate and histidine, significantly predicted DFS and overall survival. Lipidomics analysis confirmed clear differences between the R and NR pts, indicating a statistically significant increase in lipids (cholesterol, triglycerides and phospholipids) in NR pts, reflecting a nonspecific inflammatory response. Indeed, a significant increase in proinflammatory cytokines was demonstrated in NR pts plasma. Finally, a multiple biomarkers model based on the combination of presurgery plasma levels of 3-hydroxybutyrate, cholesterol, phospholipids, triglycerides and IL-6 was able to correctly classify patients by their DFS with good accuracy.

Conclusion

Overall, this exploratory study suggests the potential of these combined biomarker approaches to predict outcomes in mCRC patients who are candidates for liver metastasis resection after induction treatment for defining personalized management and treatment strategies.

Serum metabolite signatures in normal individuals and patients with colorectal adenoma or colorectal cancer using UPLC-MS/MS method

Colorectal cancer (CRC) is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenoma to adenocarcinoma, which provides a long screening window for clinical detection. However, early diagnosis of sporadic colorectal adenoma (CRA) and CRC using serum metabolic screening remains unclear. The purpose of this study was to identify some promising signatures for distinguishing the different pathological metabolites of colorectal mucosal malignant transformation. A total of 238 endogenous metabolites were elected. We found that CRA and CRC patients had 72 and 73 different metabolites compared with healthy controls, respectively. There were 20 different metabolites between CRA and CRC patients. The potential metabolites of tumor growth (including patients with CRA and CRC) were found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, Sarcosine, TXB 2, 12-Hete, and chenodeoxycholic acid. Compared with CRA, 3,4,5-trimethoxybenzoic acid was significantly higher in CRC patients. There results prompt us to use the potential serum signatures to screen CRC as the novel strategy. Serum metabolite screening is useful for early detection of mucosal intestinal malignancy. We will further investigate the roles of these promising biomarkers during intestinal tumorigenesis in future. SIGNIFICANCE: CRC is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenomas to adenocarcinoma, which provides a long screening window for about 5-10 years. We adopt the metabolic analysis of extensive targeted metabolic technology. The main purpose of the metabolic group analysis is to detect and screen the different metabolites, thereby performing related functional prediction and analysis of the differential metabolites. In our study, 30 samples are selected, divided into 3 groups for metabolic analysis, and 238 metabolites are elected. In 238 metabolites, we find that CRA patients have 72 different metabolites compared with health control. Compared with health control, CRC have 73 different metabolites. Compared with CRA and CRC patients, there are 20 different metabolites. The annotation results of the significantly different metabolites are classified according to the KEGG pathway type. The potential metabolites of tumor growth stage (including patients with CRA and CRC) are found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, sarcosine, TXB 2, 12-Hete and chenodeoxycholic acid. Compared with CRA patients, CRC patients had significantly higher 3,4,5-trimethoxybenzoic acid level. It is prompted to use serum different metabolites to screen CRC to provide new possibilities.

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.

Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives

Colorectal cancer (CRC) is common Cancer as well as the third leading cause of mortality around the world; its exact molecular mechanism remains elusive. Although CRC risk is significantly correlated with genetic factors, the pathophysiology of CRC is also influenced by external and internal exposures and their interactions with genetic factors. The field of CRC research has recently benefited from significant advances through Omics technologies for screening biomarkers, including genes, transcripts, proteins, metabolites, microbiome, and lipidome unbiasedly. A promising application of omics technologies could enable new biomarkers to be found for the screening and diagnosis of CRC. Single-omics technologies cannot fully understand the molecular mechanisms of CRC. Therefore, this review article aims to summarize the multi-omics studies of Colorectal cancer, including genomics, transcriptomics, proteomics, microbiomics, metabolomics, and lipidomics that may shed new light on the discovery of novel biomarkers. It can contribute to identifying and validating new CRC biomarkers and better understanding colorectal carcinogenesis. Discovering biomarkers through multi-omics technologies could be difficult but valuable for disease genotyping and phenotyping. That can provide a better knowledge of CRC prognosis, diagnosis, and treatments.

Systematic evaluation of irinotecan-induced intestinal mucositis based on metabolomics analysis

Chemotherapy-induced intestinal mucositis (CIM) is a major dose-limiting side effect of chemotherapy, especially in regimens containing irinotecan (CPT-11). Several studies on the pathologic mechanisms of CIM focused on both the genomics and molecular pathways triggered by chemotherapy. However, systematic evaluation of metabolomic analysis in irinotecan-induced intestinal mucositis (IIM) has not been investigated. This study aimed to comprehensively analyze metabolite changes in main tissues of IIM mouse models. Male ICR mice were assigned to two groups: the model group (n = 11) treated with CPT-11 (20 mg/kg daily; i.p.) and the control group (n= 11) with solvent for 9 days. Gas chromatography-mass spectrometry (GC-MS) was used to investigate the metabolic alterations in the serum, intestinal, colonic, hepatic, and splenic samples of mice between two groups by multivariate statistical analyses, including GC–MS data processing, pattern recognition analysis, and pathway analysis. Forty-six metabolites, including hydrocarbons, amino acids, lipids, benzenoids, hydroxy acids, and amines, had significant changes in levels in tissues and sera of IIM mouse models. The most important pathways related to the identified metabolites were the glycerolipid metabolism in the colon and aminoacyl-tRNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism in the liver. Our study firstly provided a comprehensive and systematic view of metabolic alterations of IIM using GC-MS analysis. The characterizations of metabolic changes could offer profound and theoretical insight into exploring new biomarkers for diagnosis and treatment of IIM.

Serum Serine and the Risk of All-Cause Mortality: A Nested Case-Control Study From the China Stroke Primary Prevention Trial (CSPPT)

Background

Serine plays a key role in numerous cellular processes, the levels and metabolism is therefore of critical importance. However, few data are available to illustrate the association of serine with long-term health effects, especially, the predictive value for long-term mortality.

Objective

This study was conducted to evaluate the relationship between serum serine levels and all-cause mortality in general hypertensive patients in a longitudinal cohort, and to examine the potential effect modifiers.

Methods

A nested case-control (NCC) study was conducted utilizing 20702 hypertensive participants from the China Stroke Primary Prevention Trial (CSPPT), a randomized, double-blind, actively controlled trial conducted from May 2008 to August 2013 in China. The current study included 291 cases of all-cause mortality and 291 controls matched on age (≤ 1 year), sex and treatment group. All-cause mortality was the main outcome in this analysis, which included death due to any reason.

Results

With the increase in serum serine levels, the risk of all-cause mortality first increased before flattening. After adjusting for related variables, the risk of mortality increased significantly with the increase of serum serine levels. Compared with group Q1, the mortality risk of group Q2, Q3 and Q4 were significantly increased [ORs, 95% CI: Q2: 2.32, (1.32–4.07); Q3: 2.59, (1.48–4.54); and Q4: 1.85, (1.07–3.22)]. In the exploratory analysis, we observed three effect modifiers, total homocysteine, 5-Methyltetrahydrofolate, and estimated glomerular filtration rate significantly modified the serum serine and all-cause mortality association.

Conclusion

Serum serine levels were significantly associated with an increased risk of all-cause mortality in hypertensive patients. Our results and findings, if confirmed further, suggest that serum serine should be considered as a marker for screening risk factors of mortality.

Clinical Trial Registration

[https://www.clinicaltrials.gov/ct2/show/study/NCT00794885.], identifier [CSPPT, NCT00794885].

The Association between Serum Serine and Glycine and Related-Metabolites with Pancreatic Cancer in a Prospective Cohort Study

Background. Serine and glycine play an important role in the folate-dependent one-carbon metabolism. The metabolism of serine and glycine has been shown to be associated with cancer cell proliferation. No prior epidemiologic study has investigated the associations for serum levels of serine and glycine with pancreatic cancer risk. Methods. We conducted a nested case-control study involved 129 incident pancreatic cancer cases and 258 individually matched controls within a prospective cohort study of 18,244 male residents in Shanghai, China. Glycine and serine and related metabolites in pre-diagnostic serum were quantified using gas chromatography-tandem mass spectrometry. A conditional logistic regression method was used to evaluate the associations for serine, glycine, and related metabolites with pancreatic cancer risk with adjustment for potential confounders. Results: Odds ratios (95% confidence intervals) of pancreatic cancer for the highest quartile of serine and glycine were 0.33 (0.14−0.75) and 0.25 (0.11−0.58), respectively, compared with their respective lowest quartiles (both p’s < 0.01). No significant association with risk of pancreatic cancer was observed for other serine- or glycine related metabolites including cystathionine, cysteine, and sarcosine. Conclusion. The risk of pancreatic cancer was reduced by more than 70% in individuals with elevated levels of glycine and serine in serum collected, on average, more than 10 years prior to cancer diagnosis in a prospectively designed case-control study. These novel findings support a protective role of serine and glycine against the development of pancreatic cancer in humans that might have an implication for cancer prevention.

Distinct Urinary Metabolic Biomarkers of Human Colorectal Cancer

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers with high mortality rate due to its poor diagnosis in the early stage. Here, we report a urinary metabolomic study on a cohort of CRC patients (n =67) and healthy controls (n =21) using ultraperformance liquid chromatography triple quadrupole mass spectrometry. Pathway analysis showed that a series of pathways that belong to amino acid metabolism, carbohydrate metabolism, and lipid metabolism were dysregulated, for instance the glycine, serine and threonine metabolism, alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, glycolysis, and TCA cycle. A total of 48 differential metabolites were identified in CRC compared to controls. A panel of 12 biomarkers composed of chenodeoxycholic acid, vanillic acid, adenosine monophosphate, glycolic acid, histidine, azelaic acid, hydroxypropionic acid, glycine, 3,4-dihydroxymandelic acid, 4-hydroxybenzoic acid, oxoglutaric acid, and homocitrulline were identified by Random Forest (RF), Support Vector Machine (SVM), and Boruta analysis classification model and validated by Gradient Boosting (GB), Logistic Regression (LR), and Random Forest diagnostic model, which were able to discriminate CRC subjects from healthy controls. These urinary metabolic biomarkers provided a novel and promising molecular approach for the early diagnosis of CRC.

Metabolomic Biomarkers in Blood Samples Identify Cancers in a Mixed Population of Patients with Nonspecific Symptoms

PURPOSE

Early diagnosis of cancer is critical for improving patient outcomes, but cancers may be hard to diagnose if patients present with nonspecific signs and symptoms. We have previously shown that nuclear magnetic resonance (NMR) metabolomics analysis can detect cancer in animal models and distinguish between differing metastatic disease burdens. Here, we hypothesized that biomarkers within the blood metabolome could identify cancers within a mixed population of patients referred from primary care with nonspecific symptoms, the so-called "low-risk, but not no-risk" patient group, as well as distinguishing between those with and without metastatic disease.

EXPERIMENTAL DESIGN

Patients (n = 304 comprising modeling, n = 192, and test, n = 92) were recruited from 2017 to 2018 from the Oxfordshire Suspected CANcer (SCAN) pathway, a multidisciplinary diagnostic center (MDC) referral pathway for patients with nonspecific signs and symptoms. Blood was collected and analyzed by NMR metabolomics. Orthogonal partial least squares discriminatory analysis (OPLS-DA) models separated patients, based upon diagnoses received from the MDC assessment, within 62 days of initial appointment.

RESULTS

Area under the ROC curve for identifying patients with solid tumors in the independent test set was 0.83 [95% confidence interval (CI): 0.72-0.95]. Maximum sensitivity and specificity were 94% (95% CI: 73-99) and 82% (95% CI: 75-87), respectively. We could also identify patients with metastatic disease in the cohort of patients with cancer with sensitivity and specificity of 94% (95% CI: 72-99) and 88% (95% CI: 53-98), respectively.

CONCLUSIONS

For a mixed group of patients referred from primary care with nonspecific signs and symptoms, NMR-based metabolomics can assist their diagnosis, and may differentiate both those with malignancies and those with and without metastatic disease. See related commentary by Van Tine and Lyssiotis, p. 1477.

Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers

Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.

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.

Changing Metabolic Patterns along the Colorectal Adenoma–Carcinoma Sequence

Colorectal cancer (CRC) is a major public health burden and one of the leading causes of cancer-related deaths worldwide. Screening programs facilitate early diagnosis and can help to reduce poor outcomes. Serum metabolomics can extract vital molecular information that may increase the sensitivity and specificity of colonoscopy in combination with histopathological examination. The present study identifies serum metabolite patterns of treatment-naïve patients, diagnosed with either advanced adenoma (AA) or CRC in colonoscopy screenings, in the framework of the SAKKOPI (Salzburg Colon Cancer Prevention Initiative) program. We used a targeted flow injection analysis and liquid chromatography-tandem mass spectrometry metabolomics approach (FIA- and LC-MS/MS) to characterise the serum metabolomes of an initial screening cohort and two validation cohorts (in total 66 CRC, 76 AA and 93 controls). The lipidome was significantly perturbed, with a proportion of lipid species being downregulated in CRC patients, as compared to AA and controls. The predominant alterations observed were in the levels of lyso-lipids, glycerophosphocholines and acylcarnitines, but additionally, variations in the quantity of hydroxylated sphingolipids could be detected. Changed amino acid metabolism was restricted mainly to metabolites of the arginine/dimethylarginine/NO synthase pathway. The identified metabolic divergences observed in CRC set the foundation for mechanistic studies to characterise biochemical pathways that become deregulated during progression through the adenoma to carcinoma sequence and highlight the key importance of lipid metabolites. Biomarkers related to these pathways could improve the sensitivity and specificity of diagnosis, as well as the monitoring of therapies.

A Metabolomics-Based Screening Proposal for Colorectal Cancer

Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.

Metabolomic Profiling Identified Serum Metabolite Biomarkers and Related Metabolic Pathways of Colorectal Cancer

Background

The screening and early detection of colorectal cancer (CRC) still remain a challenge due to the lack of reliable and effective serum biomarkers. Thus, this study is aimed at identifying serum biomarkers of CRC that could be used to distinguish CRC from healthy controls.

Methods

A prospective 1 : 2 individual matching case-control study was performed which included 50 healthy control subjects and 98 CRC patients. Untargeted metabolomic profiling was conducted with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify CRC-related metabolites and metabolic pathways.

Results

In total, 178 metabolites were detected, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish CRC patients from healthy controls. Nine metabolites showed significantly differential serum levels in CRC patients under the conditions of variable importance in projection (VIP) > 1, p < 0.05 using Student's t-test, and fold change (FC) ≥ 1.2 or ≤0.5. The above nine metabolites were 3-hydroxybutyric acid, hexadecanedioic acid, succinic acid semialdehyde, 4-dodecylbenzenesulfonic acid, prostaglandin B2, 2-pyrocatechuic acid, xanthoxylin, 12-hydroxydodecanoic acid, and formylanthranilic acid. Four potential biomarkers were identified to diagnose CRC through ROC curves: hexadecanedioic acid, 4-dodecylbenzenesulfonic acid, 2-pyrocatechuic acid, and formylanthranilic acid. All AUC values of these four serum biomarkers were above 0.70. In addition, the exploratory analysis of metabolic pathways revealed the activated states for the vitamin B metabolic pathway and the alanine, aspartate, and glutamate metabolic pathways associated with CRC.

Conclusion

The 4 identified potential metabolic biomarkers could discriminate CRC patients from healthy controls, and the 2 metabolic pathways may be activated in the CRC tissues.

NMR-Based Metabolomics Analysis Predicts Response to Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most fatal type of breast cancer (BC). Due to the lack of relevant targeted drug therapy, in addition to surgery, chemotherapy is still the most common treatment option for TNBC. TNBC is heterogeneous, and different patients have an unusual sensitivity to chemotherapy. Only part of the patients will benefit from chemotherapy, so neoadjuvant chemotherapy (NAC) is controversial in the treatment of TNBC. Here, we performed an NMR spectroscopy–based metabolomics study to analyze the relationship between the patients’ metabolic phenotypes and chemotherapy sensitivity in the serum samples. Metabolic phenotypes from patients with pathological partial response, pathological complete response, and pathological stable disease (pPR, pCR, and pSD) could be distinguished. Furthermore, we conducted metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with three groups of TNBC patients. We evaluated the discriminative ability of metabolites related to significantly disturbed metabolic pathways by using the multi-receiver–operating characteristic (ROC) curve analysis. Three significantly disturbed metabolic pathways of glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism could be used as potential predictive models to distinguish three types of TNBC patients. These results indicate that a metabolic phenotype could be used to predict whether a patient is suitable for NAC. Metabolomics research could provide data in support of metabolic phenotypes for personalized treatment of TNBC.

Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps

BACKGROUND

Colorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors.

AIM

To characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP).

METHODS

Proton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index.

RESULTS

NMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified.

CONCLUSION

In conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments.

Whole-exome sequencing of rectal cancer identifies locally recurrent mutations in the Wnt pathway

Locally recurrent rectal cancer (LRRC) leads to a poor prognosis and appears as a clinically predominant pattern of failure. In this research, whole-exome sequencing (WES) was performed on 21 samples from 8 patients to search for the molecular mechanisms of LRRC. The data was analyzed by bioinformatics. Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) were performed to validate the candidate genes. Immunohistochemistry was used to detect the protein expression of LEF1 and CyclinD1 in LRRC, primary rectal cancer (PRC), and non-recurrent rectal cancer (NRRC) specimens. The results showed that LRRC, PRC, and NRRC had 668, 794, and 190 specific genes, respectively.

FGFR1 and MYC have copy number variants (CNVs) in PRC and LRRC, respectively. LRRC specific genes were mainly enriched in positive regulation of transcription from RNA polymerase II promoter, plasma membrane, and ATP binding. The specific signaling pathways of LRRC were Wnt signaling pathway, gap junction, and glucagon signaling pathway, etc. The transcriptional and translational expression levels of genes including NFATC1, PRICKLE1, SOX17, and WNT6 related to Wnt signaling pathway were higher in rectal cancer (READ) tissues than normal rectal tissues. The PRICKLE1 mutation (c.C875T) and WNT6 mutation (c.G629A) were predicted as “D (deleterious)”. Expression levels of LEF1 and cytokinin D1 proteins: LRRC > PRC > NRRC > normal rectal tissue. Gene variants in the Wnt signaling pathway may be critical for the development of LRRC. The present study may provide a basis for the prediction of LRRC and the development of new therapeutic drugs.

Systems-level biomarkers identification and drug repositioning in colorectal cancer

Colorectal cancer (CRC) is the most commonly diagnosed fatal cancer in both women and men worldwide. CRC ranked second in mortality and third in incidence in 2020. It is difficult to diagnose CRC at an early stage as there are no clinical symptoms. Despite advances in molecular biology, only a limited number of biomarkers have been translated into routine clinical practice to predict risk, prognosis and response to treatment. In the last decades, systems biology approaches at the omics level have gained importance. Over the years, several biomarkers for CRC have been discovered in terms of disease diagnosis and prognosis. On the other hand, a few drugs are being developed and used in clinics for the treatment of CRC. However, the development of new drugs is very costly and time-consuming as the research and development takes about 10 years and more than $1 billion. Therefore, drug repositioning (DR) could save time and money by establishing new indications for existing drugs. In this review, we aim to provide an overview of biomarkers for the diagnosis and prognosis of CRC from the systems biology perspective and insights into DR approaches for the prevention or treatment of CRC.

Effect of supplementing fava bean (Vicia faba L.) on ulcerative colitis and colonic mucosal DNA content in rats fed a high-sucrose diet

Ulcerative colitis (UC) is an inflammatory bowel disease with high morbidity. Acetic acid-induced damage of colonic mucosa in rats is a commonly used experimental animal model of UC. This research aimed to explore for the first time the ameliorative effect of dietary supplementation with fava bean on the incidence of UC in rats fed with sucrose containing diet. Rats were divided into five groups as follows: G1, control healthy rats; G2, colitic rats; G3, colitic rats fed diets containing 30% sucrose, G4, healthy rats fed diets containing 30% sucrose and G5, colitic rats fed diets containing 30% sucrose supplemented with dried ground fava bean. Colonic injury and inflammation were evaluated through a disturbance of oxidative biomarkers, a significant increase in inflammatory biomarkers and inflammatory cytokines, and histological abnormalities in colonic tissues accompanied by colonic mucosal DNA damage. Colitic rats fed on sucrose containing diet demonstrated additional histological, biochemical, and DNA alterations in colonic mucosa of rats. Dietary supplementation with dried ground fava bean significantly corrected the impaired oxidative and inflammatory biomarker levels and modulated histological features and DNA alterations. Finally, fava bean attenuated the oxidative damage and colonic injury induced by acetic acid, which confirmed its high anti-oxidant and anti-incendiary properties.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

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.

Untargeted Metabolomics Reveals Major Differences in the Plasma Metabolome between Colorectal Cancer and Colorectal Adenomas

Sporadic colorectal cancer is characterized by a multistep progression from normal epithelium to precancerous low-risk and high-risk adenomas to invasive cancer. Yet, the underlying molecular mechanisms of colorectal carcinogenesis are not completely understood. Within the "Metabolomic profiles throughout the continuum of colorectal cancer" (MetaboCCC) consortium we analyzed data generated by untargeted, mass spectrometry-based metabolomics using plasma from 88 colorectal cancer patients, 200 patients with high-risk adenomas and 200 patients with low-risk adenomas recruited within the "Colorectal Cancer Study of Austria" (CORSA). Univariate logistic regression models comparing colorectal cancer to adenomas resulted in 442 statistically significant molecular features. Metabolites discriminating colorectal cancer patients from those with adenomas in our dataset included acylcarnitines, caffeine, amino acids, glycerophospholipids, fatty acids, bilirubin, bile acids and bacterial metabolites of tryptophan. The data obtained discovers metabolite profiles reflecting metabolic differences between colorectal cancer and colorectal adenomas and delineates a potentially underlying biological interpretation.

Colorectal cancer screening and diagnosis: omics-based technologies for development of a non-invasive blood-based method

Introduction: Colorectal cancer (CRC) is one of the most important health problems in the Western world. In order to reduce the burden of the disease, two strategies are proposed: screening and prompt detection in symptomatic patients. Although diagnosis and prevention are mainly based on colonoscopy, fecal hemoglobin detection has been widely implemented as a noninvasive strategy. Various studies aiming to discover blood-based biomarkers have recently emerged.Areas covered: The burgeoning omics field provides diverse high-throughput approaches for CRC blood-based biomarker discovery. In this review, we appraise the most robust and commonly used technologies within the fields of genomics, transcriptomics, epigenomics, proteomics, and metabolomics, together with their targeted validation approaches. We summarize the transference process from the discovery phase until clinical translation. Finally, we review the best candidate biomarkers and their potential clinical applicability.Expert opinion: Some available biomarkers are promising, especially in the field of epigenomics: DNA methylation and microRNA. Transference requires the joint collaboration of basic researchers, intellectual property experts, technology transfer officers and clinicians. Blood-based biomarkers will be selected not only based on their diagnostic accuracy and cost but also on their reliability, applicability to clinical analysis laboratories and their acceptance by the population.

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.

Omics technologies for improved diagnosis and treatment of colorectal cancer: Technical advancement and major perspectives

Colorectal cancer (CRC) ranks third among the most commonly occurring cancers worldwide, and it causes half a million deaths annually. Alongside mechanistic study for CRC detection and treatment by conventional techniques, new technologies have been developed to study CRC. These technologies include genomics, transcriptomics, proteomics, and metabolomics which elucidate DNA markers, RNA transcripts, protein and, metabolites produced inside the colon and rectum part of the gut. All these approaches form the omics arena, which presents a remarkable opportunity for the discovery of novel prognostic, diagnostic and therapeutic biomarkers and also delineate the underlying mechanism of CRC causation, which may further help in devising treatment strategies. This review also mentions the latest developments in metagenomics and culturomics as emerging evidence suggests that metagenomics of gut microbiota has profound implications in the causation, prognosis, and treatment of CRC. A majority of bacteria cannot be studied as they remain unculturable, so culturomics has also been strengthened to develop culture conditions suitable for the growth of unculturable bacteria and identify unknown bacteria. The overall purpose of this review is to succinctly evaluate the application of omics technologies in colorectal cancer research for improving the diagnosis and treatment strategies.

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.

Metabolomics profile in gastrointestinal cancers: Update and future perspectives

Despite recent progress in diagnosis and therapy, gastrointestinal (GI) cancers remain one of the most important causes of death with a poor prognosis due to late diagnosis. Serum tumor markers and detection of occult blood in the stool are the current tests used in the clinic of GI cancers; however, these tests are not useful as diagnostic screening since they have low specificity and low sensitivity. Considering that one of the hallmarks of cancer is dysregulated metabolism and metabolomics is an optimal approach to illustrate the metabolic mechanisms that belong to living systems, is now clear that this -omics could open a new way to study cancer. In the last years, nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for diseases' diagnosis nevertheless a few studies focus on the NMR capability to find new biomarkers for early diagnosis of GI cancers. For these reasons in this review, we will give an update on the status of NMR metabolomic studies for the diagnosis and development of GI cancers using biological fluids.

Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

The Role of Circulating CD16+CD56+ Natural Killer Cells in the Screening, Diagnosis, and Staging of Colorectal Cancer before Initial Treatment

Background and Objective

A reliable noninvasive prediction tool for the screening, diagnosis, and/or staging of colorectal cancer (CRC) before surgery is critical for the choice of treatment and prognosis.

Methods

Patients admitted for initial treatment of CRC between January 1, 2015, and December 31, 2018, were retrieved and reviewed. Records of CD16+CD56+ natural killer (NK) cells were analyzed according to the stages of CRC.

Results

The number of qualified participants in the healthy, stage I, stage II, stage III, and stage IV CRC patients were 60, 66, 60, 70, and 68, respectively. There was a significant difference in circulating CD16+CD56+ NK cells between the healthy group and the CRC group (p < 0.01), as well as between the healthy group and stage III or IV CRC group (p < 0.01 and 0.001, respectively). The percentage of circulating CD16+CD56+ NK cells in lymphocytes was negatively correlated with the occurrence of CRC. When comparing the pool of stage I and II CRC cases with the pool of stage III and IV CRC cases using circulating CD16+CD56+ NK cells, the area under the Receiver Operating Characteristic curve was 0.878. Using an optimal cutoff value of 15.6%, the OR was 0.06 (0.03, 0.11), p < 0.001, sensitivity was 86.5%, specificity was 72.5%, positive predictive value was 74.2%, and negative predictive value was 85.5%.

Conclusions

Circulating CD16+CD56+ NK cells can be used as a screening and diagnostic/staging tool for CRC.

Circulating CD16+CD56+ nature killer cells indicate the prognosis of colorectal cancer after initial chemotherapy

As the prognosis of colorectal cancer (CRC) does not always coincide with the pathology and/or surgical findings, a reliable noninvasive prediction tool for the prognosis of CRC is needed. Patients admitted for initial treatment of CRC between January 1, 2015 and December 31, 2015 were retrieved and reviewed. Records of circulating CD16+ CD56+ natural killer (NK) cells were analyzed before and after the initial chemotherapy of FOLFOX plan. Patients were followed up until June 30, 2019. One hundred and twenty-four cases after the FOLFOX chemotherapy were enrolled into this study. There were no significant differences in gender, age, or number of metastasis cases between the survival group and the nonsurvival group (p > 0.05), but significant differences in pre-chemotherapy, post-chemotherapy, and the differences between pre- and post-chemotherapy circulating CD16+ CD56+ NK cells between the survival group and the nonsurvival group (p < 0.01, p < 0.01, and p < 0.05, respectively) were observed. For the prediction of survival and nonsurvival CRC cases, the Areas Under the Curve were 0.626 and 0.759 in the Receiver-Operating Characteristic curves for the pre- and post-chemotherapy circulating CD16+ CD56+NK cells, respectively. Using an optimal cutoff value of 11.8% in post-chemotherapy circulating CD16+CD56+NK cells to differentiate survival and nonsurvival cases, the odds ratio was 0.12 (0.05, 0.27), p < 0.001. The percentages of both pre-chemotherapy and post-chemotherapy circulating CD16+CD56+NK cells were negatively correlated with the prognosis of CRC. The percentage of post-chemotherapy circulating CD16+CD56+NK cells was able to effectively predict the prognosis of CRC cases.