Global serum metabolomics profiling of colorectal cancer

Untargeted metabolomics in gastric and colorectal cancer patients - preliminary results

Introduction

Recent years, microbiota-associated aspects have been analysed in multiple disorders regarding cancers. Existing evidence pints that gut microorganisms might take part in tumour origin and therapy efficacy. Nevertheless, to date, data on faecal metabolomics in cancer patients is still strongly limited. Therefore, we aimed to analyse gut untargeted metabolome in gastrointestinal cancer patients (i.e., gastric and colorectal cancer).

Patients and methods

There were 12 patients with either gastric (n=4) or colorectal cancer (n=8) enrolled and 8 analysed (n=4 each). Stool samples were collected prior to anti-cancer treatments. Untargeted metabolomics analyses were conducted by means of mass spectrometry.

Results

A plethora of metabolites in cancer patients we analysed were noted, with higher homogenity in case of gastric cancer patients. We found that the level of Deoxyguanosine,m/z 266.091,[M-H]-, Uridine,m/z 245.075,[M+H]+, Deoxyguanosine,m/z 268.104,[M]+, 3-Indoleacetic acid,m/z 176.07,[M+H]+, Indoxyl,m/z 132.031,[M-H]-, L-Phenylalanine,m/z 164.073,[M-H]-, L-Methionine,m/z 150.058,[M+NH4]+, was significantly higher in colorectal cancer patients and Ethyl hydrogen malonate,m/z 133.031,[M+H]+ in gastric cancer.

Conclusion

The overall insights into untargeted metabolomics showed that most often higher levels of analysed metabolites were detected in colorectal cancer patients compared to gastric cancer patients. The link between gut metabolome and both local and distal metastasis might exist, however it requires confirmation in further multi-centre studies regarding larger sample size.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

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.

Clinical efficacy and metabolomic analysis of ozone major autohemotherapy for the treatment of herpes zoster

Introduction

It is essential to understand the underlying changes in the patients' metabolic profiles that may be indicative of the therapy's effectiveness.

Aim

To prospectively analyse the clinical efficacy of ozone autohemotherapy in the treatment of acute herpes zoster and investigate its impact on serum metabolomics.

Material and methods

A total of 76 patients with acute herpes zoster between May 2018 and June 2020 were enrolled and divided into an experimental group and a control group. The pain location, Numeric Rating Scale (NRS) scores before and after treatment (1 week, 1 month, 3 months, and 6 months post-treatment), medication usage, and Quality of Sleep (QS) scores were prospectively analysed. Additionally, serum metabolomic data were obtained and analysed before and 6 months after the treatment.

Results

There were statistically significant differences in the total NRS scores before and after ozone autohemotherapy (p < 0.05). The NRS scores of both groups significantly decreased (p < 0.05). At the 6-month follow-up, no patients were lost, and 83 patients completed the follow-up. The NRS improvement at 1 week, 1 month, 3 months, and 6 months post-treatment in the experimental group was significantly lower than that in the control group (p < 0.05). There was no significant difference in the medication usage (pregabalin or tramadol sustained-release tablets) between the two groups (p > 0.05). One month after treatment, the QS score improvement in the diabetes group was significantly lower than that in the non-diabetes group (p < 0.05). Serum metabolomics analysis revealed three significantly decreased metabolites, namely creatine, adipate, and glucose, after treatment.

Conclusions

Ozone autohemotherapy is an effective treatment for acute herpes zoster patients and can rapidly and effectively alleviate pain symptoms in the short term. The changes in serum metabolomics may provide further insights into the treatment mechanism.

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.

Epstein-Barr Virus-Encoded MicroRNA-BART18-3p Promotes Colorectal Cancer Progression by Targeting De Novo Lipogenesis

The Epstein-Barr virus (EBV) genome encodes a cluster of 22 viral microRNAs, called miR-BamHI-A rightward transcripts (miR-BARTs), which are shown to promote the development of cancer. Here, this study reports that EBV-miR-BART18-3p is highly expressed in colorectal cancer (CRC) and is closely associated with the pathological and advanced clinical stages of CRC. Ectopic expression of EBV-miR-BART18-3p leads to increased migration and invasion capacities of CRC cells in vitro and causes tumor metastasis in vivo. Mechanistically, EBV-miR-BART18-3p activates the hypoxia inducible factor 1 subunit alpha/lactate dehydrogenase A axis by targeting Sirtuin, which promotes lactate accumulation and acetyl-CoA production in CRC cells under hypoxic condition. Increased acetyl-CoA utilization subsequently leads to histone acetylation of fatty acid synthase and fatty acid synthase-dependent fat synthesis, which in turn drives de novo lipogenesis. The oncogenic role of EBV-miR-BART18-3p is confirmed in the patient-derived tumor xenograft mouse model. Altogether, the findings define a novel mechanism of EBV-miR-BART18-3p in CRC development through the lipogenesis pathway and provide a potential clinical intervention target for CRC.

Joint Transcriptome and Metabolome Analysis Prevails the Biological Mechanisms Underlying the Pro-Survival Fight in In Vitro Heat-Stressed Granulosa Cells

Previous studies reported the physical, transcriptome, and metabolome changes in in vitro acute heat-stressed (38 °C versus 43 °C for 2 h) bovine granulosa cells. Granulosa cells exhibited transient proliferation senescence, oxidative stress, an increased rate of apoptosis, and a decline in steroidogenic activity. In this study, we performed a joint integration and network analysis of metabolomic and transcriptomic data to further narrow down and elucidate the role of differentially expressed genes, important metabolites, and relevant cellular and metabolic pathways in acute heat-stressed granulosa cells. Among the significant (raw p-value < 0.05) metabolic pathways where metabolites and genes converged, this study found vitamin B6 metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism, arginine biosynthesis, tryptophan metabolism, arginine and proline metabolism, histidine metabolism, and glyoxylate and dicarboxylate metabolism. Important significant convergent biological pathways included ABC transporters and protein digestion and absorption, while functional signaling pathways included cAMP, mTOR, and AMPK signaling pathways together with the ovarian steroidogenesis pathway. Among the cancer pathways, the most important pathway was the central carbon metabolism in cancer. Through multiple analysis queries, progesterone, serotonin, citric acid, pyridoxal, L-lysine, succinic acid, L-glutamine, L-leucine, L-threonine, L-tyrosine, vitamin B6, choline, and CYP1B1, MAOB, VEGFA, WNT11, AOX1, ADCY2, ICAM1, PYGM, SLC2A4, SLC16A3, HSD11B2, and NOS2 appeared to be important enriched metabolites and genes, respectively. These genes, metabolites, and metabolic, cellular, and cell signaling pathways comprehensively elucidate the mechanisms underlying the intricate fight between death and survival in acute heat-stressed bovine granulosa cells and essentially help further our understanding (and will help the future quest) of research in this direction.

The potential of fecal microbiota and amino acids to detect and monitor patients with adenoma

The risk of recurrent dysplastic colonic lesions is increased following polypectomy. Yield of endoscopic surveillance after adenoma removal is low, while interval colorectal cancers occur. To longitudinally assess the dynamics of fecal microbiota and amino acids in the presence of adenomatous lesions and after their endoscopic removal. In this longitudinal case-control study, patients collected fecal samples prior to bowel preparation before scheduled colonoscopy and 3 months after this intervention. Based on colonoscopy outcomes, patients with advanced adenomas and nonadvanced adenomas (0.5-1.0 cm) who underwent polypectomy during endoscopy (n = 19) were strictly matched on age, body-mass index, and smoking habits to controls without endoscopic abnormalities (n = 19). Microbial taxa were measured by 16S RNA sequencing, and amino acids (AA) were measured by high-performance liquid chromatography (HPLC). Adenoma patients were discriminated from controls based on AA and microbial composition. Levels of proline (p = .001), ornithine (p = .02) and serine (p = .02) were increased in adenoma patients compared to controls but decreased to resemble those of controls after adenoma removal. These AAs were combined as a potential adenoma-specific panel (AUC 0.79(0.64-0.94)). For bacterial taxa, differences between patients with adenomas and controls were found (Bifidobacterium spp.↓, Anaerostipes spp.↓, Butyricimonas spp.↑, Faecalitalea spp.↑ and Catenibacterium spp.↑), but no alterations in relative abundance were observed after polypectomy. Furthermore, Faecalitalea spp. and Butyricimonas spp. were significantly correlated with adenoma-specific amino acids. We selected an amino acid panel specifically increased in the presence of adenomas and a microbial signature present in adenoma patients, irrespective of polypectomy. Upon validation, these panels may improve the effectiveness of the surveillance program by detection of high-risk individuals and determination of surveillance endoscopy timing, leading to less unnecessary endoscopies and less interval cancer.

Serum Metabolomic Patterns in Patients With Aldosterone-Producing Adenoma

Aldosterone-producing adenoma (APA), the main cause of endocrine hypertension, has recently been reported to be associated with other diseases, such as metabolic syndrome, but the detailed mechanism underlying this association remains unclear. Here, we used untargeted metabolomics and compared the abundance of serum metabolites between essential hypertension (EHT) and APA patients, as well as the serum metabolites of APA patients before and after adrenalectomy. Our results revealed 44 differential metabolites between APA and EHT patients and 39 differential metabolites between pre- and postoperative APA patients. Several metabolites involved in cardiovascular disease, obesity, and diabetes were dysregulated in APA patients compared to EHT patients, including arachidonic acid metabolites [e.g., 5(S)-HpETE and 12-HETE], amino acids (e.g., L-carnitine, taurine, and L-arginine), nucleotide metabolites (e.g., hypoxanthine) and cholesterol 3-sulfate. Importantly, the levels of hypoxanthine and cholesterol 3-sulfate, two metabolites that promote the development of atherosclerotic lesions and obesity, were originally increased in APA patients, but those elevated levels were reversed by adrenalectomy. Conversely, levels of L-carnitine and (3-carboxypropyl) trimethylammonium cation, two metabolites participating in lipid metabolism, were decreased in APA patients but increased postoperatively. We conclude that APA might participate in cardiovascular and metabolic diseases by regulating serum metabolites.

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.

Gut Microbiota-Derived Metabolites in Colorectal Cancer: The Bad and the Challenges

Accumulating evidence from studies in humans and animal models has elucidated that gut microbiota, acting as a complex ecosystem, contributes critically to colorectal cancer (CRC). The potential mechanisms often reported emphasize the vital role of carcinogenic activities of specific pathogens, but in fact, a series of metabolites produced from exogenous dietary substrates or endogenous host compounds occupy a decisive position similarly. Detrimental gut microbiota-derived metabolites such as trimethylamine-N-oxide, secondary bile acids, hydrogen sulfide and N-nitroso compounds could reconstruct the ecological composition and metabolic activity of intestinal microorganisms and formulate a microenvironment that opens susceptibility to carcinogenic stimuli. They are implicated in the occurrence, progression and metastasis of CRC through different mechanisms, including inducing inflammation and DNA damage, activating tumorigenic signaling pathways and regulating tumor immunity. In this review, we mainly summarized the intimate relationship between detrimental gut microbiota-derived metabolites and CRC, and updated the current knowledge about detrimental metabolites in CRC pathogenesis. Then, multiple interventions targeting these metabolites for CRC management were critically reviewed, including diet modulation, probiotics/prebiotics, fecal microbiota transplantation, as well as more precise measures such as engineered bacteria, phage therapy and chemopreventive drugs. A better understanding of the interplay between detrimental microbial metabolites and CRC would hold great promise against CRC.

Metabolic signatures of greater body size and their associations with risk of colorectal and endometrial cancers in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

The mechanisms underlying the obesity-cancer relationship are incompletely understood. This study aimed to characterise metabolic signatures of greater body size and to investigate their association with two obesity-related malignancies, endometrial and colorectal cancers, and with weight loss within the context of an intervention study.

METHODS

Targeted mass spectrometry metabolomics data from 4326 participants enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 17 individuals from a single-arm pilot weight loss intervention (Intercept) were used in this analysis. Metabolic signatures of body size were first determined in discovery (N = 3029) and replication (N = 1297) sets among EPIC participants by testing the associations between 129 metabolites and body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) using linear regression models followed by partial least squares analyses. Conditional logistic regression models assessed the associations between the metabolic signatures with endometrial (N = 635 cases and 648 controls) and colorectal (N = 423 cases and 423 controls) cancer risk using nested case-control studies in EPIC. Pearson correlation between changes in the metabolic signatures and weight loss was tested among Intercept participants.

RESULTS

After adjustment for multiple comparisons, greater BMI, WC, and WHR were associated with higher levels of valine, isoleucine, glutamate, PC aa C38:3, and PC aa C38:4 and with lower levels of asparagine, glutamine, glycine, serine, lysoPC C17:0, lysoPC C18:1, lysoPC C18:2, PC aa C42:0, PC ae C34:3, PC ae C40:5, and PC ae C42:5. The metabolic signature of BMI (OR1-sd 1.50, 95% CI 1.30-1.74), WC (OR1-sd 1.46, 95% CI 1.27-1.69), and WHR (OR1-sd 1.54, 95% CI 1.33-1.79) were each associated with endometrial cancer risk. Risk of colorectal cancer was positively associated with the metabolic signature of WHR (OR1-sd: 1.26, 95% CI 1.07-1.49). In the Intercept study, a positive correlation was observed between weight loss and changes in the metabolic signatures of BMI (r = 0.5, 95% CI 0.06-0.94, p = 0.03), WC (r = 0.5, 95% CI 0.05-0.94, p = 0.03), and WHR (r = 0.6, 95% CI 0.32-0.87, p = 0.01).

CONCLUSIONS

Obesity is associated with a distinct metabolic signature comprising changes in levels of specific amino acids and lipids which is positively associated with both colorectal and endometrial cancer and is potentially reversible following weight loss.

Andrographolide Against Lung Cancer-New Pharmacological Insights Based on High-Throughput Metabolomics Analysis Combined with Network Pharmacology

Andrographolide (Andro) has known to treat various illnesses such as colds, diarrhea, fever and infectious diseases. However, the effect mechanism of Andro is still unclear. Therefore, we used high-throughput metabolomics analysis to discover biomarkers, metabolic profiles and pathways to reveal the pharmacological action and effective mechanism of Andro against lung cancer. The metabolic effects of Andro on lung cancer animal was explored by ultra-performance liquid chromatography-triple-time of flight/mass spectrometry (UPLC-TOF/MS) analysis. Our results showed that Andro exhibited significant protective effects against lung cancer. Compared with control group, a total of 25 metabolites biomarkers was identified in urine of model animals, which 18 of them were regulated toward the normal direction after Andro treatment, and network pharmacology analysis showed that they were related with 570 proteins. Biological pathways analysis showed that the 11 metabolism pathways were regulated by Andro treatment in lung cancer mouse, and amino acid metabolism and arachidonic acid metabolism have great potential as target pathways for Andro against lung cancer. It revealed that high-throughput metabolomics combined with network pharmacology analysis provides deeply insight into the therapeutic mechanisms of natural product for promoting medicine development and disease treatment.

Efficacy of Analytical Technologies in Metabolomics Studies of the Gastrointestinal Cancers

According to the reports of the World Health Organization and the International Agency for Research on Cancer, cancer is the second leading cause of human death worldwide. However, early-stage detection of cancers can efficiently enhance the chance of therapy and saving lives. Metabolomics strategies apply a variety of approaches to discover new potential diagnoses, prognoses, and/or therapeutic biomarkers of various diseases. Metabolomics aims to identify and measure different low-molecular-weight biomolecules in physiological environments. In these studies, special metabolites are extracted from biological samples and identified using analytical techniques. Afterward, using data processing programs discovering significantly associated biomarkers is pursued. In the present review, we aimed to discuss recently reported analytical approaches on the metabolomics studies of gastrointestinal cancers including gastric, colorectal, and esophageal cancers. The gas- and liquid-chromatography with different detectors have been shown that are the main analytical techniques and for metabolites quantification, nuclear magnetic resonance has been utilized as a master method.

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

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

Lactic acid promotes metastatic niche formation in bone metastasis of colorectal cancer

BACKGROUND

To investigate the effect of lactic acid (LA) on the progression of bone metastasis from colorectal cancer (CRC) and its regulatory effects on primary CD115 (+) osteoclast (OC) precursors.

METHODS

The BrdU assay, Annexin-V/PI assay, TRAP staining and immunofluorescence were performed to explore the effect of LA on the proliferation, apoptosis and differentiation of OC precursors in vitro and in vivo. Flow cytometry was performed to sort primary osteoclast precursors and CD4(+) T cells and to analyze the change in the expression of target proteins in osteoclast precursors. A recruitment assay was used to test how LA and Cadhein-11 regulate the recruitment of OC precursors. RT-PCR and Western blotting were performed to analyze the changes in the mRNA and protein expression of genes related to the PI3K-AKT pathway and profibrotic genes. Safranin O-fast green staining, H&E staining and TRAP staining were performed to analyze the severity of bone resorption and accumulation of osteoclasts.

RESULTS

LA promoted the expression of CXCL10 and Cadherin-11 in CD115(+) precursors through the PI3K-AKT pathway. We found that CXCL10 and Cadherin-11 were regulated by the activation of CREB and mTOR, respectively. LA-induced overexpression of CXCL10 in CD115(+) precursors indirectly promoted the differentiation of osteoclast precursors through the recruitment of CD4(+) T cells, and the crosstalk between these two cells promoted bone resorption in bone metastasis from CRC. On the other hand, Cadherin-11 mediated the adhesion between osteoclast precursors and upregulated the production of specific collagens, especially Collagen 5, which facilitated fibrotic changes in the tumor microenvironment. Blockade of the PI3K-AKT pathway efficiently prevented the progression of bone metastasis caused by lactate.

CONCLUSION

LA promoted metastatic niche formation in the tumor microenvironment through the PI3K-AKT pathway. Our study provides new insight into the role of LA in the progression of bone metastasis from CRC. Video Abstract.

Association between Metabolites and the Risk of Lung Cancer: A Systematic Literature Review and Meta-Analysis of Observational Studies

Globally, lung cancer is the most prevalent cancer type. However, screening and early detection is challenging. Previous studies have identified metabolites as promising lung cancer biomarkers. This systematic literature review and meta-analysis aimed to identify metabolites associated with lung cancer risk in observational studies. The literature search was performed in PubMed and EMBASE databases, up to 31 December 2019, for observational studies on the association between metabolites and lung cancer risk. Heterogeneity was assessed using the I² statistic and Cochran’s Q test. Meta-analyses were performed using either a fixed-effects or random-effects model, depending on study heterogeneity. Fifty-three studies with 297 metabolites were included. Most identified metabolites (252 metabolites) were reported in individual studies. Meta-analyses were conducted on 45 metabolites. Five metabolites (cotinine, creatinine riboside, N-acetylneuraminic acid, proline and r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene) and five metabolite groups (total 3-hydroxycotinine, total cotinine, total nicotine, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (sum of concentrations of the metabolite and its glucuronides), and total nicotine equivalent (sum of total 3-hydroxycotinine, total cotinine and total nicotine)) were associated with higher lung cancer risk, while three others (folate, methionine and tryptophan) were associated with lower lung cancer risk. Significant heterogeneity was detected across most studies. These significant metabolites should be further evaluated as potential biomarkers for lung cancer.

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

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

Signatures of Mucosal Microbiome in Oral Squamous Cell Carcinoma Identified Using a Random Forest Model

Objective

The aim of this study was to explore the signatures of oral microbiome associated with OSCC using a random forest (RF) model.

Patients and Methods

A total of 24 patients with OSCC were enrolled in the study. The oral microbiome was assessed in cancerous lesions and matched paracancerous tissues from each patient using 16S rRNA gene sequencing. Signatures of mucosal microbiome in OSCC were identified using a RF model.

Results

Significant differences were found between OSCC lesions and matched paracancerous tissues with respect to the microbial profile and composition. Linear discriminant analysis effect size analyses (LEfSe) identified 15 bacteria genera associated with cancerous lesions. Fusobacterium, Treponema, Streptococcus, Peptostreptococcus, Carnobacterium, Tannerella, Parvimonas and Filifactor were enriched. A classifier based on RF model identified a microbial signature comprising 12 bacteria, which was capable of distinguishing cancerous lesions and paracancerous tissues (AUC = 0.82). The network of the oral microbiome in cancerous lesions appeared to be simplified and fragmented. Functional analyses of oral microbiome showed altered functions in amino acid metabolism and increased capacity of glucose utilization in OSCC.

Conclusion

The identified microbial signatures may potentially be used as a biomarker for predicting OSCC or for clinical assessment of oral cancer risk.

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.

Metabolomic characterization of colorectal cancer cell lines highlighting stage-specific alterations during cancer progression

Introduction: Metabolomic studies on various colorectal cancer (CRC) cell lines have improved our understanding of the biochemical events underlying the disease. However, the metabolic profile dynamics associated with different stages of CRC progression is still lacking. Such information can provide further insights into the pathophysiology and progression of the disease that will prove useful in identifying specific targets for drug designing and therapeutics. Thus, our study aims to characterize the metabolite profiles in the established cell lines corresponding to different stages of CRC. Methods: Metabolite profiling of normal colon cell lines (CCD 841 CoN) and CRC cell lines corresponding to different stages, i.e., SW 1116 (stage A), HT 29 and SW 480 (stage B), HCT 15 and DLD-1 (stage C), and HCT 116 (stage D), was carried out using liquid chromatography-mass spectrometry (LC-MS). Mass Profiler Professional and Metaboanalyst 4.0 software were used for statistical and pathway analysis. METLIN database was used for the identification of metabolites. Results: We identified 72 differential metabolites compared between CRC cell lines of all the stages and normal colon cells. Principle component analysis and partial least squares discriminant analysis score plot were used to segregate normal and CRC cells, as well as CRC cells in different stages of the disease. Variable importance in projection score identified unique differential metabolites in CRC cells of the different stages. We identified 7 differential metabolites unique to stage A, 3 in stage B, 5 in stage C, and 5 in stage D. Conclusion: This study highlights the differential metabolite profiling in CRC cell lines corresponding to different stages. The identification of the differential metabolites in CRC cells at individual stages will lead to a better understanding of the pathophysiology of CRC development and progression and, hence, its application in treatment strategies.

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Global metabolomics profiling of colorectal cancer in Malaysian patients

Introduction: The serum metabolomics approach has been used to identify metabolite biomarkers that can diagnose colorectal cancer (CRC) accurately and specifically. However, the biomarkers identified differ between studies suggesting that more studies need to be performed to understand the influence of genetic and environmental factors. Therefore, this study aimed to identify biomarkers and affected metabolic pathways in Malaysian CRC patients. Methods: Serum from 50 healthy controls and 50 CRC patients were collected at UKM Medical Centre. The samples were deproteinized with acetonitrile and untargeted metabolomics profile determined using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOFMS, Agilent USA). The data were analysed using Mass Profiler Professional (Agilent, USA) software. The panel of biomarkers determined were then used to identify CRC from a new set of 20 matched samples. Results: Eleven differential metabolites were identified whose levels were significantly different between CRC patients compared to normal controls. Based on the analysis of the area under the curve, 7 of these metabolites showed high sensitivity and specificity as biomarkers. The use of the 11 metabolites on a new set of samples was able to differentiate CRC from normal samples with 80% accuracy. These metabolites were hypoxanthine, acetylcarnitine, xanthine, uric acid, tyrosine, methionine, lysoPC, lysoPE, citric acid, 5-oxoproline, and pipercolic acid. The data also showed that the most perturbed pathways in CRC were purine, catecholamine, and amino acid metabolisms. Conclusion: Serum metabolomics profiling can be used to identify distinguishing biomarkers for CRC as well as to further our knowledge of its pathophysiological mechanisms.