Associations between metabolites and pancreatic cancer risk in a large prospective epidemiological study

Integrating OMICS-based platforms and analytical tools for diagnosis and management of pancreatic cancer: a review

Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

Novel metabolomic predictors of incident colorectal cancer in men and women

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Human blood metabolites and gastric cancer: a Mendelian randomization analysis

BACKGROUND

Gastric cancer (GC) remains one of the predominant malignant tumors within the digestive tract, yet its underlying biological mechanisms remain elusive. The primary objective of this study is to delineate the causal relationship between circulating metabolites and GC.

METHOD

The primary Mendelian randomization (MR) analysis was based on three large GWAS datasets. While the inverse variance weighted served as the primary analysis technique for investigating causal relationships, additional sensitivity analyses were facilitated through methods such as MR-PRESSO, the weighted median, and MR-Egger. Subsequently, replication, meta-analysis, and multivariable MR were executed using another GC GWAS.

RESULTS

The results of this study indicated significant associations between three metabolites 3-methyl-2-oxovalerate (OR 5.8, 95%CI: 1.53-22.05, p = 0.0099), piperine (OR 2.05, 95%CI: 1.13-3.7, p = 0.0175), Phe-Phe dipeptide (OR 0.16, 95%CI: 0.03-0.93, p = 0.0409) and GC.

CONCLUSION

The present study provides evidence supporting a causal relationship between these three circulating metabolites and GC risk. Elevated levels of 3-methyl-2-oxovalerate and piperine may increase the risk of GC, while Phe-Phe dipeptide may have a protective effect. By integrating genomics and metabolomics, we offer a novel perspective on the biological mechanisms underlying GC. Such insights have the potential to enhance strategies for the screening, prevention, and treatment of GC.

Microbiome and metabolome analysis in smoking and non-smoking pancreatic ductal adenocarcinoma patients

BACKGROUND

Smoking is a significant risk factor for pancreatic ductal adenocarcinoma (PDAC). This study aimed to investigate the effects of smoking on the pancreatic microbiome and metabolome in resectable and unresectable male PDAC patients.

METHODS

The pancreatic tissue samples were collected from resectable PDACs via surgery and unresectable PDACs via endoscopic ultrasound fine needle aspiration (EUS-FNA). Surgical samples obtained from 10 smoking and 6 non-smoking PDACs were measured by 16S ribosomal RNA (16S rRNA) gene sequencing and liquid chromatography-mass spectrometry (LC/MS). Fine needle aspiration (FNA) samples obtained from 20 smoking and 14 non-smoking PDACs were measured by 16S rRNA gene sequencing.

RESULTS

From resectable to unresectable patients, the dominant genus in the pancreas changed from Achromobacter to Delftia. Smoking further altered the abundance of specific bacteria, mainly manifested as an increase of Slackia in surgical tumor tissue of the smoking group, and an enrichment of Aggregatibacter and Peptococcus in FNA samples of the smoking group. In tumor tissue, smoking caused an enrichment of the cancer-promoting cAMP signaling pathway and L-lactic acid. In paracancerous tissue, smoking also induced a detrimental disturbance in the pancreatic microbiome and metabolome, including an enrichment of Veillonella, Novosphingobium, Deinococcus, and 3-hydroxybutanoic acid, and a reduction of linoleic acid. Besides, the cancer-promoting L-lactic acid was negatively correlated with Faecalibacterium in tumor tissue based on the correlation analysis.

CONCLUSION

There were differences in the pancreatic microbiome of PDAC patients at different stages, and smoking can further disrupt the pancreatic microbiome and metabolism in PDAC.

Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.

Serum Erythritol and Risk of Overall and Cause-Specific Mortality in a Cohort of Men

Erythritol occurs naturally in some fruits and fermented foods, and has also been used as an artificial sweetener since the 1990s. Although there have been questions and some studies regarding its potential adverse health effects, the association between serum erythritol and long-term mortality has not been evaluated. To examine the association between serum erythritol's biochemical status and risk of overall and cause-specific mortality, a prospective cohort analysis was conducted using participants in the ATBC Study (1985-1993) previously selected for metabolomic sub-studies. The analysis included 4468 participants, among whom 3377 deaths occurred during an average of 19.1 years of follow-up. Serum erythritol was assayed using an untargeted, global, high-resolution, accurate-mass platform of ultra-high-performance liquid and gas chromatography. Cause-specific deaths were identified through Statistics Finland and defined by the International Classification of Diseases. After adjustment for potential confounders, serum erythritol was associated with increased risk of overall mortality (HR = 1.50 [95% CI = 1.17-1.92]). We found a positive association between serum erythritol and cardiovascular disease mortality risk (HR = 1.86 [95% CI = 1.18-2.94]), which was stronger for heart disease mortality than for stroke mortality risk (HR = 3.03 [95% CI = 1.00-9.17] and HR = 2.06 [95% CI = 0.72-5.90], respectively). Cancer mortality risk was also positively associated with erythritol (HR = 1.54 [95% CI = 1.09-2.19]). The serum erythritol-overall mortality risk association was stronger in men ≥ 55 years of age and those with diastolic blood pressure ≥ 88 mm Hg (p for interactions 0.045 and 0.01, respectively). Our study suggests that elevated serum erythritol is associated with increased risk of overall, cardiovascular disease, and cancer mortality. Additional studies clarifying the role of endogenous production and dietary/beverage intake of erythritol in human health and mortality are warranted.

From serum metabolites to the gut: revealing metabolic clues to susceptibility to subtypes of Crohn's disease and ulcerative colitis

Background and aims

Inflammatory bowel disease (IBD) is a common chronic inflammatory bowel disease characterized by diarrhea and abdominal pain. Recently human metabolites have been found to help explain the underlying biological mechanisms of diseases of the intestinal system, so we aimed to assess the causal relationship between human blood metabolites and susceptibility to IBD subtypes.

Methods

We selected a genome-wide association study (GWAS) of 275 metabolites as the exposure factor, and the GWAS dataset of 10 IBD subtypes as the outcome, followed by univariate and multivariate analyses using a two-sample Mendelian randomization study (MR) to study the causal relationship between exposure and outcome, respectively. A series of sensitivity analyses were also performed to ensure the robustness of the results.

Results

A total of 107 metabolites were found to be causally associated on univariate analysis after correcting for false discovery rate (FDR), and a total of 9 metabolites were found to be significantly causally associated on subsequent multivariate and sensitivity analyses. In addition we found causal associations between 7 metabolite pathways and 6 IBD subtypes.

Conclusion

Our study confirms that blood metabolites and certain metabolic pathways are causally associated with the development of IBD subtypes and their parenteral manifestations. The exploration of the mechanisms of novel blood metabolites on IBD may provide new therapeutic ideas for IBD patients.

Microbiota and metabolite alterations in pancreatic head and body/tail cancer patients

Pancreatic head cancer (PHC) and pancreatic body/tail cancer (PBTC) have distinct clinical and biological behaviors. The microbial and metabolic differences in PHC and PBTC have not been studied. The pancreatic microbiota and metabolome of 15 PHC and 8 PBTC tissues and their matched nontumor tissues were characterized using 16S rRNA amplicon sequencing and untargeted metabolomics. At the genus level, Bradyrhizobium was increased while Corynebacterium and Ruminococcus were decreased in the PHC tissues (Head T) compared with the matched nontumor tissues (Head N) significantly. Shuttleworthia, Bacillus, and Bifidobacterium were significantly decreased in the PBTC tissues (Body/Tail T) compared with the matched nontumor tissues (Body/Tail N). Significantly, Ileibacterium was increased whereas Pseudoxanthomonas was decreased in Head T and Body/Tail T, and Lactobacillus was increased in Head T but decreased in Body/Tail T. A total of 102 discriminative metabolites were identified between Head T and Head N, which were scattered through linoleic acid metabolism and purine metabolism pathways. However, there were only four discriminative metabolites between Body/Tail T and Body/Tail N, which were related to glycerophospholipid metabolism and autophagy pathways. The differential metabolites in PHC and PBTC were commonly enriched in alpha-linolenic acid metabolism and choline metabolism in cancer pathways. Eubacterium decreased in Head T was positively correlated with decreased linoleic acid while negatively correlated with increased arachidyl carnitine and stearoylcarnitine. Bacillus decreased in Body/Tail T was negatively correlated with increased L-carnitine. These microbiota and metabolites deserve further investigations to reveal their roles in the pathogenesis of PHC and PBTC, providing clues for future treatments.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank

Background

Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.

Methods

We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.

Results

Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].

Conclusions

Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.

Non-targeted metabolomics revealed novel links between serum metabolites and primary ovarian insufficiency: a Mendelian randomization study

Background

Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms.

Methods

Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis.

Results

Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weighted-median =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants.

Conclusion

By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention.

Elucidating the role of blood metabolites on pancreatic cancer risk using two-sample Mendelian randomization analysis

Pancreatic ductal adenocarcinoma (PDAC) is an uncommon but highly fatal malignancy. Identifying causal metabolite biomarkers offers an opportunity to facilitate effective risk assessment strategies for PDAC. In this study, we performed a two-sample Mendelian randomization (MR) study to characterize the potential causal effects of metabolites in plasma on PDAC risk. Genetic instruments were determined for a total of 506 metabolites from one set of comprehensive genome-wide association studies (GWAS) involving 913 individuals of European ancestry from the INTERVAL/EPIC-Norfolk cohorts. Another set of genetic instruments was developed for 483 metabolites from an independent GWAS conducted with 8299 individuals of European ancestry from the Canadian Longitudinal Study on Aging (CLSA) cohort. We analyzed GWAS data of the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), comprising 8275 PDAC cases and 6723 controls of European ancestry. The association of metabolites with PDAC risk was assessed using the inverse-variance weighted (IVW) method, and complemented with sensitivity analyses of MR-Egger and MR-PRESSO tests. Potential side effects of targeting the identified metabolites for PDAC intervention were further evaluated by a phenome-wide MR (Phe-MR) analysis. Forty-four unique metabolites were identified to be significantly associated with PDAC risk, of which four top-ranking metabolites (X: 12798, X: 11787, X: 11308 and X: 19141) showed replication evidence when using instruments developed from both two cohorts. Our results highlight novel blood metabolites related to PDAC risk, which may help prioritize metabolic features for PDAC mechanistic research and further evaluation of their potential role in PDAC risk assessment.

Investigating causal associations among gut microbiota, metabolites, and psoriatic arthritis: a Mendelian randomization study

BACKGROUND

Currently, there has been observed a significant alteration in the composition of the gut microbiome (GM) and serum metabolites in patients with psoriatic arthritis (PsA) compared to healthy individuals. However, previous observational studies have shown inconsistent results regarding the alteration of gut microbiota/metabolites. In order to shed light on this matter, we utilized Mendelian randomization to determine the causal effect of GM/metabolites on PsA.

METHODS

We retrieved summary-level data of GM taxa/metabolites and PsA from publicly available GWAS statistics. Causal relationships between GM/metabolites and PsA were determined using a two-sample MR analysis, with the IVW approach serving as the primary analysis method. To ensure the robustness of our findings, we conducted sensitivity analyses, multivariable MR analysis (MVMR), and additional analysis including replication verification analysis, LDSC regression, and Steiger test analysis. Furthermore, we investigated reverse causality through a reverse MR analysis. Finally, we conducted an analysis of expression quantitative trait loci (eQTLs) involved in the metabolic pathway to explore potential molecular mechanisms of metabolism.

RESULTS

Our findings reveal that eight GM taxa and twenty-three serum metabolites are causally related to PsA (P < 0.05). Notably, a higher relative abundance of Family Rikenellaceae (ORIVW: 0.622, 95% CI: 0.438-0.883, FDR = 0.045) and elevated serum levels of X-11538 (ORIVW: 0.442, 95% CI: 0.250-0.781, FDR = 0.046) maintain significant causal associations with a reduced risk of PsA, even after adjusting for multiple testing correction and conducting MVMR analysis. These findings suggest that Family Rikenellaceae and X-11538 may have protective effects against PsA. Our sensitivity analysis and additional analysis revealed no significant horizontal pleiotropy, reverse causality, or heterogeneity. The functional enrichment analysis revealed that the eQTLs examined were primarily associated with glycerolipid metabolism and the expression of key metabolic factors influenced by bacterial infections (Vibrio cholerae and Helicobacter pylori) as well as the mTOR signaling pathway.

CONCLUSION

In conclusion, our study demonstrates that Family Rikenellaceae and X-11538 exhibit a strong and negative causal relationship with PsA. These particular GM taxa and metabolites have the potential to serve as innovative biomarkers, offering valuable insights into the treatment and prevention of PsA. Moreover, bacterial infections and mTOR-mediated activation of metabolic factors may play an important role in this process.

Serum Metabolomic Profiles for Distinguishing Lung Cancer From Pulmonary Tuberculosis: Identification of Rapid and Noninvasive Biomarker

BACKGROUND

Pulmonary tuberculosis (PTB) and lung cancer (LC) have similar clinical symptoms and atypical imaging findings, which are easily misdiagnosed. There is an urgent need for a noninvasive and accurate biomarker to distinguish LC from PTB.

METHODS

A total of 694 subjects were enrolled and divided into discovery set (n = 122), identification set (n = 214), and validation set (n = 358). Metabolites were identified by multivariate and univariate analyses. Receiver operating characteristic curve were used to evaluate the diagnostic efficacy of biomarkers.

RESULTS

Seven metabolites were identified and validated. Phenylalanylphenylalanine for distinguishing LC from PTB yielded an area under the curve of 0.89, sensitivity of 71%, and specificity of 92%. It also showed good diagnostic abilities in discovery set and identification set. Compared with that in healthy volunteers (median [interquartile range], 1.57 [1.01, 2.34] μg/mL), it was elevated in LC (4.76 [2.74, 7.08] μg/mL; ratio of median, [ROM] = 3.03, P < .01) and reduced in PTB (1.06 [0.51, 2.09] μg/mL; ROM = 0.68, P < .05).

CONCLUSIONS

The metabolomic profile of LC and PTB was described and a key biomarker identified. We produced a rapid and noninvasive method to supplement existing clinical diagnostic examinations for distinguishing LC from PTB.

Unveiling the intricate causal nexus between pancreatic cancer and peripheral metabolites through a comprehensive bidirectional two-sample Mendelian randomization analysis

Aim: Pancreatic cancer (PC) is a devastating malignancy characterized by its aggressive nature and poor prognosis. However, the relationship of PC with peripheral metabolites remains not fully investigated. The study aimed to explore the causal linkage between PC and peripheral metabolite profiles. Methods: Employing publicly accessible genome-wide association studies (GWAS) data, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis. The primary analysis employed the inverse-variance weighted (IVW) method. To address potential concerns about horizontal pleiotropy, we also employed supplementary methods such as maximum likelihood, weighted median, MR-Egger regression, and MR pleiotropy residual sum and outlier (MR-PRESSO). Results: We ascertained 20 genetically determined peripheral metabolites with causal linkages to PC while high-density lipoprotein (HDL) and very low-density lipoprotein (VLDL) particles accounted for the vast majority. Specifically, HDL particles exhibited an elevated PC risk while VLDL particles displayed an opposing pattern. The converse MR analysis underscored a notable alteration in 17 peripheral metabolites due to PC, including branch chain amino acids and derivatives of glycerophospholipid. Cross-referencing the bidirectional MR results revealed a reciprocal causation of PC and X-02269 which might form a self-perpetuating loop in PC development. Additionally, 1-arachidonoylglycerophosphocholine indicated a reduced PC risk and an increase under PC influence, possibly serving as a negative feedback regulator. Conclusion: Our findings suggest a complex interplay between pancreatic cancer and peripheral metabolites, with potential implications for understanding the etiology of pancreatic cancer and identifying novel early diagnosis and therapeutic targets. Moreover, X-02269 may hold a pivotal role in PC onset and progression.

Multiomic Approaches for Cancer Biomarker Discovery in Liquid Biopsies: Advances and Challenges

Cancer is a complex and heterogeneous disease that poses a significant threat to global health. Early diagnosis and treatment are critical for improving patient outcomes, and the use of liquid biopsies has emerged as a promising approach for cancer detection and monitoring. Traditionally, cancer diagnosis has relied on invasive tissue biopsies, the collection of which can prove challenging for patients and the results of which may not always provide accurate results due to tumor heterogeneity. Liquid biopsies have gained increasing attention as they provide a non-invasive and accessible source of cancer biomarkers, which can be used to diagnose cancer, monitor treatment response, and detect relapse. The integration of -omics technologies, such as proteomics, genomics, and metabolomics, has further enhanced the capabilities of liquid biopsies by introducing precision oncology and enabling the tailoring of treatment for individual patients based on their unique tumor biology. In this review, we will discuss the challenges and advances in the field of cancer liquid biopsies and the integration of -omics technologies for different types of liquid biopsies, including blood, tear, urine, sweat, saliva, and cerebrospinal fluid.

A metabolomic investigation of serum perfluorooctane sulfonate and perfluorooctanoate

BACKGROUND

Exposures to perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA), environmentally persistent chemicals detectable in the blood of most Americans, have been associated with several health outcomes. To offer insight into their possible biologic effects, we evaluated the metabolomic correlates of circulating PFOS and PFOA among 3,647 participants in eight nested case-control serum metabolomic profiling studies from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

METHODS

Metabolomic profiling was conducted by Metabolon Inc., using ultra high-performance liquid chromatography/tandem accurate mass spectrometry. We conducted study-specific multivariable linear regression analyses estimating the associations of metabolite levels with levels of PFOS or PFOA. For metabolites measured in at least 3 of 8 nested case-control studies, random effects meta-analysis was used to summarize study-specific results (1,038 metabolites in PFOS analyses and 1,100 in PFOA analyses).

RESULTS

The meta-analysis identified 51 and 38 metabolites associated with PFOS and PFOA, respectively, at a Bonferroni-corrected significance level (4.8x10-5 and 4.6x10-5, respectively). For both PFOS and PFOA, the most common types of associated metabolites were lipids (sphingolipids, fatty acid metabolites) and xenobiotics (xanthine metabolites, chemicals). Positive associations were commonly observed with lipid metabolites sphingomyelin (d18:1/18:0) (P = 2.0x10-10 and 2.0x10-8, respectively), 3-carboxy-4-methyl-5-pentyl-2-furanpropionate (P = 2.7x10-15, 1.1x10-17), and lignoceroylcarnitine (C24) (P = 2.6x10-8, 6.2x10-6). The strongest positive associations were observed for chemicals 3,5-dichloro-2,6-dihydroxybenzoic acid (P = 3.0x10-112 and 6.8x10-13, respectively) and 3-bromo-5-chloro-2,6-dihydroxybenzoic acid (P = 1.6x10-14, 2.3x10-6). Other metabolites positively associated with PFOS included D-glucose (carbohydrate), carotene diol (vitamin A metabolism), and L-alpha-aminobutyric acid (glutathione metabolism), while uric acid (purine metabolite) was positively associated with PFOA. PFOS associations were consistent even after adjusting for PFOA as a covariate, while PFOA associations were greatly attenuated with PFOS adjustment.

CONCLUSIONS

In this large metabolomic study, we observed robust positive associations with PFOS for several molecules. Further investigation of these metabolites may offer insight into PFOS-related biologic effects.

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

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

Diabetes Mellitus in Pancreatic Cancer: A Distinct Approach to Older Subjects with New-Onset Diabetes Mellitus

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis, with near-identical incidence and mortality. According to the World Health Organization Globocan Database, the estimated number of new cases worldwide will rise by 70% between 2020 and 2040. There are no effective screening methods available so far, even for high-risk individuals. The prognosis of PDAC, even at its early stages, is still mostly unsatisfactory. Impaired glucose metabolism is present in about 3/4 of PDAC cases.

METHODS

Available literature on pancreatic cancer and diabetes mellitus was reviewed using a PubMed database. Data from a national oncology registry (on PDAC) and information from a registry of healthcare providers (on diabetes mellitus and a number of abdominal ultrasound investigations) were obtained.

RESULTS

New-onset diabetes mellitus in subjects older than 60 years should be an incentive for a prompt and detailed investigation to exclude PDAC. Type 2 diabetes mellitus, diabetes mellitus associated with chronic non-malignant diseases of the exocrine pancreas, and PDAC-associated type 3c diabetes mellitus are the most frequent types. Proper differentiation of particular types of new-onset diabetes mellitus is a starting point for a population-based program. An algorithm for subsequent steps of the workup was proposed.

CONCLUSIONS

The structured, well-differentiated, and elaborately designed approach to the elderly with a new onset of diabetes mellitus could improve the current situation in diagnostics and subsequent poor outcomes of therapy of PDAC.

Metabolomic Analysis of Vitamin E Supplement Use in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

The effects of vitamin E supplementation on cancer and other chronic diseases are not clear. We compared the serum metabolomic profile of differing vitamin E dosages in order to re-examine the previously observed changes in a novel C22 lactone sulfate compound, androgenic steroids, and other metabolites. A total of 3409 women and men previously selected for metabolomics studies in the PLCO Cancer Screening Trial were included in this investigation. Serum metabolites were profiled using ultrahigh-performance liquid and gas chromatography/tandem mass spectrometry. Seventy known metabolites including C22 lactone sulfate and androgens were significantly associated with vitamin E supplementation. In the sex-stratified analysis, 10 cofactors and vitamins (e.g., alpha-CEHC sulfate and alpha-CEHC glucuronide), two carbohydrates (glyceric and oxalic acids), and one lipid (glycocholenate sulfate) were significantly associated with vitamin E dose in both males and females (FDR-adjusted p-value < 0.01). However, the inverse association between C22 lactone sulfate and daily vitamin E supplementation was evident in females only, as were two androgenic steroids, 5-androstenediol and androsterone glucuronide. Our study provides evidence of distinct steroid hormone pathway responses based on vitamin E dosages. Further studies are needed to gain biological insights into vitamin E biochemical effects relevant to cancer and other chronic diseases.

Serum Metabolite Biomarkers for Pancreatic Tumors: Neuroendocrine and Pancreatic Ductal Adenocarcinomas-A Preliminary Study

BACKGROUND

Pancreatic cancer is the most common pancreatic solid malignancy with an aggressive clinical course and low survival rate. There are a limited number of reliable prognostic biomarkers and a need to understand the pathogenesis of pancreatic tumors; neuroendocrine (PNET) and pancreatic ductal adenocarcinomas (PDAC) encouraged us to analyze the serum metabolome of pancreatic tumors and disturbances in the metabolism of PDAC and PNET.

METHODS

Using the AbsoluteIDQ^® p180 kit (Biocrates Life Sciences AG, Innsbruck, Austria) with liquid chromatography-mass spectrometry (LC-MS), we identified changes in metabolite profiles and disrupted metabolic pathways serum of NET and PDAC patients.

RESULTS

The concentration of six metabolites showed statistically significant differences between the control group and PDAC patients (p.adj < 0.05). Glutamine (Gln), acetylcarnitine (C2), and citrulline (Cit) presented a lower concentration in the serum of PDAC patients, while phosphatidylcholine aa C32:0 (PC aa C32:0), sphingomyelin C26:1 (SM C26:1), and glutamic acid (Glu) achieved higher concentrations compared to serum samples from healthy individuals. Five of the tested metabolites: C2 (FC = 8.67), and serotonin (FC = 2.68) reached higher concentration values in the PNET serum samples compared to PDAC, while phosphatidylcholine aa C34:1 (PC aa C34:1) (FC = -1.46 (0.68)) had a higher concentration in the PDAC samples. The area under the curves (AUC) of the receiver operating characteristic (ROC) curves presented diagnostic power to discriminate pancreatic tumor patients, which were highest for acylcarnitines: C2 with AUC = 0.93, serotonin with AUC = 0.85, and PC aa C34:1 with AUC = 0.86.

CONCLUSIONS

The observations presented provide better insight into the metabolism of pancreatic tumors, and improve the diagnosis and classification of tumors. Serum-circulating metabolites can be easily monitored without invasive procedures and show the present clinical patients' condition, helping with pharmacological treatment or dietary strategies.

Adverse events after the transjugular intrahepatic portal shunt are linked to serum metabolomic changes following the procedure

Background and Objective: Transjugular intrahepatic portal shunt (TIPS) insertion could promote weight gain and muscle and fat mass increase in patients with cirrhosis. However, few studies have focused on metabolic changes after TIPS. This study aims to explore metabolic changes after TIPS and potential biomarkers of adverse events. Methods: Peripheral and portal serum samples were collected before and after TIPS insertion. Untargeted metabolomics was performed using ultra-high-performance liquid chromatography-mass spectrometry. Spearman's correlation analysis was used to determine the relationship between metabolites and clinical parameters. Metabolite set enrichment analysis was performed to explore enriched pathways. The predictive value of the metabolites was calculated by receiver operating characteristic curve (ROC) analysis. Results: Metabolites in the peripheral and portal serum significantly changed early after TIPS. Some lipid metabolites were significantly correlated with liver function parameters. Both elevated and depleted metabolites were mainly enriched in amino acid metabolism. Nine and 12 portal metabolites have moderate predictive value in post-TIPS liver function decline and hepatic encephalopathy (HE), separately (area under curve >0.7). Conclusion: Metabolites in the peripheral and portal veins significantly changed after TIPS. Some metabolic changes might be ascribed to liver function decline early after TIPS. Nine and 12 portal metabolites might be potential biomarkers in prediction of liver function decline and HE, separately.

Circulating gamma-glutamyl transpeptidase and risk of pancreatic cancer: A prospective cohort study in the UK Biobank

BACKGROUND

To determine whether serum gamma-glutamyl transpeptidase (GGT) level is associated with pancreatic cancer risk in a large prospective cohort.

METHODS

The study analyzed serum GGT concentration at baseline of 421,032 participants recruited in the UK Biobank since 2006 through 2010. Information on incidence of pancreatic cancer was obtained from cancer and death registers, updated until 2015 in Scotland or 2016 in England and Wales. Adjusted Cox proportional hazards models were used to measure the association between serum GGT and pancreatic cancer risk.

RESULTS

The study identified 586 cases of pancreatic cancer over a median follow-up period of 7.16 years. In the multivariable-adjusted Cox model, serum GGT level was associated with 14% higher pancreatic cancer risk (hazard ratio (HR) per one standard deviation increment of log2 GGT level = 1.14, 95% confidence interval (CI) 1.02-1.28, p = 0.025). In the total population, the HR for the highest GGT group was 1.68 (95%CI: 1.22-2.30) versus the lowest GGT group. The HR for the highest GGT group in men (≥50.2 U/L) was 1.72 (95%CI: 1.14-2.61) and that in women (≥31.6 U/L) was 1.75 (95%CI: 1.06-2.88) versus the lowest GGT group.

CONCLUSION

Our findings suggested a positive association of serum GGT in pancreatic cancer etiology, implying the potential of monitoring GGT level for identifying at-risk individuals for pancreatic cancer.

Biomarkers for pancreatic cancer based on tissue and serum metabolomics analysis in a multicenter study

BACKGROUND

Early detection of pancreatic ductal adenocarcinoma (PDAC) may improve the prognosis of patients. This study was to identify metabolic features of PDAC and to discover early detection biomarkers for PDAC by tissue and serum metabolomics analysis.

METHODS

We conducted nontargeted metabolomics analysis in tissue samples of 51 PDAC tumors, 40 noncancerous pancreatic tissues (NT), and 14 benign pancreatic neoplasms (BP) as well as serum samples from 80 patients with PDAC, 36 with BP, and 48 healthy controls (Ctr). The candidate metabolites identified from the initial analysis were further quantified using targeted analysis in serum samples of an independent cohort of 22 early stage PDAC, 27 BP, and 27 Ctr subjects. Unconditional binary logistic regression analysis was used to construct the optimal model for PDAC diagnosis.

RESULTS

Upregulated levels of fatty acids and lipids and downregulated amino acids were observed in tissue and serum samples of PDAC patients. Proline, creatine, and palmitic acid were identified as a panel of potential biomarkers to distinguish PDAC from BP and Ctr (odds ratio = 2.17, [95% confidence interval 1.34-3.53]). The three markers showed area under the receiver-operating characteristic curves (AUCs) of 0.854 and 0.865, respectively, for the comparison of PDAC versus Ctr and PDAC versus BP. The AUCs were 0.830 and 0.852 in the validation set and were improved to 0.949 and 0.909 when serum carbohydrate antigen 19-9 (CA19-9) was added to the model.

CONCLUSION

The novel metabolite biomarker panel identified in this study exhibited promising performance in distinguishing PDAC from BP or Ctr, especially in combination with CA19-9.

Untargeted metabolomic profiling identifies serum metabolites associated with type 2 diabetes in a cross-sectional study of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study

Type 2 diabetes (T2D) is a complex chronic disease with substantial phenotypic heterogeneity affecting millions of individuals. Yet, its relevant metabolites and etiological pathways are not fully understood. The aim of this study is to assess a broad spectrum of metabolites related to T2D in a large population-based cohort. We conducted a metabolomic analysis of 4,281 male participants within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. The serum metabolomic analysis was performed using an LC-MS/GC-MS platform. Associations between 1,413 metabolites and T2D were examined using linear regression, controlling for important baseline risk factors. Standardized β-coefficients and standard errors (SEs) were computed to estimate the difference in metabolite concentrations. We identified 74 metabolites that were significantly associated with T2D based on the Bonferroni-corrected threshold (P < 3.5 × 10-5). The strongest signals associated with T2D were of carbohydrates origin, including glucose, 1,5-anhydroglucitol (1,5-AG), and mannose (β = 0.34, -0.91, and 0.41, respectively; all P < 10-75). We found several chemical class pathways that were significantly associated with T2D, including carbohydrates (P = 1.3 × 10-11), amino acids (P = 2.7 × 10-6), energy (P = 1.5 × 10-4), and xenobiotics (P = 1.2 × 10-3). The strongest subpathway associations were seen for fructose-mannose-galactose metabolism, glycolysis-gluconeogenesis-pyruvate metabolism, fatty acid metabolism (acyl choline), and leucine-isoleucine-valine metabolism (all P < 10-8). Our findings identified various metabolites and candidate chemical class pathways that can be characterized by glycolysis and gluconeogenesis metabolism, fructose-mannose-galactose metabolism, branched-chain amino acids, diacylglycerol, acyl cholines, fatty acid oxidation, and mitochondrial dysfunction.NEW & NOTEWORTHY These metabolomic patterns may provide new additional evidence and potential insights relevant to the molecular basis of insulin resistance and the etiology of T2D.

Metabolomics Profiling of Nephrotic Syndrome towards Biomarker Discovery

Nephrotic syndrome (NS) is a kidney illness characterized by excessive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, which may lead to kidney failure and necessitate renal transplantation. End-stage renal disease, cardiovascular issues, and mortality are much more common in those with NS. Therefore, the present study aimed to identify potential new biomarkers associated with the pathogenesis and diagnosis of NS. The liquid chromatography–mass spectrometry (LC–MS) metabolomics approach was applied to profile the metabolome of human serum of patients with NS. A total of 176 metabolites were significantly altered in NS compared to the control. Arginine, proline, and tryptophan metabolism; arginine, phenylalanine, tyrosine, and tryptophan biosynthesis were the most common metabolic pathways dysregulated in NS. Furthermore, alanyl-lysine and isoleucyl-threonine had the highest discrimination between NS and healthy groups. The candidate biomarkers may lead to understanding the possible metabolic alterations associated with NS and serve as potential diagnostic biomarkers.

Integrated Analysis of Colorectal Cancer Reveals Cross-Cohort Gut Microbial Signatures and Associated Serum Metabolites

BACKGROUND & AIMS

Studies have reported abnormal gut microbiota or circulating metabolome associated with colorectal cancer (CRC), but it remains a challenge to capture the CRC-relevant features consistent across geographic regions. This is particularly the problem for metabolic traits of CRC because the analyses generally use different platforms and laboratory methods, which poses a barrier to cross-dataset examination. In light of this, we sought to elucidate the microbial and metabolic signatures of CRC with broad population relevance.

METHODS

In this integrated metagenomic (healthy controls [HC], n = 91; colorectal adenoma [CRA], n = 63; CRC, n = 71) and metabolomic (HC, n = 34; CRA, n = 31; CRC, n = 35) analysis, CRC-associated features and microbe-metabolite correlations were first identified from a Shanghai cohort. A gut microbial panel was trained in the in-house cohort and cross-validated in 7 published metagenomic datasets of CRC. The in-house metabolic connections to the cross-cohort microbial signatures were used as evidence to infer serum metabolites with potentially external relevance. In addition, a combined microbe-metabolite panel was produced for diagnosing CRC or adenoma.

RESULTS

CRC-associated alterations were identified in the gut microbiome and serum metabolome. A composite microbe-metabolite diagnostic panel was developed and yielded an area under the curve of 0.912 for adenoma and 0.994 for CRC. We showed that many CRC-associated metabolites were linked to cross-cohort gut microbiome signatures of the disease, including CRC-enriched leucylalanine, serotonin, and imidazole propionate; and CRC-depleted perfluorooctane sulfonate, 2-linoleoylglycerol (18:2), and sphingadienine.

CONCLUSIONS

We generated cross-cohort metagenomic signatures of CRC, some of which linked to in-house CRC-associated serum metabolites. The microbial and metabolic shifts may have wide population relevance.

Metabolic perturbations in human hepatocytes induced by bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate exposure: Insights from high-coverage quantitative metabolomics

Bis (2-ethylhexyl)-2,3,4,5-tetrabromophthalate (TBPH) is an extensively used novel brominated flame retardant that is present ubiquitously in the environment and in biota. However, there is inadequate data on its potential hepatotoxicity to humans. In this study, high-coverage quantitative metabolomics based on ¹²C-/¹³C-dansylation labeling LC-MS was performed for the first time to assess the metabolic perturbations and underlying mechanisms of TBPH on human hepatocytes. HepG2 cells were exposed to TBPH at dosages of 0.1,1,10 μM for 24 or 72 h. Overall, 1887 and 1364 amine/phenol-containing metabolites were relatively quantified in cells and culture supernatant. Our results revealed that exposure to 0.1 μM TBPH showed little adverse effects, whereas exposure to 10 μM TBPH for 24 h enhanced intracellular protein catabolism and disrupted energy and lipid homeostasis-related pathways such as histidine metabolism, pantothenate and CoA biosynthesis, alanine, aspartate and glutamate metabolism. Nevertheless, most of these perturbations returned to the same levels as controls after 72 h of exposure. Additionally, prolonged TBPH exposure increased oxidative stress, as reflected by marked disturbances in taurine metabolism. This study sensitively revealed the dysregulations of intracellular and extracellular metabolome induced by TBPH, providing a comprehensive understanding of metabolic responses of cells to novel brominated flame retardants.

The Role of Circulating Protein and Metabolite Biomarkers in the Development of Pancreatic Ductal Adenocarcinoma (PDAC): A Systematic Review and Meta-analysis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and this is attributed to it being diagnosed at an advanced stage. Understanding the pathways involved in initial development may improve early detection strategies. This systematic review assessed the association between circulating protein and metabolite biomarkers and PDAC development.

METHODS

A literature search until August 2020 in MEDLINE, EMBASE, and Web of Science was performed. Studies were included if they assessed circulating blood, urine, or salivary biomarkers and their association with PDAC risk. Quality was assessed using the Newcastle-Ottawa scale for cohort studies. Random-effects meta-analyses were used to calculate pooled relative risk.

RESULTS

A total of 65 studies were included. Higher levels of glucose were found to be positively associated with risk of developing PDAC [n = 4 studies; pooled relative risk (RR): 1.61; 95% CI: 1.16-2.22]. Additionally, an inverse association was seen with pyridoxal 5'-phosphate (PLP) levels (n = 4 studies; RR: 0.62; 95% CI: 0.44-0.87). Meta-analyses showed no association between levels of C-peptide, members of the insulin growth factor signaling pathway, C-reactive protein, adiponectin, 25-hydroxyvitamin D, and folate/homocysteine and PDAC risk. Four individual studies also reported a suggestive positive association of branched-chain amino acids with PDAC risk, but due to differences in measures reported, a meta-analysis could not be performed.

CONCLUSIONS

Our pooled analysis demonstrates that higher serum glucose levels and lower levels of PLP are associated with risk of PDAC.

IMPACT

Glucose and PLP levels are associated with PDAC risk. More prospective studies are required to identify biomarkers for early detection.

Metabolomic analysis of serum alpha-tocopherol among men in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study

BACKGROUND/OBJECTIVES

The role of vitamin E in chronic disease risk remains incompletely understood, particularly in an un-supplemented state, and evidence is sparse regarding the biological actions and pathways involved in its influence on health outcomes. Identifying vitamin-E-associated metabolites through agnostic metabolomics analyses can contribute to elucidating the specific associations and disease etiology. This study aims to investigate the association between circulating metabolites and serum α-tocopherol concentration in an un-supplemented state.

SUBJECTS/METHODS

Metabolomic analysis of 4,294 male participants was conducted based on pre-supplementation fasting serum in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study. The associations between 1,791 known metabolites measured by ultra-high-performance LC-MS/GC-MS and HPLC-determined α-tocopherol concentration were estimated using multivariable linear regression. Differences in metabolite levels per unit difference in α-tocopherol concentration were calculated as standardized β-coefficients and standard errors.

RESULTS

A total of 252 metabolites were associated with serum α-tocopherol at the Bonferroni-corrected p value (p < 2.79 × 10^-5). Most of these metabolites were of lipid and amino acid origin, with the respective subclasses of dicarboxylic fatty acids, and valine, leucine, and isoleucine metabolism, being highly represented. Among lipids, the strongest signals were observed for linoleoyl-arachidonoyl-glycerol (18:2/20:4)[2](β = 0.149; p = 8.65 × 10^-146) and sphingomyelin (D18:2/18:1) (β = 0.035; p = 1.36 × 10^-30). For amino acids, the strongest signals were aminoadipic acid (β = 0.021; p = 5.01 × 10^-13) and l-leucine (β = 0.007; p = 1.05 × 10^-12).

CONCLUSIONS

The large number of metabolites, particularly lipid and amino acid compounds associated with serum α-tocopherol provide leads regarding potential mechanisms through which vitamin E influences human health, including its role in cardiovascular disease and cancer.

Potential Novel Serum Metabolic Markers Associated With Progression of Prediabetes to Overt Diabetes in a Chinese Population

BACKGROUND

Identifying the metabolite profile of individuals with prediabetes who turned to type 2 diabetes (T2D) may give novel insights into early T2D interception. The purpose of this study was to identify metabolic markers that predict the development of T2D from prediabetes in a Chinese population.

METHODS

We used an untargeted metabolomics approach to investigate the associations between serum metabolites and risk of prediabetes who turned to overt T2D (n=153, mean follow up 5 years) in a Chinese population (REACTION study). Results were compared with matched controls who had prediabetes at baseline [age: 56 ± 7 years old, body mass index (BMI): 24.2 ± 2.8 kg/m2] and at a 5-year follow-up [age: 61 ± 7 years old, BMI: 24.5 ± 3.1 kg/m2]. Confounding factors were adjusted and the associations between metabolites and diabetes risk were evaluated with multivariate logistic regression analysis. A 10-fold cross-validation random forest classification (RFC) model was used to select the optimal metabolites panels for predicting the development of diabetes, and to internally validate the discriminatory capability of the selected metabolites beyond conventional clinical risk factors.

FINDINGS

Metabolic alterations, including those associated with amino acid and lipid metabolism, were associated with an increased risk of prediabetes progressing to diabetes. The most important metabolites were inosine [odds ratio (OR) = 19.00; 95% confidence interval (CI): 4.23-85.37] and carvacrol (OR = 17.63; 95% CI: 4.98-62.34). Thirteen metabolites were found to improve T2D risk prediction beyond eight conventional T2D risk factors [area under the curve (AUC) was 0.98 for risk factors + metabolites vs 0.72 for risk factors, P < 0.05].

INTERPRETATIONS

Use of the metabolites identified in this study may help determine patients with prediabetes who are at highest risk of progressing to diabetes.

Hypergraph-based logistic matrix factorization for metabolite-disease interaction prediction

MOTIVATION

Function-related metabolites, the terminal products of the cell regulation, show a close association with complex diseases. The identification of disease-related metabolites is critical to the diagnosis, prevention and treatment of diseases. However, most existing computational approaches build networks by calculating pairwise relationships, which is inappropriate for mining higher-order relationships.

RESULTS

In this study, we presented a novel approach with hypergraph-based logistic matrix factorization, HGLMF, to predict the potential interactions between metabolites and disease. First, the molecular structures and gene associations of metabolites and the hierarchical structures and GO functional annotations of diseases were extracted to build various similarity measures of metabolites and diseases. Next, the kernel neighborhood similarity of metabolites (or diseases) was calculated according to the completed interactive network. Second, multiple networks of metabolites and diseases were fused, respectively, and the hypergraph structures of metabolites and diseases were built. Finally, a logistic matrix factorization based on hypergraph was proposed to predict potential metabolite-disease interactions. In computational experiments, HGLMF accurately predicted the metabolite-disease interaction, and performed better than other state-of-the-art methods. Moreover, HGLMF could be used to predict new metabolites (or diseases). As suggested from the case studies, the proposed method could discover novel disease-related metabolites, which has been confirmed in existing studies.

AVAILABILITY AND IMPLEMENTATION

The codes and dataset are available at: https://github.com/Mayingjun20179/HGLMF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolomics Study Reveals Systematic Metabolic Dysregulation and Early Detection Markers Associated with Incident Pancreatic Cancer

Biomarkers for early detection of pancreatic cancer are in urgent need. To explore systematic circulating metabolites unbalance and identify potential biomarkers for pancreatic cancer in prospective Chinese cohorts, we conducted an untargeted metabolomics study in subjects with incident pancreatic cancer and matched controls (n=192) from the China Cardiometabolic Disease and Cancer Cohort (4C) Study. We characterized 998 metabolites in baseline serum and calculated 156 product-to-precursor ratios based on the KEGG database. The identified metabolic profiling revealed systematic metabolic network disorders before pancreatic cancer diagnosis. Forty-five metabolites or product-to-precursor ratios showed significant associations with pancreatic cancer (P < 0.05 and FDR < 0.1), revealing abnormal metabolism of amino acids (especially alanine, aspartate and glutamate), lipids (especially steroid hormone, vitamins, nucleotides and peptides. A novel metabolite panel containing aspartate/alanine (OR [95% CI]: 1.97 [1.31-2.94]), androstenediol monosulfate (0.69 [0.49-0.97]) and glycylvaline (1.68 [1.04-2.70]) was significantly associated with risk of pancreatic cancer. Area under the receiver operating characteristic curves (AUCs) was improved from 0.573 (reference model of CA 19-9) to 0.721. The novel metabolite panel was validated in an independent cohort with AUC improved from 0.529 to 0.661. These biomarkers may have a potential value in early detection of pancreatic cancer. This article is protected by copyright. All rights reserved.

Circulating Tissue Polypeptide-Specific Antigen in Pre-Diagnostic Pancreatic Cancer Samples

Simple Summary

Detecting cancer early significantly increases the chances of successful (surgical) treatment. Pancreatic cancer is one of the deadliest cancer forms, since it is usually discovered at a late and already spread stage. Finding biomarkers showing pancreatic cancer at an early stage is a possible approach to early detection and improved treatment. The aim of our study was to assess the potential of tissue polypeptide specific antigen (TPS) as a biomarker for early pancreatic cancer detection. We studied TPS levels in blood plasma samples from a population-based biobank in Västerbotten, Sweden that were collected before individuals were diagnosed with pancreatic cancer. Although TPS levels are raised at diagnosis, this occurs late, and thus TPS does not seem to hold promise as an early detection marker for pancreatic cancer.

Abstract

Early detection of pancreatic ductal adenocarcinoma (PDAC) is challenging, and late diagnosis partly explains the low 5-year survival. Novel and sensitive biomarkers are needed to enable early PDAC detection and improve patient outcomes. Tissue polypeptide specific antigen (TPS) has been studied as a biomarker in PDAC diagnostics, and it has previously been shown to reflect clinical status better than the ‘golden standard’ biomarker carbohydrate antigen 19-9 (CA 19-9) that is most widely used in the clinical setting. In this cross-sectional case-control study using pre-diagnostic plasma samples, we aim to evaluate the potential of TPS as a biomarker for early PDAC detection. Furthermore, in a subset of individuals with multiple samples available at different time points before diagnosis, a longitudinal analysis was used. We assessed plasma TPS levels using enzyme-linked immunosorbent assay (ELISA) in 267 pre-diagnostic PDAC plasma samples taken up to 18.8 years before clinical PDAC diagnosis and in 320 matched healthy controls. TPS levels were also assessed in 25 samples at PDAC diagnosis. Circulating TPS levels were low both in pre-diagnostic samples of future PDAC patients and in healthy controls, whereas TPS levels at PDAC diagnosis were significantly increased (odds ratio 1.03; 95% confidence interval: 1.01–1.05) in a logistic regression model adjusted for age. In conclusion, TPS levels increase late in PDAC progression and hold no potential as a biomarker for early detection.

Akkermansia Muciniphila Potentiates the Antitumor Efficacy of FOLFOX in Colon Cancer

FOLFOX (oxaliplatin, fluorouracil and calcium folinate) is the first-line chemotherapy regimen for colon cancer therapy in the clinic. It provides superior efficacy than oxaliplatin alone, but the underlying mechanism remains unclear. In the present study, pharmacomicrobiomics integrated with metabolomics was conducted to uncover the role of the gut microbiome behind this. First, in vivo study demonstrated that FOLFOX exhibited better efficacy than oxaliplatin alone in colon cancer animal models. Second, 16S rDNA gene sequencing analysis showed that the abundance of Akkermansia muciniphila (A. muciniphila) remarkably increased in the FOLFOX treated individuals and positively correlated with the therapeutic effect. Third, further exploration confirmed A. muciniphila colonization significantly enhanced the anti-cancer efficacy of FOLFOX. Last, metabolomics analysis suggested dipeptides containing branched-chain amino acid (BCAA) might be responsible for gut bacteria mediated FOLFOX efficacy. In conclusion, our study revealed the key role of A. muciniphila in mediating FOLFOX efficacy, and manipulating A. muciniphila might serve as a novel strategy for colon cancer therapy.

Downregulation of 5-hydroxymethylcytosine is an early event in pancreatic tumorigenesis

Epigenetic alterations are increasingly recognized as important contributors to the development and progression of pancreatic ductal adenocarcinoma. 5-hydroxymethylcytosine (5hmC) is an epigenetic DNA mark generated through the ten-eleven translocation (TET) enzyme-mediated pathway and is closely linked to gene activation. However, the timing of alterations in epigenetic regulation in the progression of pancreatic neoplasia is not well understood. In this study, we hypothesized that aberrant expression of ten-eleven translocation methylcytosine dioxygenase 1 (TET1) and subsequent global 5hmC alteration are linked to early tumorigenesis in the pancreas. Therefore, we evaluated alterations of 5hmC and TET1 levels using immunohistochemistry in pancreatic neoplasms (n = 380) and normal ducts (n = 118). The study cohort included representation of the full spectrum of precancerous lesions from low- and high-grade pancreatic intraepithelial neoplasia (n = 95), intraductal papillary mucinous neoplasms (all subtypes, n = 129), intraductal oncocytic papillary neoplasms (n = 12), and mucinous cystic neoplasms (n = 144). 5hmC and TET1 were significantly downregulated in all types of precancerous lesion and associated invasive pancreatic ductal adenocarcinomas compared with normal ductal epithelium (all p < 0.001), and expression of 5hmC positively correlated with expression of TET1. Importantly, downregulation of both 5hmC and TET1 was observed in most low-grade precancerous lesions. There were no clear associations between 5hmC levels and clinicopathological factors, thereby suggesting a common epigenetic abnormality across precancerous lesions. We conclude that downregulation of 5hmC and TET1 is an early event in pancreatic tumorigenesis. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Chlorogenic acid depresses cellular bioenergetics to suppress pancreatic carcinoma through modulating c-Myc-TFR1 axis

Pancreatic ductal adenocarcinoma (PDAC) is severe malignant tumor in human, the outcomes of PDAC is extremely poor. Here, we evaluated the potential anti-tumor activity of chlorogenic Acid (CA) in PDAC. Here, we found CA was effective to suppress PDAC cell growth in vitro and in vivo. Importantly, we found overall oxygen consumption rate was significantly decreased in CA dose-dependent manner. We also found glycolysis reverse was decreased in CA-treated cells, while basal glycolysis and glycolytic capacity were not significantly changed. Mechanistically, we demonstrated TFR1 could be a novel downstream target of CA, which is essential for PDAC cell growth and cellular bioenergetics maintenance. Furthermore, we validated that CA-reduced c-Myc resulted to down-regulation of TFR1, which contributes to mitochondrial respiration dysfunction and cell growth delay. Together, this study indicates that CA suppresses PDAC cell growth through targeting c-Myc-TFR1 axis and suggests CA could be considered as a promising compound for PDAC treatment.

Deubiquitinase USP18 promotes the progression of pancreatic cancer via enhancing the Notch1-c-Myc axis

The dysregulation of deubiquitinating enzymes (DUBs), which regulate the stability of most cellular proteins, has been implicated in many human diseases, including cancers. Thus, DUBs can be considered potential therapeutic targets for many cancers. However, the role of deubiquitinase ubiquitin-specific protease 18 (USP18) in pancreatic cancer remains unknown. Here, we found that the deubiquitinase ubiquitin-specific protease 18 (USP18) is significantly upregulated in pancreatic cancer and is correlated with a shorter median overall and relapse-free survival. A functional assay demonstrated that overexpression of USP18 resulted in increased proliferation of pancreatic cancer cells. Conversely, these phenomena were reversed after USP18 was silenced in pancreatic cancer cells. Further investigation revealed that USP18 promoted cell progression by increasing c-Myc expression, which has been reported to control pancreatic cancer progression, and our data demonstrated that c-Myc is key for USP18-mediated pancreatic cancer cell progression in vitro and in vivo. Moreover, we found that USP18 promoted pancreatic cancer progression via upregulation of Notch-1-dependent c-Myc. Mechanistically, USP18 interacts with and removes K48-linked ubiquitin chains from Notch1, thereby stabilizing Notch1 and promoting the Notch1-c-Myc pathway. Our work identifies and validates USP18 as a pancreatic cancer oncogene and provides a potential druggable target for this intractable disease.