Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination

Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics

Clinical metabolomics is a rapidly expanding field focused on identifying molecular biomarkers to aid in the efficient diagnosis and treatment of human diseases. Variations in study design, metabolomics methodologies, and investigator protocols raise serious concerns about the accuracy and reproducibility of these potential biomarkers. The explosive growth of the field has led to the recent availability of numerous replicate clinical studies, which permits an evaluation of the consistency of biomarkers identified across multiple metabolomics projects. Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related death and has the lowest five-year survival rate primarily due to the lack of an early diagnosis and the limited treatment options. Accordingly, PDAC has been a popular target of clinical metabolomics studies. We compiled 24 PDAC metabolomics studies from the scientific literature for a detailed meta-analysis. A consistent identification across these multiple studies allowed for the validation of potential clinical biomarkers of PDAC while also highlighting variations in study protocols that may explain poor reproducibility. Our meta-analysis identified 10 metabolites that may serve as PDAC biomarkers and warrant further investigation. However, 87% of the 655 metabolites identified as potential biomarkers were identified in single studies. Differences in cohort size and demographics, p-value choice, fold-change significance, sample type, handling and storage, data collection, and analysis were all factors that likely contributed to this apparently large false positive rate. Our meta-analysis demonstrated the need for consistent experimental design and normalized practices to accurately leverage clinical metabolomics data for reliable and reproducible biomarker discovery.

Tumor metabolic alterations after neoadjuvant chemoradiotherapy predict postoperative recurrence in patients with pancreatic cancer

OBJECTIVE

We investigated the metabolic changes in pancreatic ductal adenocarcinoma to identify the mechanisms of treatment response of neoadjuvant chemoradiation therapy.

METHODS

Frozen tumor and non-neoplastic pancreas tissues were prospectively obtained from 88 patients with pancreatic ductal adenocarcinoma who underwent curative-intent surgery. Sixty-two patients received neoadjuvant chemoradiation therapy and 26 patients did not receive neoadjuvant therapy (control group). Comprehensive analysis of metabolites in tumor and non-neoplastic pancreatic tissue was performed by capillary electrophoresis-mass spectrometry.

RESULTS

Capillary electrophoresis-mass spectrometry detected 90 metabolites for analysis among more than 500 ionic metabolites quantified. There were significant differences in 27 tumor metabolites between the neoadjuvant chemoradiation therapy and control groups. There were significant differences in eight metabolites [1-MethylnNicotinamide, Carnitine, Glucose, Glutathione (red), N-acetylglucosamine 6-phosphate, N-acetylglucosamine 1-phosphate, UMP, Phosphocholine] between good responder and poor responder for neoadjuvant chemoradiation therapy. Among these metabolites, phosphocholine, Carnitine and Glutathione were associated with recurrence-free survival only in the neoadjuvant chemoradiation therapy group. Microarray confirmed marked gene suppression of choline transporters [CTL1-4 (SLC44A1-44A4)] in pancreatic ductal adenocarcinoma tissue of neoadjuvant chemoradiation therapy group.

CONCLUSION

The present study identifies several important metabolic consequences and potential neoadjuvant chemoradiation therapy targets in pancreatic ductal adenocarcinoma. Choline metabolism is one of the key pathways involved in recurrence of the patients with pancreatic ductal adenocarcinoma who received neoadjuvant chemoradiation therapy.

GC-MS based metabolite fingerprinting of serous ovarian carcinoma and benign ovarian tumor

The aim of this study is to identify urinary metabolomic profile of benign and malign ovarian tumors patients. Samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and metabolomic tools to define biomarkers that cause differentiation between groups. 7 metabolites were found to be different in patients with ovarian cancer (OC) and benign tumors (BT). R2Y and Q2 values were found to be 0.670 and 0.459, respectively. L-tyrosine, glycine, stearic acid, turanose and L-threonine metabolites were defined as prominent biomarkers. The sensitivity of the model was calculated as 90.72% and the specificity as 82.09%. In the pathway analysis, glutathione metabolism, aminoacyl-tRNA biosynthesis, glycine serine and threonine metabolic pathway, primary bile acid biosynthesis pathways were found to be important. According to the t-test, 29 metabolites were found to be significant in urine samples of OC patients and healthy controls (HC). R2Y and Q2 values were found to be 0.8170 and 0.749, respectively. These results showed that the model has high compatibility and predictive power. Benzoic acid, L-threonine, L-pyroglutamic acid, creatinine and 3,4-dihydroxyphenylacetic acid metabolites were determined as prominent biomarkers. The sensitivity of the model was calculated as 93.81% and the specificity as 98.59%. Glycine serine and threonine metabolic pathway, glutathione metabolism and aminoacyl-tRNA biosynthesis pathways were determined important in OC patients and HC. The R2Y, Q2, sensitivity and specificity values in the urine samples of BT patients and HC were found to be 0.869, 0.794, 91.75, 97.01% and 97.18%, respectively. L-threonine, L-pyroglutamic acid, benzoic acid, creatinine and pentadecanol metabolites were determined as prominent biomarkers. Valine, leucine and isoleucine biosynthesis and aminoacyl-tRNA biosynthesis were significant. In this study, thanks to the untargeted metabolomic approach and chemometric methods, every group was differentiated from the others and prominent biomarkers were determined.

Inhibition of JAK2/STAT3 signaling pathway by panaxadiol limits the progression of pancreatic cancer

Pancreatic cancer is the fourth leading cause of cancer-related death with the characteristics of chemoresistance and early metastasis. Panaxadiol, a triterpenoid saponin extracted from the roots of American ginseng, has been proved to display anti-tumor activity in colon cancer. In this study, we found panaxadiol significantly inhibited proliferation, and induced apoptosis in human pancreatic cancer cell lines PANC-1 and Patu8988 in a dose-dependent manner. Furthermore, the expression of apoptosis-related proteins (Bax, Bcl2, Cleaved-caspase3) was detected via western blot and immunofluorescence staining. In addition, panaxadiol was also found to inhibit the migration of pancreatic cancer cells by wound healing and transwell assays. In vivo, the growth of xenograft pancreatic cancer models was also notably suppressed by panaxadiol compared to the control group. Moreover, the down-regulation of JAK2-STAT3 signaling pathway was responsible for the underlying pro-apoptosis mechanism of panaxadiol, and this result was in good agreement with molecular docking analysis between panaxadiol and STAT3. In conclusion, our work comprehensively explored the anti-tumor ability in PANC-1 and Patu8988 cells of panaxadiol and provided a potential choice for the clinical treatment of pancreatic cancer patients.

Metabolic Profiling Revealed Prediction Biomarkers for Infantile Hemangioma in Umbilical Cord Blood Sera: A Prospective Study

Infantile hemangioma (IH), the most common benign tumor in infancy, mostly arises and has rapid growth before 3 months of age. Because irreversible skin changes occur in the early proliferative stage, early medical treatment is essential to reduce the permanent sequelae caused by IH. Yet there are still no early screening biomarkers for IH before its visible emergence. This study aimed to explore prediction biomarkers using noninvasive umbilical cord blood (UCB). A prospective study of the metabolic profiling approach was performed on UCB sera from 28 infants with IH and 132 matched healthy controls from a UCB population comprising over 1500 infants (PeptideAtlas: PASS01675) using liquid chromatography-mass spectrometry. The metabolic profiling results exhibited the characteristic metabolic aberrance of IH. Machine learning suggested a panel of biomarkers to predict the occurrence of IH, with the area under curve (AUC) values in the receiver operating characteristic analysis all >0.943. Phenylacetic acid had potential to predict infants with large IH (diameter >2 cm) from those with small IH (diameter <2 cm), with an AUC of 0.756. The novel biomarkers in noninvasive UCB sera for predicting IH before its emergence might lead to a revolutionary clinical utility.

Differential Tissue Metabolic Signatures in IgG4-Related Ophthalmic Disease and Orbital Mucosa-Associated Lymphoid Tissue Lymphoma

Purpose

To identify tissue metabolomic profiles in biopsy specimens with IgG4-related ophthalmic disease (IgG4-ROD) and mucosa-associated lymphoid tissue (MALT) lymphoma and investigate their potential implication in the disease pathogenesis and biomarkers.

Methods

We conducted a comprehensive analysis of the metabolomes and lipidomes of biopsy-proven IgG4-ROD (n = 22) and orbital MALT lymphoma (n = 21) specimens and matched adjacent microscopically normal adipose tissues using liquid chromatography time-of-flight mass spectrometry. The altered metabolomic profiles were visualized by heat map and principal component analysis. Metabolic pathway analysis was performed by Metabo Analyst 4.0 using differentially expressed metabolites. The diagnostic performance of the metabolic markers was evaluated using receiver operating characteristic curves. Machine learning algorithms were implemented by random forest using the R environment. Finally, an independent set of 18 IgG4-ROD and 17 orbital MALT lymphoma specimens were used to validate the identified biomarkers.

Results

The principal component analysis showed a significant difference of both IgG4-ROD and orbital MALT lymphoma for biopsy specimens and controls. Interestingly, lesions in IgG4-ROD were uniquely enriched in arachidonic metabolism, whereas those in orbital MALT lymphoma were enriched in tricarboxylic acid cycle metabolism. We identified spermine as the best discriminator between IgG4-ROD and orbital MALT lymphoma, and the area under the receiver operating characteristic curve of the spermine to discriminate between the two diseases was 0.89 (95% confidence interval, 0.803–0.984). A random forest model incorporating a panel of five metabolites showed a high area under the receiver operating characteristic curve value of 0.983 (95% confidence interval, 0.981–0.984). The results of validation revealed that four tissue metabolites: N¹,N¹²-diacetylspermine, spermine, malate, and glycolate, had statistically significant differences between IgG4-ROD and orbital MALT lymphoma with receiver operating characteristic values from 0.708 to 0.863.

Conclusions

These data revealed the characteristic differences in metabolomic profiles between IgG4-ROD and orbital MALT lymphoma, which may be useful for developing new diagnostic biomarkers and elucidating the pathogenic mechanisms of these common orbital lymphoproliferative disorders.

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

OBJECTIVE

To assess whether prediagnostic metabolites were associated with incident pancreatic ductal adenocarcinoma (PDAC) in a prospective cohort study.

DESIGN

We conducted an untargeted analysis of 554 known metabolites measured in prediagnostic serum (up to 24 years) to determine their association with incident PDAC in a nested case-control study of male smokers (372 matched case-control sets) and an independent nested case-control study that included women and non-smokers (107 matched sets). Metabolites were measured using Orbitrap Elite or Q-Exactive high-resolution/accurate mass spectrometers. Controls were matched to cases by age, sex, race, date of blood draw, and follow-up time. We used conditional logistic regression adjusted for age to calculate ORs and 95% CIs for a 1 SD increase in log-metabolite level separately in each cohort and combined the two ORs using a fixed-effects meta-analysis.

RESULTS

Thirty-one metabolites were significantly associated with PDAC at a false discovery rate <0.05 with 12 metabolites below the Bonferroni-corrected threshold (p<9.04×10^-5). Similar associations were observed in both cohorts. The dipeptides glycylvaline, aspartylphenylalanine, pyroglutamylglycine, phenylalanylphenylalanine, phenylalanylleucine and tryptophylglutamate and amino acids aspartate and glutamate were positively while the dipeptides tyrosylglutamine and α-glutamyltyrosine, fibrinogen cleavage peptide DSGEGDFXAEGGGVR and glutathione-related amino acid cysteine-glutathione disulfide were inversely associated with PDAC after Bonferroni correction. Five top metabolites demonstrated significant time-varying associations (p<0.023) with the strongest associations observed 10-15 years after participants' blood collection and attenuated thereafter.

CONCLUSION

Our results suggest that prediagnostic metabolites related to subclinical disease, γ-glutamyl cycle metabolism and adiposity/insulin resistance are associated with PDAC.

Overexpression of hydroxyproline via EGLN/HIF1A is associated with distant metastasis in pancreatic cancer

For pancreatic cancer, the probability of distant metastasis can help choose the best course of treatment. The aim of this study is to establish the efficacy of hydroxyproline as a biomarker for distant metastasis for pancreatic cancer and to clarify the mechanism of EGLN/HIF1A axis that controls the invasion and metastasis. Metabolites (hydroxyproline) and genes (EGLN2 and EGLN3) were identified by metabolome analysis of the serum with pancreatic cancers with and without distant metastasis. The mechanism of EGLN/HIF1A axis including angiogenesis was examined in pancreatic cancer cells. Hydroxyproline associated with these mechanisms was evaluated to suggest the association with overall survival in pancreatic cancer. Decreased expression of EGLN2 and EGLN3 in pancreatic cancer, via the HIF1A and TGF ß1 pathway, was associated with the induction of angiogenic factors, increased vascular invasion, and poor overall patient survival. Hydroxyproline concentrations were regulated via the HIF1A pathway by EGLN2 and EGLN3, and that increased concentrations of hydroxyproline promote the invasion and metastasis of pancreatic cancer cells. These results suggested that the expression of hydroxyproline through the HIF1A pathway induced by EGLN2 and EGLN3 could be a surrogate marker for treatment and might predict distant metastasis in pancreatic cancer.

A Novel Serum Metabolomic Profile for the Differential Diagnosis of Distal Cholangiocarcinoma and Pancreatic Ductal Adenocarcinoma

The diagnosis of adenocarcinomas located in the pancreas head, i.e., distal cholangiocarcinoma (dCCA) and pancreatic ductal adenocarcinoma (PDAC), constitutes a clinical challenge because they share many symptoms, are not easily distinguishable using imaging techniques and accurate biomarkers are not available. Searching for biomarkers with potential usefulness in the differential diagnosis of these tumors, we have determined serum metabolomic profiles in healthy controls and patients with dCCA, PDAC or benign pancreatic diseases (BPD). Ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) analysis was performed in serum samples from dCCA (n = 34), PDAC (n = 38), BPD (n = 42) and control (n = 25) individuals, divided into discovery and validation cohorts. This approach permitted 484 metabolites to be determined, mainly lipids and amino acids. The analysis of the results led to the proposal of a logistic regression model able to discriminate patients with dCCA and PDAC (AUC value of 0.888) based on the combination of serum levels of nine metabolites (acylcarnitine AC(16:0), ceramide Cer(d18:1/24:0), phosphatidylcholines PC(20:0/0:0) and PC(O-16:0/20:3), lysophosphatidylcholines PC(20:0/0:0) and PC(0:0/20:0), lysophosphatidylethanolamine PE(P-18:2/0:0), and sphingomyelins SM(d18:2/22:0) and SM(d18:2/23:0)) and CA 19-9. In conclusion, we propose a novel specific panel of serum metabolites that can help in the differential diagnosis of dCCA and PDAC. Further validation of their clinical usefulness in prospective studies is required.

Developing a preoperative serum metabolome-based recurrence-predicting nomogram for patients with resected pancreatic ductal adenocarcinoma

We investigated the potential application of preoperative serum metabolomes in predicting recurrence in patients with resected pancreatic cancer. From November 2012 to June 2014, patients who underwent potentially curative pancreatectomy for pancreatic ductal adenocarcinoma were examined. Among 57 patients, 32 were men; 42 had pancreatic head cancers. The 57 patients could be clearly categorized into two main clusters using 178 preoperative serum metabolomes. Patients within cluster 2 showed earlier tumor recurrence, compared with those within cluster 1 (p = 0.034). A nomogram was developed for predicting the probability of early disease-free survival in patients with resected pancreatic cancer. Preoperative cancer antigen (CA) 19–9 levels and serum metabolomes PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful preoperative clinical variables with which to predict 6-month and 1-year cancer recurrence-free survival after radical pancreatectomy, with a Harrell’s concordance index of 0.823 (95% CI: 0.750–0.891) and integrated area under the curve of 0.816 (95% CI: 0.736–0.893). Patients with resected pancreatic cancer could be categorized according to their different metabolomes to predict early cancer recurrence. Preoperative detectable parameters, serum CA 19–9, PC.aa.C38_4, PC.ae.C42_5, and PC.ae.C38_6 were the most powerful predictors of early recurrence of pancreatic cancer.

The metabolic time line of pancreatic cancer: Opportunities to improve early detection of adenocarcinoma

BACKGROUND

A reliable biomarker to detect pancreatic ductal adenocarcinoma (PDAC) continues to be elusive. With employing metabolomics we hypothesize that a broader analysis of systemic blood can differentiate different stages of PDAC.

METHODS

Patients undergoing pancreatic resection had plasma samples grouped by diagnosis and assayed with mass spectrometry. 10 per group [neuroendocrine (PNET), intraductal papillary mucinous neoplasm (IPMN), localized PDAC, locally advanced PDAC, and metastatic] were analyzed to assess if metabolites could delineation different stages of adenocarcinoma.

RESULTS

Of the 215 metabolites measured, four had a stronger correlation to disease burden than CA19-9. However, none of these metabolites differentiated stepwise progression in malignancy. Principal component analysis identified five metabolic components. Each cancer cohort was characterized by a unique combination of components, two components were predictors of PDCA stages.

CONCLUSIONS

Enhanced metabolomic analysis identified metabolic pathways that may assist in differentiating PDCA stages that do not occur in a linear stepwise progression.

Metabonomic Variation of Exopolysaccharide from Rhizopus nigricans on AOM/DSS-Induced Colorectal Cancer in Mice

BACKGROUND

Colorectal cancer (CRC), which occurs at the junction of the rectum and sigmoid colon, is a common malignancy associated with poor prognosis and high mortality worldwide. The exopolysaccharide (EPS1-1), isolated from the fermentation broth of Rhizopus nigricans (R. nigricans), has been reported to possess anti-CRC properties. However, the metabolic alterations caused by azoxymethane (AOM) and dextran sulfate sodium (DSS) are still unknown.

METHODS

In the present study, a mice colon cancer model was established by treatment with AOM/DSS. LC-MS/MS-based metabolomics studies were performed to analyze metabolic alterations at the tissue level. Partial least squares discriminant analysis (PLS-DA) was used to identify differentially expressed metabolites.

RESULTS

Nineteen distinct metabolites were identified that were associated with disruptions in the following pathways: biosynthesis of unsaturated fatty acids, pyrimidine metabolism, phenylalanine metabolism, fatty acid metabolism, folate biosynthesis, and inositol phosphate metabolism. Furthermore, six significantly altered metabolites were involved in these six pathways. Compared with the Model group, the expression of cytosine, deoxyuridine, 20-hydroxy-leukotriene E4, and L-homocysteic acid was lower, whereas that of 2-dehydro-3-deoxy-6-phospho-D-gluconic acid and hematoporphyrin was higher in the EPS1-1 group.

CONCLUSION

The results of multivariate statistical analysis demonstrate a promising application of the above metabolites by EPS1-1 in CRC therapy. Deeper understanding of the related mechanism warrants further investigation.

Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways

Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.

Diagnosis of acute pediatric appendicitis from children with inflammatory diseases by combination of metabolic markers and inflammatory response variables

BACKGROUND

The discovery of new metabolic markers may be helpful for early diagnosis of acute pediatric appendicitis (APA). However, no studies have been reported regarding identification of potential metabolic markers for the APA diagnosis by metabonomics.

METHODS

Serum samples of APA (n=32), non-appendicitis inflammation (NAI, n=32) and healthy children (HS, n=65) were analyzed by the 1H NMR-based metabonomics. A logistic regression model was established to screen the most efficient markers combinations for classification. Forty double-blind samples were further validated the model.

RESULTS

Nine blood metabolites that were different in the APA group from other groups were identified. To differentiate APA from HS, single variable of acetate, formate, white blood cell (WBC) and C-reactive protein (CRP) showed a high diagnostic value (area under the receiver operating characteristic [AUROC]<0.92), while they had a weak diagnostic value (AUROC<0.77) for identifying the APA and NAI. By contrast, the AUROC values of leucine (0.799) were higher than that of WBC and CRP. A combination of five variables, i.e. leucine, lactate, betaine, WBC and CRP, showed a high diagnostic value (AUROC=0.973) for the APA discriminating from the NAI, and the sensitivity and specificity were 93.8% and 93.7%, respectively. Further double-blind sample prediction showed that the accuracy of the model was 85% for 40 unknown samples.

CONCLUSIONS

The current study provides useful information in our understanding of the metabolic alterations associated with APA and indicates that measurement of these metabolites in serum effectively aids in the clinical identification of APA.

Recent advances in capillary electrophoresis-mass spectrometry: Instrumentation, methodology and applications

Capillary electrophoresis (CE) offers fast and high-resolution separation of charged analytes from small injection volumes. Coupled to mass spectrometry (MS), it represents a powerful analytical technique providing (exact) mass information and enables molecular characterization based on fragmentation. Although hyphenation of CE and MS is not straightforward, much emphasis has been placed on enabling efficient ionization and user-friendly coupling. Though several interfaces are now commercially available, research on more efficient and robust interfacing with nano-electrospray ionization (ESI), matrix-assisted laser desorption/ionization (MALDI) and inductively coupled plasma mass spectrometry (ICP) continues with considerable results. At the same time, CE-MS has been used in many fields, predominantly for the analysis of proteins, peptides and metabolites. This review belongs to a series of regularly published articles, summarizing 248 articles covering the time between June 2016 and May 2018. Latest developments on hyphenation of CE with MS as well as instrumental developments such as two-dimensional separation systems with MS detection are mentioned. Furthermore, applications of various CE-modes including capillary zone electrophoresis (CZE), nonaqueous capillary electrophoresis (NACE), capillary gel electrophoresis (CGE) and capillary isoelectric focusing (CIEF) coupled to MS in biological, pharmaceutical and environmental research are summarized.

Identification of ENTPD8 and cytidine in pancreatic cancer by metabolomic and transcriptomic conjoint analysis

To identify metabolic pathways that were perturbed in pancreatic cancer (PC), we investigated gene‐metabolite networks by integration of metabolomic and transcriptomic. In this research, we undertook the metabolomic study of 43 paired human PC samples, aiming to identify key metabolic alterations in PC. We also carried out in vitro experiments to validate that the key metabolite cytidine and its related gene ENTPD8 played an important role in PC cell proliferation. We screened out 13 metabolites differentially expressed in PC tissue (PCT) by liquid chromatography/mass spectrometry analysis on 34 metabolites, and the partial least square discrimination analysis results revealed that 9 metabolites among them were remarkably altered in PCT compared to adjacent noncancerous tissue (variable importance in projection >1, P < .05). Among the 9 metabolites, 7 might be potential biomarkers. The most significantly enriched metabolic pathway was pyrimidine metabolism. We analyzed 351 differentially expressed genes from The Cancer Genome Atlas and intersected them with Kyoto Encyclopedia of Genes and Genomes metabolic pathways. We found that ENTPD8 had a gene‐metabolite association with cytidine in the CTP dephosphorylation pathway. We verified by in vitro experiments that the CTP dephosphorylation pathway was changed in PCT compared with adjacent noncancerous tissue. ENTPD8 was downregulated in PCT, causing a reduction in cytidine formation and hence weakened CTP dephosphorylation in pyrimidine metabolism.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer

INTRODUCTION

Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients.

OBJECTIVES

In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

METHODS

PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Elevated Polyamines in Saliva of Pancreatic Cancer

Detection of pancreatic cancer (PC) at a resectable stage is still difficult because of the lack of accurate detection tests. The development of accurate biomarkers in low or non-invasive biofluids is essential to enable frequent tests, which would help increase the opportunity of PC detection in early stages. Polyamines have been reported as possible biomarkers in urine and saliva samples in various cancers. Here, we analyzed salivary metabolites, including polyamines, using capillary electrophoresis-mass spectrometry. Salivary samples were collected from patients with PC (n = 39), those with chronic pancreatitis (CP, n = 14), and controls (C, n = 26). Polyamines, such as spermine, N₁-acetylspermidine, and N₁-acetylspermine, showed a significant difference between patients with PC and those with C, and the combination of four metabolites including N₁-acetylspermidine showed high accuracy in discriminating PC from the other two groups. These data show the potential of saliva as a source for tests screening for PC.

Screening for Pancreatic Cancer: Current Status and Future Directions

Pancreatic ductal adenocarcinoma is a lethal disease for a multitude of reasons, including difficulty of early detection, early metastatic spread, and absence of more effective therapies. Even with the advent of newer systemic therapies, the 1-year survival for metastatic disease ranges from 17–23% and 5-year survival is <5%. This necessitates an urgent need for the development of more effective modalities for early detection, particularly due to the long latent period between the genomic cellular changes and the development of metastatic disease. Currently available biochemical and molecular markers have significant potential; however, they require further clinical validation. Endoscopic ultrasound is one of the most sensitive modalities used to both screen and sample lesions, but is limited to use in high-risk patients due to its invasive nature and associated risks. Although clinically meaningful progress has been made in screening the high-risk cohorts in terms of detection of pancreatic ductal adenocarcinoma, intraductal papillary mucinous neoplasms, and mucinous cystic neoplasms, leading to early diagnosis and treatment, nonselective population-based screening is not yet available for widespread use. Currently there is no consensus on the most appropriate screening protocol for early pancreatic cancer detection. In this review, we focus on understanding the potential role of molecular and radiogenomic markers in the early detection of pancreatic cancer.