Metabonomic alterations from pancreatic intraepithelial neoplasia to pancreatic ductal adenocarcinoma facilitate the identification of biomarkers in serum for early diagnosis of pancreatic cancer

Identification and analysis of pancreatic intraepithelial neoplasia: opportunities and challenges

Pancreatic intraepithelial neoplasia (PanIN) is the most common precursor lesion of pancreatic ductal adenocarcinoma (PDAC), which has poor prognosis with a short median overall survival of 6-12 months and a low 5-year survival rate of approximately 3%. It is crucial to remove PanIN lesions to prevent the development of invasive PDAC, as PDAC spreads rapidly outside the pancreas. This review aims to provide the latest knowledge on PanIN risk, pathology, cellular origin, genetic susceptibility, and diagnosis, while identifying research gaps that require further investigation in this understudied area of precancerous lesions. PanINs are classified into PanIN 1, PanIN 2, and PanIN 3, with PanIN 3 having the highest likelihood of developing into invasive PDAC. Differentiating between PanIN 2 and PanIN 3 is clinically significant. Genetic alterations found in PDAC are also present in PanIN and increase with the grade of PanIN. Imaging methods alone are insufficient for distinguishing PanIN, necessitating the use of genetic and molecular tests for identification. In addition, metabolomics technologies and miRNAs are playing an increasingly important role in the field of cancer diagnosis, offering more possibilities for efficient identification of PanIN. Although detecting and stratifying the risk of PanIN poses challenges, the combined utilization of imaging, genetics, and metabolomics holds promise for improving patient survival in this field.

Therapeutic potential of β-hydroxybutyrate in the management of pancreatic neoplasms: exploring novel diagnostic and treatment strategies

Pancreatic neoplasm, a highly aggressive and often fatal cancer, poses challenges due to late detection and nonspecific symptoms. Therefore, both early diagnosis and appropriate therapeutic approaches are necessary to augment the condition of these patients. Cancer cells undergo metabolic deregulation, which enables their proliferation, survival, and invasion. As a result, it is crucial to focus on the metabolic pathways in prevalent cancers and explore treatment strategies that target these pathways to control tumor growth effectively. This is particularly relevant in cancers like pancreatic cancer, which undergo numerous metabolic alterations. The ketogenic regimen, characterized by low carbohydrate and protein contents and high-fat sources, does not involve caloric restriction. This allows for the induction of ketogenesis and an increase in ketone bodies, while insulin and glucose levels remain low even after meals. This unique metabolic state may influence the tumor microenvironment. Given the lack of unanimous agreement on the precise role and mechanism of the ketogenic diet, this review aims to clarify the diagnostic value and accuracy of ketone bodies in various types of pancreatic tumors and explore the potential anti-cancer effects of the ketogenic diet when used alone or in conjunction with chemotherapy, also to determine the potential of the ketogenic diet to be used as adjuvant therapy. The outcomes of this study are instrumental in enhancing our understanding of the benefits and drawbacks associated with employing this diet for the management and diagnosis of pancreatic cancer.

Development of a metabolite calculator for diagnosis of pancreatic cancer

BACKGROUND

Carbohydrate antigen (CA) 19-9 is a known pancreatic cancer (PC) biomarker, but is not commonly used for general screening due to its low sensitivity and specificity. This study aimed to develop a serum metabolites-based diagnostic calculator for detecting PC with high accuracy.

METHODS

A targeted quantitative approach of direct flow injection-tandem mass spectrometry combined with liquid chromatography-tandem mass spectrometry was employed for metabolomic analysis of serum samples using an Absolute IDQ™ p180 kit. Integrated metabolomic analysis was performed on 241 pooled or individual serum samples collected from healthy donors and patients from nine disease groups, including chronic pancreatitis, PC, other cancers, and benign diseases. Orthogonal partial least squares discriminant analysis (OPLS-DA) based on characteristics of 116 serum metabolites distinguished patients with PC from those with other diseases. Sparse partial least squares discriminant analysis (SPLS-DA) was also performed, incorporating simultaneous dimension reduction and variable selection. Predictive performance between discrimination models was compared using a 2-by-2 contingency table of predicted probabilities obtained from the models and actual diagnoses.

RESULTS

Predictive values obtained through OPLS-DA for accuracy, sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were 0.9825, 0.9916, 0.9870, 0.9866, and 0.9870, respectively. The number of metabolite candidates was narrowed to 76 for SPLS-DA. The SPLS-DA-obtained predictive values for accuracy, sensitivity, specificity, balanced accuracy, and AUC were 0.9773, 0.9649, 0.9832, 0.9741, and 0.9741, respectively.

CONCLUSIONS

We successfully developed a 76 metabolome-based diagnostic panel for detecting PC that demonstrated high diagnostic performance in differentiating PC from other diseases.

The role of amino acid metabolism alterations in pancreatic cancer: From mechanism to application

The incidence of pancreatic cancer is increasing in both developed and developing Nations. In recent years, various research evidence suggested that reprogrammed metabolism may play a key role in pancreatic cancer tumorigenesis and development. Therefore, it has great potential as a diagnostic, prognostic and therapeutic target. Amino acid metabolism is deregulated in pancreatic cancer, and changes in amino acid metabolism can affect cancer cell status, systemic metabolism in malignant tumor patients and mistakenly involved in different biological processes including stemness, proliferation and growth, invasion and migration, redox state maintenance, autophagy, apoptosis and even tumor microenvironment interaction. Generally, the above effects are achieved through two pathways, energy metabolism and signal transduction. This review aims to highlight the current research progress on the abnormal alterations of amino acids metabolism in pancreatic cancer, how they affect tumorigenesis and development of pancreatic cancer and the application prospects of them as diagnostic, prognostic and therapeutic targets.

Early Detection of Pancreatic Cancer in Type 2 Diabetes Mellitus Patients Based on 1H NMR Metabolomics

The association of pancreatic cancer with type 2 diabetes mellitus was investigated by ¹H NMR metabolomic analysis of blood plasma. Concentration data of 58 metabolites enabled discrimination of pancreatic cancer (PC) patients from healthy controls (HC) and long-term type 2 diabetes mellitus (T2DM) patients. A panel of eight metabolites was proposed and successfully tested for group discrimination. Furthermore, a prediction model for the identification of at-risk individuals for future development of pancreatic cancer was built and tested on recent-onset diabetes mellitus (RODM) patients. Six of 59 RODM samples were assessed as PC with an accuracy of more than 80%. The health condition of these individuals was re-examined, and in four cases, a correlation to the prediction was found. The current health condition can be retrospectively attributed to misdiagnosed pancreatogenic diabetes or to early-stage pancreatic cancer.

Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study

AIM

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at a late, incurable stage. We sought to determine whether individuals at high risk of developing PDAC could be identified early using routinely collected data.

METHODS

Electronic health record (EHR) databases from two independent hospitals in Boston, Massachusetts, providing inpatient, outpatient, and emergency care, from 1979 through 2017, were used with case-control matching. PDAC cases were selected using International Classification of Diseases 9/10 codes and validated with tumour registries. A data-driven feature selection approach was used to develop neural networks and L2-regularised logistic regression (LR) models on training data (594 cases, 100,787 controls) and compared with a published model based on hand-selected diagnoses ('baseline'). Model performance was validated on an external database (408 cases, 160,185 controls). Three prediction lead times (180, 270 and 365 days) were considered.

RESULTS

The LR model had the best performance, with an area under the curve (AUC) of 0.71 (confidence interval [CI]: 0.67-0.76) for the training set, and AUC 0.68 (CI: 0.65-0.71) for the validation set, 365 days before diagnosis. Data-driven feature selection improved results over 'baseline' (AUC = 0.55; CI: 0.52-0.58). The LR model flags 2692 (CI 2592-2791) of 156,485 as high risk, 365 days in advance, identifying 25 (CI: 16-36) cancer patients. Risk stratification showed that the high-risk group presented a cancer rate 3 to 5 times the prevalence in our data set.

CONCLUSION

A simple EHR model, based on diagnoses, can identify high-risk individuals for PDAC up to one year in advance. This inexpensive, systematic approach may serve as the first sieve for selection of individuals for PDAC screening programs.

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.

Application of NMR metabolomics to search for human disease biomarkers in blood

Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.

Elevating pancreatic cystic lesion stratification: Current and future pancreatic cancer biomarker(s)

Pancreatic ductal adenocarcinoma (PDAC) is an incredibly deadly disease with a 5-year survival rate of 9%. The presence of pancreatic cystic lesions (PCLs) confers an increased likelihood of future pancreatic cancer in patients placing them in a high-risk category. Discerning concurrent malignancy and risk of future PCL progression to cancer must be carefully and accurately determined to improve survival outcomes and avoid unnecessary morbidity of pancreatic resection. Unfortunately, current image-based guidelines are inadequate to distinguish benign from malignant lesions. There continues to be a need for accurate molecular and imaging biomarker(s) capable of identifying malignant PCLs and predicting the malignant potential of PCLs to enable risk stratification and effective intervention management. This review provides an update on the current status of biomarkers from pancreatic cystic fluid, pancreatic juice, and seromic molecular analyses and discusses the potential of radiomics for differentiating PCLs harboring cancer from those that do not.

Amino acid analyses of the exosome-eluted fractions from human serum by HPLC with fluorescence detection

Objectives

Amino acid levels in serum or plasma are used for early detection and diagnosis of several diseases. The objective of this study was to analyze amino acid levels in serum exosomes, which have not been previously reported.

Design and methods

We investigated the amino acid composition of exosomes from human serum using HPLC with fluorescence detection.

Results

The composition ratios of His, Arg, Glu, Cys-Cys, Lys, and Tyr were significantly increased in the exosomes compared with those in the corresponding native serum. d-Ser, an endogenous co-agonist of the N-methyl-d-aspartate receptor, was also enriched in the exosome-eluted fraction.

Conclusions

Our results suggest that certain amino acids are enriched in the exosome-eluted fraction from human serum. These differences could have future diagnostic potential.

Identification and causes of metabonomic difference between orthotopic and subcutaneous xenograft of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal tumors. However, the methodological differences between orthotopic and subcutaneous xenograft (OX and SX) models will cause confusion in understanding its pathological mechanism and clinical relevance. In this study, SX and OX models were established by implanting Panc-1 and BxPC-3 cell strains under skin and on the pancreas of mice, respectively. The tumor tissue and serum samples were collected for1H NMR spectroscopy followed by univariate and multivariate statistical analyses. As results, no obvious metabonomic difference was demonstrated in serum between the two models, however, the model- and cell strain-specific metabonomic differences were observed in tumor tissues. According to the KEGG analysis, ABC transporters, glycerophospholipid metabolism, purine metabolism and central carbon metabolism were identified to be the most significant components involved in metabonomic differences. Considering the methodological discrepancy in SX and OX models, such differences should be contributed to tumor microenvironment. In general, SX are not equivalent to OX models at molecular level. Subcutaneous transplantation displayed its inherent limitations though it offered a simple, inexpensive, reproducible and quantifiable advantage. And orthotopic transplantation may be favorable to simulate PDAC in patients due to its similar pathogenesis to human pancreatic cancer.

Holistic and dynamic metabolic alterations of traditional Chinese medicine syndrome in a toxic heat and blood stasis syndrome rat model

Dynamic changes of the metabolic network during the evolution of a syndrome based on the toxic heat and blood stasis syndrome (THBSS) rat model have been elucidated for the first time.