Tu1493 Diagnostic Potential of Pancreatic Cancer by Using Metabolomic Analysis

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease and affects 1 in 1,000 individuals. Ultrasound is most often used to diagnose ADPKD; such a modality is only useful late in the disease after macroscopic cysts are present. There is accumulating evidence suggesting that there are common cellular and molecular mechanisms responsible for cystogenesis in human and murine PKD regardless of the genes mutated, and, in the case of complex metabolomic analysis, the use of a mouse model has distinct advantages for proof of principle over a human study. Therefore, in this study we utilized a urinary metabolomics-based investigation using gas chromatography-time of flight mass spectrometry to demonstrate that the cystic mouse can be discriminated from its wild-type counterpart by urine analysis alone. At day 26 of life, before there is serological evidence of kidney dysfunction, affected mice are distinguishable by urine metabolomic analysis; this finding persists through 45 days until 64 days, at which time body weight differences confound the results. Using functional score analysis and the KEGG pathway database, we identify several biologically relevant metabolic pathways which are altered very early in this disease, the most highly represented being the purine and galactose metabolism pathways. In addition, we identify several specific candidate biomarkers, including allantoic acid and adenosine, which are augmented in the urine of young cystic mice. These markers and pathway components, once extended to human disease, may prove useful as a noninvasive means of diagnosing cystic kidney diseases and to suggest novel therapeutic approaches. Thus, urine metabolomics has great diagnostic potential for cystic renal disorders and deserves further study.

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Diagnostic potential of mucins in pancreatic juice for pancreatic cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2016.34.4_suppl.222 ◽

2016 ◽

Vol 34 (4_suppl) ◽

pp. 222-222

Author(s):

Asish Patel ◽

Sukhwinder Kaur ◽

Lynette Smith ◽

Chandrakanth Are ◽

Surinder Batra

Keyword(s):

Pancreatic Cancer ◽

Pancreatic Juice ◽

Sandwich Elisa ◽

Pairwise Comparison ◽

Individual Performance ◽

Diagnostic Potential ◽

Wilcoxon Rank Sum Test ◽

Diagnostic Panel ◽

Curve Modeling ◽

Potential Methods

222 Background: Pancreatic juice remains an underutilized resource for diagnosing pancreatic cancer. Mucins are high molecular weight glycoproteins differentially upregulated in pancreatic cancer, and we hypothesize that their profile in pancreatic juice may have diagnostic potential. Methods: Pancreatic juice was obtained during endoscopy from non-healthy non-pancreatic control (NHPC, n = 57), chronic pancreatitis (CP, n = 23), and pancreatic cancer (PC, n = 23) patients. Sandwich ELISA was used to detect MUC1, MUC4, MUC5AC, CA125, and CA19-9. Kruskal-Wallis test and Wilcoxon rank sum test for group and pairwise comparison was done with p < 0.05 as significant. Logistic regression with ROC curve modeling of log transformed data was done for each biomarker individually and in combination to determine odds ratio (OR), sensitivity (SN), and specificity (SP) for PC. Results: PC vs NHPC: MUC5AC had the best individual performance for diagnosing PC with an OR = 2.78 (95% CI = 1.51-5.13), AUC = 0.81, and optimal SN/SP of 0.83 and 0.67, respectively. CA125 was increased in PC with an OR = 2.31 (95% CI = 1.4-4.0), AUC = 0.73, and optimal SN/SP of 0.88 and 0.67. CA19-9 was increased in PC with an OR = 1.5 (95% CI = 1.2-1.8), AUC = 0.76, and optimal SN/SP of 0.73 and 0.70. A combination of MUC1, MUC5AC, CA125, and CA19-9 outperformed all individual markers and had the largest AUC (0.89) with optimal SN/SP of 0.84 and 0.79. PC vs CP: MUC1 concentration in PC was significantly less than CP with an OR = 0.21 (95%CI = 0.088-0.49), AUC = 0.82, and optimal SN/SP of 0.87 and 0.78. PC vs NHPC+CP: MUC1 was decreased significantly in PC with an OR = 0.65 (95% CI = 0.44-0.96), AUC = 0.69, and optimal SN/SP of 0.87 and 0.63. CA125 was increased in PC with an OR = 1.64 (95%CI = 1.1-2.4), AUC = 0.66, and optimal SN/SP of 0.67 and 0.64. CA19-9 was increased in PC with an OR = 1.32 (95%CI = 1.1-1.6), AUC = 0.68, and optimal SN/SP of 0.63 and 0.67. A combination of MUC1, MUC5AC, CA125, and CA19-9 had an AUC = 0.86 with optimal SN/SP of 0.87 and 0.77 for PC. Conclusions: MUC1, MUC5AC, CA125, and CA19-9 combination provides a significantly improved diagnostic panel compared to any individual marker in pancreatic juice for detecting malignancy.

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Metabolomic Analysis of Small Extracellular Vesicles Derived from Pancreatic Cancer Cells Cultured under Normoxia and Hypoxia

Metabolites ◽

10.3390/metabo11040215 ◽

2021 ◽

Vol 11 (4) ◽

pp. 215

Author(s):

Ryosuke Hayasaka ◽

Sho Tabata ◽

Masako Hasebe ◽

Satsuki Ikeda ◽

Sumiko Ohnuma ◽

...

Keyword(s):

Mass Spectrometry ◽

Pancreatic Cancer ◽

Cancer Cells ◽

Extracellular Vesicles ◽

Tumor Hypoxia ◽

Metabolomic Analysis ◽

Liquid Chromatography Mass Spectrometry ◽

Pancreatic Cancer Cells ◽

Chromatography Tandem Mass Spectrometry ◽

Chromatography Mass Spectrometry

Extracellular vesicles (EVs) released from cancer cells contribute to various malignant phenotypes of cancer, including metastasis, cachexia, and angiogenesis. Although DNA, mRNAs, miRNAs, and proteins contained in EVs have been extensively studied, the function of metabolites in EVs remains unclear. In this study, we performed a comprehensive metabolomic analysis of pancreatic cancer cells, PANC-1, cultured under different oxygen concentrations, and small EVs (sEVs) released from them, considering the fact that hypoxia contributes to the malignant behavior of cells in pancreatic cancer, which is a poorly diagnosed cancer. sEVs were collected by ultracentrifugation, and hydrophilic metabolites were analyzed using capillary ion chromatography-mass spectrometry and liquid chromatography-mass spectrometry, and lipids were analyzed by supercritical fluid chromatography-tandem mass spectrometry. A total of 140 hydrophilic metabolites and 494 lipids were detected in sEVs, and their profiles were different from those in cells. In addition, the metabolomic profile of sEVs was observed to change under hypoxic stress, and an increase in metabolites involved in angiogenesis was also detected. We reveal the hallmark of the metabolites contained in sEVs and the effect of tumor hypoxia on their profiles, which may help in understanding EV-mediated cancer malignancy.

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