Serum metabolomics as a novel diagnostic approach for pancreatic cancer

Periodontitis is one of the most prevalent threats to oral health as the most common cause of tooth loss. In order to perform e ective treatment, a clinical test that detect sites where disease activity is high and predicts periodontal tissue destruction is strongly desired, however, it is still di cult to prognose the periodontal tissue breakdown on the basis of conventional methods. e aim of this study is to examine the usefulness of gas chromatography/mass spectrometry (GC/MS), which could eventually be used for on-site analysis of metabolites in gingival crevicular uid (GCF) in order to objectively diagnose periodontitis at a molecular level. GCF samples were collected from two diseased sites (one site with a moderate pocket and another site with a deep pocket) from each patient and from clinically healthy sites of volunteers. Nineteen metabolites were identi ed using GC/MS. Total ion current chromatograms showed broad differences in metabolite peak patterns between GCF samples obtained from healthy sites, moderate-pocket sites, and deep-pocket sites. e intensity di erence of some metabolites was signi cant at sites with deep pockets compared to healthy sites. Additionally, metabolite intensities at moderate-pocket sites showed an intermediate pro le between the severely diseased sites and healthy sites, which suggested that periodontitis progression could be observed with a changing metabolite pro le. Principal component analysis con rmed these observations by clearly delineating healthy sites and sites with deep pockets. ese results suggest that metabolomic analysis of GCF could be useful for prediction and diagnosis of periodontal disease in a single visit from a patient and provides the groundwork for establishing a new, on-site diagnostic method for periodontitis.

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“…Sample preparation was similar to that previously reported by Nishiumi et al 11) with slight modications.…”

Section: Sample Collection and Sample Preparationmentioning

confidence: 66%

Metabolomic Analysis of Gingival Crevicular Fluid Using Gas Chromatography/Mass Spectrometry

et al. 2016

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“…We have showed the use of metabolomics for diagnosing and evaluating the pathologic conditions of various diseases (19)(20)(21)(22)(23)(24). Metabolomics is the comprehensive study of low molecular weight metabolites, which are the endpoint of the omics cascade and hence are the closest point in the cascade to the phenotype.…”

Section: Introductionmentioning

confidence: 99%

A Novel Serum Metabolomics-Based Diagnostic Approach to Pancreatic Cancer

Kobayashi

Nishiumi

Ikeda

et al. 2013

Cancer Epidemiology, Biomarkers &Amp; Prevention

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158

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Background: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer.Methods: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study.Results: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers.Conclusions: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis.Impact: It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis. Cancer Epidemiol Biomarkers Prev; 22(4); 571-9. Ó2013 AACR.

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“…Human serum metabolomics using gas chromatography-mass spectrometry has been reported to be useful for discriminating patients with PC from healthy volunteers [21]. Kobayashi et al [22] developed a serum metabolomics-based diagnostic model for PC using multiple logistic regression analysis.…”

Section: Biomarkersmentioning

confidence: 99%