Salivary metabolite signatures of oral cancer and leukoplakia

Abstract

Oral cancer, one of the six most common human cancers with an overall 5-year survival rate of <50%, is often not diagnosed until it has reached an advanced stage. The aim of the current study is to explore salivary metabolomics as a disease diagnostic and stratification tool for oral cancer and leukoplakia and evaluate the potential of salivary metabolome for detection of oral squamous cell carcinoma (OSCC). Saliva metabolite profiling for a group of 37 OSCC patients, 32 oral leukoplakia (OLK) patients and 34 healthy subjects was performed using ultraperformance liquid chromatography coupled with quadrupole/time-of-flight mass spectrometry in conjunction with multivariate statistical analysis. The OSCC, OLK and healthy control groups demonstrate characteristic salivary metabolic signatures. A panel of five salivary metabolites including c-aminobutyric acid, phenylalanine, valine, n-eicosanoic acid and lactic acid were selected using OPLS-DA model with S-plot. The predictive power of each of the five salivary metabolites was evaluated by receiver operating characteristic curves for OSCC. Valine, lactic acid and phenylalanine in combination yielded satisfactory accuracy (0.89, 0.97), sensitivity (86.5% and 94.6%), specificity (82.4% and 84.4%) and positive predictive value (81.6% and 87.5%) in distinguishing OSCC from the controls or OLK, respectively. The utility of salivary metabolome diagnostics for oral cancer is successfully demonstrated in this study and these results suggest that metabolomics approach complements the clinical detection of OSCC and stratifies the two types of lesions, leading to an improved disease diagnosis and prognosis. About 1.5 million new cancer cases are expected to be diagnosed in 2009 in the United States and >0.5 million Americans are expected to die of cancer this year, averaging about 1,500 deaths per day.1 These numbers have been steadily increasing over the past 15 years, despite significant progress in cancer treatment. Oral cancer represents one of the six most common human cancers with a high morbidity rate and an overall 5-year survival rate of <50%.2,3 Reports indicate an increasing worldwide incidence of oral cancer at an earlier age.4-6 Over 90% of oral cancer is oral squamous cell carcinoma (OSCC) which arises from the oral mucosal lining.7 A critical factor in the lack of prognostic improvement is the fact that a significant proportion of cancers initially are asymptomatic lesions and are not diagnosed or treated until Key words: metabolomics, saliva, oral squamous cell carcinoma, ultraperformance liquid chromatography quadrupole-time of flight mass spectrometry, multivariate statistical analysis, receiver operating characteristics Abbreviations:: OSCC: oral squamous cell carcinoma; OLK: oral leukoplakia; UPLC-QTOFMS: ultraperformance liquid chromatographyquadrupole/time-of-flight mass spectrometry; PCA: principal component analysis; OPLS-DA: orthogonal partial least squares-discriminant analysis; ROC: receiver operating characteristic; LR: logistic regression; VIP: va...

Urinary Metabolite Markers of Precocious Puberty

“…Receiver Operating Characteristic Curve Analysis and Prediction Models-The receiver operating characteristic curve analysis was done as described previously (34). Using the results obtained from the OPLS-DA analysis of the UPLC-QTOFMS and GC-TOFMS data, we conducted receiver operating characteristic (ROC) curve analysis with IBM SPSS Statistics 19 (SPSS Inc.) to evaluate the predictive power of each of the discriminant metabolites (31,34).…”

“…Using the results obtained from the OPLS-DA analysis of the UPLC-QTOFMS and GC-TOFMS data, we conducted receiver operating characteristic (ROC) curve analysis with IBM SPSS Statistics 19 (SPSS Inc.) to evaluate the predictive power of each of the discriminant metabolites (31,34). The cutpoint was determined for each biomarker by searching for those that yielded both high sensitivity and specificity.…”

Untargeted metabolic profiling of saliva by liquid chromatography-mass spectrometry for the identification of potential diagnostic biomarkers of asthma

“…The classification of asthma was based on established diagnostic tests for asthma including forced expiratory volume (FEV1%), sputum eosinophil count and methacholine challenge test 21 Participants all had well controlled asthma diagnosed by a consultant respiratory physician with a normal asthma control questionnaire 22 score over the last 3 months, and had not required oral steroids within a year. Tests of airway function, including methacholine PC 20 and induced sputum had all been performed between 1 and 6 months prior to saliva collection (Supplementary Fig. S1 and Table S1.…”

Salivary Metabolomics and Oral Carcinogenesis

“…Among these the most discriminant metabolites were γ-aminobutyric acid, phenylalanine, valine, n-eicosanoic acid and lactic acid [28] . Capillary electrophoresis mass spectrometry based saliva metabolomics was performed by Sugimoto et al (2010) to identify the metabolomic profiles of oral, breast and pancreatic cancers.…”