Premium
Salivary metabolite signatures of oral cancer and leukoplakia
Author(s) -
Wei Jie,
Xie Guoxiang,
Zhou Zengtong,
Shi Peng,
Qiu Yunping,
Zheng Xiaojiao,
Chen Tianlu,
Su Mingming,
Zhao Aihua,
Jia Wei
Publication year - 2011
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.25881
Subject(s) - metabolome , metabolomics , saliva , metabolite , leukoplakia , medicine , receiver operating characteristic , cancer , pathology , oncology , bioinformatics , biology
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 γ‐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.