Potential Salivary Proteomic Markers of Oral Squamous Cell Carcinoma

The aim of the present study was to investigate the diagnostic and prognostic potential of proteomic signatures in saliva of patients with oral squamous cell carcinoma (OSCC).

Data from SELDI-TOF mass spectrometry of saliva from 45 OSCC patients and 30 healthy controls were analyzed by means of univariate and multivariate statistical approaches, in order to identify proteomic OSCC signatures, reduce dimensionality and build models for discriminating between OSCC and controls, as well as predict nodal status.

The saliva proteome presents significant modifications in OSCC patients; some of them seem to be related to nodal involvement, and may be useful for knowledge advancement regarding oral carcinogenesis and definition of diagnostic and prognostic biomarkers. Our attempt to create a predictive model using different artificial neural networks (i.e. feed-forward (FF), radial basis function (RBF), vector quantization (VQ)) demonstrated that such biostatistical tools are powerful but not all network architectures have similar performance. RBF architecture showed the best diagnostic performance (91.89%), whereas FF had the best (77.27%) prognostic accuracy (distinguishing between N(-) and N(+)).

Searching for potential biomarkers among differently expressed peptides is a challenge requiring for appropriate strategies that still remain to be defined. A number of factors may potentially impair results, e.g.: (i) a group's definition for adequate comparison; (ii) reduction of data dimensionality and selection of variables to be tested in predictive models; (iii) selection of the biostatistical tool for predictive models.