title: Potential Salivary Proteomic Markers of Oral Squamous Cell Carcinoma.
abstract: OBJECTIVE
  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).
  
  
  METHODS
  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.
  
  
  RESULTS
  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(+)).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Clinical and Experimental Sciences, University of Foggia, Foggia, Italy.
authors.forename: Crescenzio
authors.initials: C
authors.lastname: Gallo
authors.affiliation: Department of Clinical and Experimental Sciences, University of Foggia, Foggia, Italy.
authors.forename: Domenico
authors.initials: D
authors.lastname: Ciavarella
authors.affiliation: Department of Clinic Specialistic and Odontostomatological Sciences, Polytechnic University of Marche, Ancona, Italy.
authors.forename: Andrea
authors.initials: A
authors.lastname: Santarelli
authors.affiliation: Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy.
authors.forename: Elena
authors.initials: E
authors.lastname: Ranieri
authors.affiliation: Department of Head and Neck Pathology, Second University of Naples, Naples, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Colella
authors.affiliation: Department of Clinical and Experimental Sciences, University of Foggia, Foggia, Italy.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Lo Muzio
authors.affiliation: Department of Clinical and Experimental Sciences, University of Foggia, Foggia, Italy lucio.lorusso@unifg.it.
authors.forename: Lucio
authors.initials: L
authors.lastname: Lo Russo
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000737:chemistry / Q000378:metabolism; D002294:Carcinoma, Squamous Cell / Q000737:chemistry* / Q000175:diagnosis / Q000378:metabolism; D016022:Case-Control Studies; D005260:Female; D006258:Head and Neck Neoplasms / Q000737:chemistry* / Q000175:diagnosis / Q000378:metabolism; D006801:Humans; D008297:Male; D008875:Middle Aged; D009062:Mouth Neoplasms / Q000737:chemistry* / Q000175:diagnosis / Q000378:metabolism; D011379:Prognosis; D012463:Saliva / Q000737:chemistry* / Q000378:metabolism; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D000077195:Squamous Cell Carcinoma of Head and Neck
chemical_list: D014408:Biomarkers, Tumor
keywords: SELDI-TOF; Saliva; biomarkers; oral cancer; proteomic