Objectives. The aim of the present study is to investigate the presence of proteomic signatures of Oral Squamous Cell Carcinoma (OSCC) in saliva and their use as potential biomarkers for early and non-invasive diagnosis, as well as prognostication. Methods. Saliva from 45 OSCC patients and 30 healthy controls was analysed by SELDI-TOF mass spectrometry and ProteinChip® technology. Proteomic profiles were tested with differential expression analysis and fold change of protein peaks, principal component analysis, Spearman rank correlation test and hierarchical clustering in order to identify a list of peaks of interest representative of controls, N- and N+ cases. Those peaks were used in a supervised artificial neural network in order to classify samples according to the following conditions: controls vs OSCC, controls vs N-, and controls vs N+. Results. When compared with controls, four peaks (i.e. 6913, 11948, 13287 and 27280 m/z) were significantly altered in both N- group and N+ group; four peaks (i.e. 3353, 3433, 3482 and 4136 m/z) were selectively altered in Ngroup; eight peaks were selectively altered in N+ group (i.e. 4038, 7133, 11755, 13746, 13841, 14264, 16807, 17127 m/z). Those peaks were capable to classify 100% of cases and controls, thus being potential diagnostic and prognostic biomarkers for OSCC. Conclusions. Proteomic profiling of saliva has the potential to provide an effective tool for early diagnosis and prognostication of OSCC.

SALIVARY PROTEOMIC BIOMARKERS OF ORAL SQUAMOUS CELL CARCINOMA

GIANNATEMPO, GIOVANNI;LO RUSSO, LUCIO;GALLO, CRESCENZIO;LO MUZIO, LORENZO
2014-01-01

Abstract

Objectives. The aim of the present study is to investigate the presence of proteomic signatures of Oral Squamous Cell Carcinoma (OSCC) in saliva and their use as potential biomarkers for early and non-invasive diagnosis, as well as prognostication. Methods. Saliva from 45 OSCC patients and 30 healthy controls was analysed by SELDI-TOF mass spectrometry and ProteinChip® technology. Proteomic profiles were tested with differential expression analysis and fold change of protein peaks, principal component analysis, Spearman rank correlation test and hierarchical clustering in order to identify a list of peaks of interest representative of controls, N- and N+ cases. Those peaks were used in a supervised artificial neural network in order to classify samples according to the following conditions: controls vs OSCC, controls vs N-, and controls vs N+. Results. When compared with controls, four peaks (i.e. 6913, 11948, 13287 and 27280 m/z) were significantly altered in both N- group and N+ group; four peaks (i.e. 3353, 3433, 3482 and 4136 m/z) were selectively altered in Ngroup; eight peaks were selectively altered in N+ group (i.e. 4038, 7133, 11755, 13746, 13841, 14264, 16807, 17127 m/z). Those peaks were capable to classify 100% of cases and controls, thus being potential diagnostic and prognostic biomarkers for OSCC. Conclusions. Proteomic profiling of saliva has the potential to provide an effective tool for early diagnosis and prognostication of OSCC.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11369/321124
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