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Data from Salivary Proteomics for Oral Cancer Biomarker Discovery

Posted on 2023-03-31 - 15:24
Abstract

Purpose: This study aims to explore the presence of informative protein biomarkers in the human saliva proteome and to evaluate their potential for detection of oral squamous cell carcinoma (OSCC).

Experimental Design: Whole saliva samples were collected from patients (n = 64) with OSCC and matched healthy subjects (n = 64). The proteins in pooled whole saliva samples of patients with OSCC (n = 16) and matched healthy subjects (n = 16) were profiled using shotgun proteomics based on C4 reversed-phase liquid chromatography for prefractionation, capillary reversed-phase liquid chromatography with quadruple time-of-flight mass spectrometry, and Mascot sequence database searching. Immunoassays were used for validation of the candidate biomarkers on a new group of OSCC (n = 48) and matched healthy subjects (n = 48). Receiver operating characteristic analysis was exploited to evaluate the diagnostic value of discovered candidate biomarkers for OSCC.

Results: Subtractive proteomics revealed several salivary proteins at differential levels between the OSCC patients and matched control subjects. Five candidate biomarkers were successfully validated using immunoassays on an independent set of OSCC patients and matched healthy subjects. The combination of these candidate biomarkers yielded a receiver operating characteristic value of 93%, sensitivity of 90%, and specificity of 83% in detecting OSCC.

Conclusion: Patient-based saliva proteomics is a promising approach to searching for OSCC biomarkers. The discovery of these new targets may lead to a simple clinical tool for the noninvasive diagnosis of oral cancer. Long-term longitudinal studies with large populations of individuals with oral cancer and those who are at high risk of developing oral cancer are needed to validate these potential biomarkers.

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AUTHORS (10)

Shen Hu
Martha Arellano
Pinmanee Boontheung
Jianghua Wang
Hui Zhou
Jiang Jiang
David Elashoff
Roger Wei
Joseph A. Loo
David T. Wong
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