Detection of hypopharyngeal squamous cell carcinoma using serum proteomics

Abstract

Conclusions. The combination of surface-enhanced laser desorption/ionization (SELDI) with bioinformatics tools could help find serum proteome biomarkers and establish a predictive model for early detection of hypopharyngeal squamous cell carcinoma (HSCC). Objectives. Proteomic profiling of serum using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an emerging technique to identify new biomarkers in biological fluids and to establish clinically useful diagnostic computational models. We used it to find new potential biomarkers and to establish a predictive model for early detection of HSCC. Materials and methods. One hundred serum samples including 48 from HSCC patients and 52 from normal controls which were divided into a training set and a blind testing set were treated on WCX2 and IMAC3 protein chip, and serum protein or peptide patterns were detected by SELDI-TOF-MS. The data of spectra were analyzed by Biomarker Wizard software to screen serum proteome biomarkers of HSCC. A decision tree classification algorithm and blind validation were determined by Biomarker Pattern Software (BPS). Results. Ranging from 2 to 30 kDa, 45 potential biomarkers could differentiate HSCC patients from normal controls (p < 0.05). Among them four candidate protein peaks with m/z values of 7796, 4216, 5927, and 5361Da were selected to establish a predictive model by BPS with sensitivity of 94% and specificity of 89%. A sensitivity of 92% and specificity of 82% were validated in the blind testing set.

• Hypopharyngeal squamous cell carcinoma
• proteome biomarkers
• surface-enhanced laser desorption/ionization mass spectrometry
• decision tree