Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma

Abstract

To screen the serum proteome biomarkers of hypopharyngeal squamous cell carcinoma (HSCC) and to establish a predictive model for early detection of HSCC. Serum samples were collected from 48 HSCC patients before surgery and 52 age and sex-matched individuals without cancer used as controls. The samples were divided into 2 sets: training set (including 36 HSCC patients and 36 controls) and blind testing set (including 12 HSCC patients and 16 controls). With WCX2 and IMAC3 protein chips, surface-enhanced laser desorption/ionization (SELDI) was used to analyze the serum protein profiling. 72 samples of the training set were analyzed by a decision tree algorithm to be able to differentiate HSCC patients from controls. Double-blind test was used to determine the sensitivity and specificity of the classification model. Ranging from 2000 - 50000 (M/Z), 11 potential biomarkers on WCX2 and 19 biomarkers on IMAC3 protein chip could differentiate HSCC patients from the control set (P < 10(-5)). Among them 4 candidate protein peaks with the m/z values of 7796, 4216, 5927, and 5361 were selected to be used to establish a predictive model by Biomarker Pattern Software. The model separated effectively the HSCC samples from the control samples, achieving a sensitivity of 94.44%, and a specificity of 88.89%. An accuracy of 85.71% (24/28), sensitivity of 91.67% (11/12), specificity of 81.25% (13/16), positive predictive value of 78.57%% (11/14), and negative predictive value of 92.85% (13/14) were validated in the double-blind testing set. The SELDI-TOF-MS Protein Chip combined with artificial intelligence classification algorithm helps find serum proteome biomarkers and establish predictive model for early diagnosis of HSCC. This technique has potential for the development of a screening test for the detection of HSCC.