[Serum diagnosis of head and neck squamous cell carcinoma using surface-enhanced desorption ionization mass spectrometry and artificial neural network analyses]

Abstract

Background & objective: The early diagnosis of head and neck squamous cell carcinoma (HNSCC) is the key factor that affecting the treatment result. We performed surface-enhanced desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) using a multi-layer artificial neural network (ANN) to develop and evaluate a proteomic diagnosis approach for HNSCC.

Methods: Serum samples from 74 HNSCC patients and 146 healthy individuals were randomized into training set (148 samples) and test set (72 samples). At first, we detected the training set of samples using SELDI mass spectrometry and WCX2 (weak cation-exchange) chips. Using a multi-layer ANN with a back propagation algorithm, we identified a proteomic pattern that could discriminate cancer from control samples in the training set. The discovered pattern was then used to determine the accuracy of the classification system in the test set.

Results: Four top-scored peaks, at m/z (mass/charge) ratio of 4 469 u, 5 924 u, 8 926 u, and 16 697 u, were finally selected as the potential biomarkers for detection of HNSCC with both sensitivity and specificity of 100.0% in the training set. The classifier predicted the HNSCC with sensitivity of 85.7%(18/21) and specificity of 96.1%(49/51) in the test set.

Conclusion: SELDI profiling is a useful tool to accurately identify patients with HNSCC.