Identification of serum biomarkers for nasopharyngeal carcinoma by proteomic analysis

Early diagnosis of nasopharyngeal carcinoma (NPC) remains a challenge. Serum protein profiling is a promising approach for the classification of cancer versus noncancer samples. The objective of the current study was to assess the feasibility of mass spectrometry-based protein profiling and a classification tree algorithm for discriminating between patients with NPC and noncancer controls.

Serum samples from patients with NPC and noncancer controls were analyzed by using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The study was divided into a preliminary training set and a blind test set: A preliminary training set and a classification tree of spectra derived from 55 patients with NPC and a group of 60 noncancer controls were used to develop a proteomic model that discriminated cancer from noncancer effectively. Then, the validity of the classification tree was challenged with a blind test set, which included another 25 patients with NPC and 28 noncancer controls.

Four protein peaks at 4097 daltons (Da), 4180 Da, 5912 Da, and 8295 Da were chosen automatically as a biomarker pattern in the training set that discriminated cancer from noncancer with sensitivity of 94.5% and specificity of 96.7%. When the SELDI marker pattern was tested with the blinded test set, it yielded a sensitivity of 92%, a specificity of 92.9%, and an accuracy rate of 92.5%. The accuracy of 2 protein peaks (4581 Da and 7802 Da) was 80% for predicting stage I and II NPC and 86% for predicting stage III and IV NPC.

The high sensitivity and specificity obtained by the serum protein profiling approach demonstrated that SELDI-TOF-MS combined with a tree analysis model both can facilitate discriminating between NPC and noncancer controls and can provide an innovative clinical diagnostic platform to improve the detection of NPC.