656 peaks. Figure 5 Relative peak intensities of m/z 3159.835, 5187.656, 13738.6 protein masses in serum check details samples from patients with nasopharyngeal carcinoma (NPC) compared

with samples from the noncancer controls. Results are shown as box-and-whisker plots. Table 2 Statistical Analysis of 3 Biomarkers for Screening Patients With Nasopharyngeal Carcinoma Versus Healthy Controls   Intensity, mean ± SD   Protein peaks, m/z Noncancer normal NPC P 3159.835 2.13 ± 1.44 1.22 ± 1.04 0.017728 5187.656* 2.00 ± 1.31 1.38 ± 0.60 0.094881 13738.6 0.86 ± 0.54 1.31 ± 0.60 0.002791 SD indicates standard deviation; m/z, mass-to-change ratio; NPC, nasopharyngeal carcinoma. *The peak is necessary for Decision Tree although the P value > 0.05. The error rate of the generated Decision Tree was estimated through a process of cross-validation. click here Performance

of the generated Decision Tree is summarized in Table 3 for the training and test sets. A blind test set, which consisted of samples GW786034 in vitro from 20 patients with cancer and 12 noncancer controls, was used to evaluate the ability of Diagnostic Pattern to distinguish between patients with NPC and noncancer controls. In our study, 10 of 12 true noncancer control samples were classified correctly, and 19 of 20 cancer samples were classified correctly as malignant. This set result yielded a sensitivity of 95%, a specificity of 83.33%, and an accuracy rate of 90.63% (Table 3). Table 3 Performance of the Decision Tree Analysis of NPC in Training Test and Blind test Sets   Sensitivity,% Specificity, % Accuracy rate, % Training set 91.66(22/24) 95.83(23/24) 93.75(45/48) Test set 87.5(21/24) 95.83(23/24) 91.67(44/48) Blind test set 95.0(19/20) 83.33(10/12) 90.63(29/32) Discussion Currently, there are no satisfactory serum diagnostic markers for NPC, especially in the early stage [12]. Complex serum proteomic patterns might reflect the potential pathological state of a disease such as NPC and enable the scientific community to develop more reliable diagnostic tools. In this study, we used SELDI-TOF

MS technology to disclose the serum protein ‘fingerprints’ of NPC and thereby establish a new diagnostic model for NPC. SELDI-TOF MS allows the identification of large Tenofovir cell line numbers of potential biomarkers in a biological sample, based on molecular weights and chemical characteristics. In essence it provides high throughput screening for biomarkers, particularly when present in low abundance, avoiding the limitations of antibody binding and of only analyzing predetermined proteins. It is able, therefore, to identify proteins not previously appreciated to be potentially valuable biomarkers. The technology has been applied to serum and urine to identify disease specific biomarkers [13]. However, the number of peaks that can be identified by this approach does not cover the whole serum proteome. This is related to several potential technical limitations.