Application of serum SELDI proteomic patterns in diagnosis of lung cancer

Abstract

Background: Currently, no satisfactory biomarkers are available to screen for lung cancer. Surface-Enhanced Laser Desorption/ionization Time-of-Flight Mass Spectrometry ProteinChip system (SELDI-TOF-MS) is one of the currently used techniques to identify biomarkers for cancers. The aim of this study is to explore the application of serum SELDI proteomic patterns to distinguish lung cancer patients from healthy individuals.

Methods: A total of 208 serum samples, including 158 lung cancer patients and 50 healthy individuals, were randomly divided into a training set (including 11 sera from patients with stages I/II lung cancer, 63 from patients with stages III/IV lung cancer and 20 from healthy controls) and a blinded test set (including 43 sera from patients with stages I/II lung cancer, 41 from patients with stages III/IV lung cancer and 30 from healthy controls). All samples were analyzed by SELDI technology. The spectra were generated on weak cation exchange (WCX2) chips, and protein peaks clustering and classification analyses were made using Ciphergen Biomarker Wizard and Biomarker Pattern software, respectively. We additionally determined Cyfra21-1 and NSE in the 208 serum samples included in this study using an electrochemiluminescent immunoassay.

Results: Five protein peaks at 11493, 6429, 8245, 5335 and 2538 Da were automatically chosen as a biomarker pattern in the training set. When the SELDI marker pattern was tested with the blinded test set, it yielded a sensitivity of 86.9%, a specificity of 80.0% and a positive predictive value of 92.4%. The sensitivities provided by Cyfra21-1 and NSE used individually or in combination were significantly lower than that of the SELDI marker pattern (P < 0.005 or 0.05, respectively). Based on the results of the test set, we found that the SELDI marker pattern showed a sensitivity of 91.4% in the detection of non-small cell lung cancers (NSCLC), which was significantly higher than that in the detection of small cell lung cancers (P < 0.05); The pattern also had a sensitivity of 79.1% in the detection of lung cancers in stages I/II.

Conclusion: These results suggest that serum SELDI protein profiling can distinguish lung cancer patients, especially NSCLC patients, from normal subjects with relatively high sensitivity and specificity, and the SELDI-TOF-MS is a potential tool for the screening of lung cancer.

Figure 1

Classification of lung cancer vs. normal samples by the decision tree algorithm. The left branch node after the first layer is the cases of peak intensity under 2.018, the right one is over or equal to 2.018. The cutoff points for 8245, 6429 and 2538 Da were 1.574, 49.64 and 13.01, respectively. The cutoff points of mass 5335 Da were 0.288 (left) and 2.163 (right). N represents the number of samples. M represents the molecular weight.

Figure 2

Differential expressions of the SELDI peaks at 11493 (group A) and 5335Da (group B) in the comparisons of lung cancer and healthy control sera. a-c: squamous cell carcinomas; d-f: adenocarcinomas; g-i: small cell carcinomas; j-l, healthy controls. X-axis was molecular weight of peak; Y-axis was intensity of peak.