Currently, no satisfactory biomarkers are available to screen for small-cell lung cancer (SCLC). We applied a surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) ProteinChip system to detect 150 serum samples (including 54 SCLC patients, 24 non-small cell lung cancer [NSCLC] patients, 32 pneumonia patients, and 40 healthy individuals). The spectra data were analyzed by support vector machine (SVM) and potential biomarkers were chosen for the system training and used to construct diagnostic model. Pattern 1, constructed of four protein peaks with mass/charge (m/z) of 4,293 Da, 4,612 Da, 6,455 Da, and 7,582 Da, separated SCLC patients from the healthy individuals with a sensitivity of 88.9% and a specificity of 85.7%. This pattern performed significantly better than the current marker, neuron-specific enolase (NSE) (P<0.05). Pattern 2, constructed of protein peaks with mass/charge (m/z) of 2,764 Da and 1,7368 Da, separated SCLC from pneumonia with a sensitivity of 88.9% and a specificity of 91.7%. Pattern 3, constructed of another three protein peaks with m/z of 3,912 Da, 7,562 Da, and 13,777 Da, separated SCLC from NSCLC. The sensitivity and specificity were 83.3% and 75.0%, respectively. These results suggested that SELDI-TOF MS combined with support vector machine yields significantly higher sensitivity and specificity for the detection of serum protein of SCLC.
Detection and significance of serum protein markers of small-cell lung cancer.