Identifying the Best Marker Combination in CEA, CA125, CY211, NSE, and SCC for Lung Cancer Screening by Combining ROC Curve and Logistic Regression Analyses: Is It Feasible?

Abstract

The detection of serum biomarkers can aid in the diagnosis of lung cancer. In recent years, an increasing number of lung cancer markers have been identified, and these markers have been reported to have varying diagnostic values. A method to compare the diagnostic value of different combinations of biomarkers needs to be established to identify the best combination. In this study, automatic chemiluminescence analyzers were employed to detect the serum concentrations of carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), cytokeratin 19 fragment (CY211), neuron-specific enolase (NSE), and squamous cell carcinoma antigen (SCC) in 780 healthy subjects, 650 patients with pneumonia, and 633 patients with lung cancer. Receiver operating characteristic (ROC) curve and logistic regression analyses were also used to evaluate the diagnostic value of single and multiple markers of lung cancer. The sensitivities of the five markers alone were lower than 65% for lung cancer screening in healthy subjects and pneumonia patients. SCC was of little value in screening lung cancer. After combining two or more markers, the areas under the curves (AUCs) did not increase with the increase in the number of markers. For healthy subjects, the best marker for lung cancer screening was the combination CEA + CA125, and the positive cutoff range was 0.577 CEA + 0.035 CA125 > 2.084. Additionally, for patients with pneumonia, the best screening markers displayed differences in terms of sex but not age. The best screening marker for male patients with pneumonia was the combination CEA + CY211 with a positive cutoff range of 0.008 CEA + 0.068 CY211 > 0.237, while that for female patients with pneumonia was CEA > 2.73 ng/mL, which could be regarded as positive. These results showed that a two-marker combination is more suitable than a multimarker combination for the serological screening of tumors. Combined ROC curve and logistic regression analyses are effective for identifying the best markers for lung cancer screening.