Enhancement of diagnostic performance in lung cancers by combining CEA and CA125 with autoantibodies detection

Objectives: Although low-dose computed tomography has been confirmed to have meaningful diagnostic utility, lung cancer is still the leading cause of cancer-related deaths for both genders worldwide. Thus, a novel panel with a stronger diagnostic performance for lung cancer is needed. This study aimed to investigate the efficacy of a new panel in lung cancer diagnosis. Materials and Methods: The serum levels of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125) and seven autoantibodies were measured and statistically analyzed in samples from healthy controls and patients with lung cancer. The 316 candidates enrolled in this study were randomly assigned into two groups for the training and validation of a diagnostic panel. Results: An optimal panel with four biomarkers (CEA, CA125, Annexin A1-Ab, and Alpha enolase-Ab) was established, with an area under the receiver operator characteristic (ROC) curve (AUC) of 0.897, a sensitivity of 86.5%, a specificity of 82.3%, a positive predictive value (PPV) of 88.3%, a negative predictive value (NPV) of 79.7%, and a diagnostic accuracy of 84.8% for the training group. The panel was validated, with an AUC of 0.856 and a sensitivity of 87.5% for the validation group. Furthermore, the new panel performed significantly better in lung cancer screening than did CEA and CA125 in all of the cohorts (p< .05). Conclusion: The diagnostic performance of CEA and CA125 was significantly enhanced through their combination with two autoantibodies (Annexin A1-Ab, and Alpha enolase-Ab). Optimization of the measured autoantibodies is critical for generating a panel to detect lung cancer in patients.