Abstract
Lung cancer is the leading cause of cancer-related mortality globally, making early detection crucial for reducing death rates. Low-dose computed tomography (LDCT) screening helps detect lung cancer early but often identifies indeterminate pulmonary nodules (PNs), leading to potential overtreatment. This study aimed to develop a diagnostic test that accurately differentiates malignant from benign PNs detected on LDCT scans by analyzing non-coding RNAs, DNA methylation, and bacterial DNA in patient samples. Using droplet digital polymerase chain reaction, we analyzed samples from a training set of 150 patients with malignant PNs and 250 smokers with benign PNs. Individual biomarkers in plasma and sputum showed moderate effectiveness, with sensitivities ranging from 62% to 77% and specificities from 54% to 87%. We developed an integromic signature by combining two plasma biomarkers and one sputum biomarker, along with additional clinical data, which demonstrated a sensitivity of 90% and specificity of 95%. The signature's diagnostic performance was further validated in a cohort consisting of 30 patients with malignant PNs and 50 smokers with benign PNs. The integromic signature showed high sensitivity and specificity in distinguishing malignant from benign PNs identified through LDCT. This tool has the potential to significantly lower both mortality and health-care costs associated with the overtreatment of benign nodules, offering a promising approach to improving lung cancer screening protocols.