Urinary exosomal RAB11A serves as a novel non-invasive biomarker for diagnosis, treatment response monitoring, and prognosis in small cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is an aggressive malignancy with a poor prognosis. This study aimed to analyze the urinary exosomal proteome of SCLC patients to identify and validate potential non-invasive biomarkers for improving diagnosis, treatment response monitoring, and prognosis prediction. METHODS: We analyzed 90 urine samples from SCLC patients, divided into training (n = 38) and validation (n = 52) sets, including untreated, partial/complete remission, and relapsed groups. Ten healthy controls were included. Urinary exosomes were isolated by ultracentrifugation. The proteomic analysis employed data-independent acquisition mass spectrometry (DIA-MS) and parallel reaction monitoring (PRM). Immunohistochemistry was performed on 30 pairs of SCLC and adjacent normal tissues. RESULTS: Proteomic analysis revealed distinct exosomal protein expression patterns across SCLC stages. RAB11A emerged as a key differentially expressed protein. PRM validation confirmed significant changes in RAB11A levels across disease stages. ROC curve analysis demonstrated excellent diagnostic performance of RAB11A in distinguishing SCLC patients from healthy controls (AUC = 0.91, 95% CI 0.79-1.00, P = 0.0004), with a sensitivity of 85% and specificity of 92%. RAB11A also showed significant potential in monitoring treatment response (AUC = 0.86, 95% CI 0.69-1.00, P = 0.0019) and disease relapse (AUC = 0.90, 95% CI 0.76-1.00, P = 0.0005). Immunohistochemistry showed significantly higher RAB11A expression in SCLC tissues compared to adjacent normal tissues (70% vs. 33% positive expression, P = 0.043). CONCLUSION: Urinary exosomal RAB11A shows promise as a non-invasive biomarker for SCLC diagnosis, treatment response monitoring, and early detection of relapse, potentially improving clinical management of SCLC patients. The findings provide insights into SCLC pathogenesis and offer a non-invasive approach for patient monitoring, which could improve clinical management strategies.