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Background: Current non-invasive approaches for lung cancer (LC) detection exhibit inherent limitations in diagnostic accuracy, or inadequate clinical validation. Consequently, there exists an urgent unmet need for rigorously validated, non-invasive biomarkers exhibiting high sensitivity and specificity to enable the early detection of LC. Methods: We employed small RNA sequencing technology to detect microRNA (miRNA) expression in small extracellular vesicle (sEV) isolated from plasma samples of study participants. The collected samples were subjected to retrospective analysis. A diagnostic model was developed (n = 80) and validated (n = 52) to discriminate between non-malignant controls (NCs, comprising healthy individuals and benign lesions cases) and patients with LC (Stages I/II). Model performance was rigorously evaluated using several metrics, with the area under the curve (AUC) serving as the primary metric. Results: The small RNA sequencing analysis of plasma sEV miRNA identified distinct expression signatures (14 differentially expressed sEV miRNAs) between NCs and LC samples. The diagnostic model with the best performance was constructed using hsa-miR-423-5p, hsa-miR-340-3p, hsa-miR-320b, hsa-miR-98-5p, hsa-miR-26a-5p, hsa-miR-193b-5p, hsa-miR-629-5p, and hsa-miR-92b-5p. The diagnostic model achieved an AUC of 0.956, a sensitivity of 94%, and a specificity of 93% in the training cohort and an AUC of 0.985, a sensitivity of 86%, and a specificity of 97% in the validation cohort. Conclusion: Our findings demonstrates that plasma sEV miRNA exhibits a highly discriminative biomarker for distinguishing NCs group from early malignant lesions, making it a promising tool for auxiliary detection of early-stage LC.
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