Identification of PIWI-interacting RNAs based models for lung adenocarcinoma early detection: a multicenter cohort study.

Early detection of lung adenocarcinoma (LUAD) remains a major clinical challenge despite the widespread application of low-dose computed tomography (LDCT). Circulating PIWI-interacting RNAs (piRNAs), characterized by tumor-specific expression and high stability, offer promise as non-invasive biomarkers. To improve the diagnostic precision of LDCT screening, we performed a large multi-center study integrating paired tissue-serum omics profiling with machine learning-based biomarker discovery. From 1,521 serum samples (1,033 LUAD, 89 benign pulmonary nodules, and 399 healthy controls), two tumor-derived PIWI-interacting RNAs (piR-hsa-8393202 and piR-hsa-8429916) were identified as highly stable, LUAD-specific molecules closely associated with disease progression. A 2-piRNA diagnostic signature demonstrated robust performance for early-stage LUAD (training AUC = 0.918; validation AUC = 0.863) and adenocarcinoma in situ (training AUC = 0.902; validation AUC = 0.907). Notably, when applied to LDCT-detected indeterminate pulmonary nodules, this signature significantly improved malignant nodule identification (AUC = 0.883), outperforming conventional serum biomarkers such as carcinoembryonic antigen and cytokeratin 19 fragment antigen 21-1. Functional assays further revealed that these piRNAs promote tumor cell proliferation and suppress apoptosis, supporting their oncogenic activity. Collectively, this study establishes circulating piRNAs as non-invasive and mechanistically relevant biomarkers for molecular stratification of pulmonary nodules within LDCT screening programs, providing a clinically applicable tool to refine early lung cancer diagnosis and guide individualized management.