A promising new study has identified a highly accurate, non-invasive method to diagnose non-small cell lung cancer (NSCLC) using a blood test based on tRNA signatures. NSCLC accounts for roughly 85% of all lung cancer cases and remains a major global health burden, with current diagnostic tools facing limitations such as radiation exposure, invasive biopsies, and high false-positive rates.
Researchers developed a machine learning model trained on small RNA sequencing data from 1,446 tissue samples. The model identified a six-tRNA signature capable of distinguishing cancerous from non-cancerous samples with high accuracy. The signature was independently validated using 233 plasma exosome samples, confirming its robustness.
The diagnostic tool demonstrated excellent performance, with an area under the curve (AUC) of 0.97 in the discovery phase, 0.96 in hold-out validation, and 0.84 in independent validation. For early-stage NSCLC, the AUC exceeded 0.80, making it a potentially powerful tool for early detection. The signature also effectively differentiated malignant from benign samples (AUC = 0.85) and performed consistently across diverse clinical and demographic subgroups.
In addition to its diagnostic value, three of the six tRNAs were linked to patient survival outcomes, indicating potential prognostic utility. Functional analysis further revealed that these tRNAs may regulate tumor metabolism pathways.
The study highlights the potential of tRNA-based liquid biopsy as a safer, earlier, and more accessible diagnostic approach for NSCLC, while also offering new insights into the molecular mechanisms underlying lung cancer.
Reference
Feng Z et al. Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures. Communications Medicine. 2025:5:364.
