Liquid biopsy diagnostics for non-small cell lung cancer via elucidation of tRNA signatures.

Abstract

Background: Lung cancer, particularly non-small cell lung cancer (NSCLC), accounts for about 85% of all lung cancer cases and remains a major global health challenge. Traditional diagnostic methods, such as chest X-rays and low-dose CT scans, have limitations, including high false-positive rates, radiation risks, and the invasiveness of tissue biopsies. This study aims to develop a non-invasive liquid biopsy approach for early NSCLC diagnosis.

Methods: We developed a machine-learning model to analyze small RNA sequencing data from 1446 tissue samples to identify a diagnostic tRNA signature. This signature was independently validated using the in-house data of 233 plasma exosome samples. Diagnostic performance was assessed using Area Under the Curve (AUC) metrics. Signature tRNAs were then evaluated across various clinical and demographic variables, with further survival analysis and functional studies to explore the molecular role of the signature tRNAs.

Results: We identify a robust six-tRNA signature with strong diagnostic performance, achieving AUC values of 0.97 in discovery, 0.96 in hold-out validation, and 0.84 in independent validation. The signature effectively distinguishes cancerous from benign samples (AUC = 0.85) and consistently performs across clinical and demographic variables, with AUC values exceeding 0.80, particularly for early-stage lung cancer diagnosis. Additionally, three signature tRNAs demonstrate prognostic value for independent survival prediction. Functional studies suggest potential regulatory roles of specific tRNAs and their associated fragments in tumor metabolism pathways.

Conclusions: This research underscores the diagnostic power of tRNA signature for NSCLC liquid biopsy and provides epigenetic insights that enhance our understanding of oncogenic molecular pathophysiology.