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 tRN

SUBMITTER: Feng Z 

PROVIDER: S-EPMC12370967 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature