Lung cancer is one of the most common and deadly cancers worldwide, with non-small cell lung cancer (NSCLC) making up about 85 percent of cases. Detecting it early is critical, but current methods like CT scans and biopsies can be invasive, carry risks, and sometimes produce false positives.

A research team from the University of Hawaii at Manoa has developed a promising new approach that relies on liquid biopsy, a simple blood test, instead of traditional imaging or tissue samples. By analyzing small RNA sequencing data from more than 1400 lung tissue samples, the team identified a unique signature of six transfer RNAs (tRNAs) that can distinguish cancerous samples from healthy ones with high accuracy.

Principal component analysis (PCA) and study design based on sample distribution

a Sample size and diagnostic classification for each dataset included in the study. Bar colors represent different diagnostic categories. b PCA plot of all samples, where each point represents a specimen. Green points indicate control samples (normal and benign), orange points represent cancer samples. The shape of each point corresponds to its dataset of origin. c Schematic representation of the study’s methodological framework.

The researchers confirmed these results by testing plasma exosome samples, where the six-tRNA signature continued to show strong diagnostic performance, especially for detecting lung cancer at an early stage. Not only did this method achieve excellent accuracy, it also worked reliably across different clinical and demographic groups.

Interestingly, some of the tRNAs in the signature were linked to patient survival, suggesting they could also provide valuable prognostic information. Further functional experiments revealed that these molecules may play a role in cancer metabolism, shedding light on how lung tumors grow and survive.

By pairing RNA sequencing with machine learning, the team has opened the door to a less invasive, more accurate way to diagnose lung cancer and better understand its biology. This could pave the way for earlier detection, improved treatment decisions, and new insights into how tumors develop.