A robust machine learning model based on ribosomal-subunit-derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large-scale of sequencing data.

ABSTRACT: Nonsmall cell lung cancer (NSCLC) is a lethal cancer and lacks robust biomarkers for noninvasive clinical diagnosis. Detecting NSCLC at the early stage can decrease the mortality rate and minimise harm caused by various treatments. We curated 2050 samples from public tissue and plasma datasets including both invasive and noninvasive types, then supplemented with in-house pooled plasma and exosome samples. Eleven independent transcriptome datasets were utilised to develop a new machine learning model by integrating PIWI-interacting RNA (piRNA) to predict NSCLC. Five piRNA signatures derived from ribosomal subunits identified to be tumour-specific exhibited robust diagnostic ability and were combined into a piRNA-Based Tumour Probability Index (pi-TPI) risk evaluation model. pi-TPI effective

SUBMITTER: Gao Z 

PROVIDER: S-EPMC12410371 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature