Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

ABSTRACT: Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor progression and immune regulation. This study aimed to identify exosome-related gene signatures as early predictive biomarkers for NSCLC and evaluate their diagnostic and therapeutic significance. We integrated gene expression data from GEO and TCGA databases. Core ExoNSCLC-DEGs were identified using three machine learning methods to construct a NSCLC diagnostic model, and the model was validated using ROC curves, calibration curves, and DCA curves. In addition, immune infiltration analysis, drug enrichment, molecular docking analysis, and regula

SUBMITTER: Zeng L 

PROVIDER: S-EPMC12552535 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature