Harnessing machine learning and AI-driven analytics to identify novel drug targets and predict chemotherapy efficacy in NSCLC.

ABSTRACT:

Introduction

Non-small cell lung cancer (NSCLC) constitutes the majority of lung cancer cases and exhibits marked heterogeneity in both clinical presentation and molecular profiles, leading to variable responses to chemotherapy. Emerging evidence suggests that mitochondria-derived RNAs (mtRNAs) may serve as novel biomarkers, although their role in predicting chemotherapy outcomes remains to be fully explored.

Methods

In this study, peripheral blood mononuclear cells were obtained from NSCLC patients for analysis of the mtRNA ratio (mt_tRNA-Tyr-GTA_5_end to mt_tRNA-Phe-GAA), while thoracic CT images were processed to derive an AI-driven BiomedGPT variable. Although individual clinical factors (Sex, Age, History_of_smoking, Pathological_type, Stage) offered limited predictive

SUBMITTER: Qin S 

PROVIDER: S-EPMC11879937 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature