Machine Learning–Driven Integrative Analysis of Multi-Omics Tumor Biomarkers for Personalized Diagnosis and Treatment of Lung Cancer

Abstract

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis, pronounced molecular heterogeneity, and variable therapeutic responses. Recent advances in high-throughput sequencing technologies have enabled the generation of multi-omics data, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics, offering unprecedented opportunities to characterize tumor biology at multiple molecular layers. However, the high dimensionality, heterogeneity, and complex interactions inherent in multi-omics data pose substantial analytical challenges. Machine learning (ML), with its strong capability for pattern recognition and data integration, has emerged as a powerful paradigm for extracting clinically relevant biomarkers and decision-support models from multi-omics datasets. This paper presents a comprehensive review and methodological framework for machine learning–driven integrative analysis of multi-omics tumor biomarkers in lung cancer, focusing on applications in personalized diagnosis, prognosis prediction, therapeutic response assessment, and treatment selection. We systematically describe data preprocessing, feature selection, multi-omics integration strategies, and commonly used ML and deep learning models. Representative experimental results and comparative analyses are provided to illustrate the advantages of integrative approaches over single-omics analyses. Finally, current challenges, inter.