Cancer Bioinformatics: Integrating Multi-Omics Data for Precision Oncology

Abstract

 
Cancer bioinformatics has evolved as a pivotal discipline in precision oncology, shifting from  single-omics analyses to integrated multi-omics approaches that encompass genomics,  epigenomics, transcriptomics, proteomics, metabolomics, and radiomics. This review explores the  foundational biological hierarchy, computational methodologies (including early, intermediate,  and late integration strategies), and advanced artificial intelligence techniques such as variational  autoencoders, graph convolutional networks, and explainable AI to decode tumor heterogeneity  and therapeutic vulnerabilities. Key innovations like the CancerSD model address incomplete data  challenges, while single-cell and spatial omics technologies reveal intra-tumoral dynamics and  microenvironment interactions. Despite barriers in data regulation, computational infrastructure,  and ethical considerations, emerging trends in federated learning, quantum computing, and digital  twins promise transformative clinical applications. By synthesizing multi-omics data, this  framework advances from population-based to individualized cancer care, enhancing biomarker  discovery, drug response prediction, and patient outcomes.