Integrative Multi-Omics Machine Learning for Precision Cancer Prognostication

Abstract

Background: Cancer remains a leading cause of mortality worldwide, necessitating accurate prognostic tools to guide clinical decision-making and personalized treatment strategies. Traditional prognostic models based on clinicopathological features demonstrate limited predictive accuracy due to the inherent molecular heterogeneity of malignancies.This narrative review examines recent advances in multi-omics machine learning approaches for cancer prognosis prediction, synthesizing evidence from 2020–2026 on integration strategies, algorithmic methodologies, and clinical applications across major cancer types.Deep learning enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. Multi-omics integration incorporating genomics, transcriptomics, epigenomics, proteomics, and metabolomics consistently outperforms single-omics approaches. DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches, robustly predicts patient survival subtypes using multi-omics data and yields significantly better risk-stratification than other multi-omics integration methods. Graph neural networks, transformer-based architectures, and attention mechanisms have emerged as powerful tools for capturing complex inter-omics relationships. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. These models demonstrate substantial improvements in survival prediction, recurrence risk stratification, and treatment response assessment across breast, lung, colorectal, liver, and hematological malignancies. Meta-learning, spatial multi-omics, and federated learning are pivotal directions for realizing the clinical translation of next-generation precision oncology. Addressing challenges in data harmonization, model interpretability, and prospective validation remains essential for clinical implementation.