# Multiomics Research Strategies in Cancer: A Growing and Innovative Field

## TL;DR

This review explores how combining multiple omics data helps understand cancer better and improve precision medicine.

## Contribution

The paper highlights innovative multiomics strategies and deep learning integration in cancer research.

## Key findings

- Multiomics data integration provides insights into cancer pathogenesis and biomarker discovery.
- Single-cell and spatial omics are advancing precision medicine and early diagnosis in cancer.
- Deep learning approaches are being used to integrate multiomics data for better therapeutic strategies.

## Abstract

Cancer is a highly complex and heterogeneous disease involving multiple pathophysiological events. Despite significant advances in modern medicine, the molecular mechanisms of cancer are still largely unknown. Omics methods have opened new avenues for identifying cancer biomarkers and elucidating disease pathogenesis. However, single‐omics approaches only provide a limited understanding of biological mechanisms. The comprehensive analysis of multiomics data will provide useful insights for the pathogenesis, identification of therapeutic targets, and discovery of biomarkers in cancer. Here, we reviewed the disease signatures of cancer. We then reviewed the current state of multiomics biomarkers research in cancer. To further delineate the upstream pathogenic changes and downstream molecular effects of cancer, we also discuss the current strategies for integrating multiomics data using deep learning approaches. In addition, single‐cell and spatial omics are being used to guide treatment strategies, risk assessment, and early diagnosis, as well as their potential impact on precision medicine. Despite the relative youth of the field, the development of single‐cell and spatial omics promises to provide a powerful tool for elucidating the pathogenesis of cancer.

This review highlights multiomics strategies in cancer research, focusing on integration methods from genomics to microbiomics. Using colorectal cancer as a key example, it discusses biomarker discovery, data integration via deep learning, and the roles of single‐cell and spatial omics. The article underscores the translational potential of multiomics in advancing precision oncology and improving clinical outcomes.