Computational models for pan-cancer classification based on multi-omics data

Jianlin Wang

doi:10.3389/fgene.2025.1667325

Computational models for pan-cancer classification based on multi-omics data

2025-10-28

0

OA

AI

DOI：10.3389/fgene.2025.1667325

Original

Save

View PDF

Abstract

En 中文

Tumor heterogeneity presents a significant challenge in cancer treatment; limiting the ability of clinicians to achieve accurate early-stage diagnoses and develop customized therapeutic strategies. Early diagnosis is crucial for effective intervention; yet current methods lack robust solutions to overcome this challenge. The Pan-Cancer Atlas has emerged as a pivotal framework to investigate cancer heterogeneity by integrating multi-omics data (genomics; transcriptomics; proteomics) across tumor types. This initiative systematically maps inter- and intratumor variations; providing insight for clinical decision making. However; such frameworks often struggle to integrate dynamic temporal changes and spatial heterogeneity within tumors; limiting their real-time clinical applicability. In this review; we first summarize the available multi-omics data and public biomedical databases used in pan-cancer research. Then; we examine current pan-cancer classification approaches based on the computational models they employed; including machine learning and deep learning. We also provide a comparison of these classification methods to explore their advantages and limitations. Finally; we conclude by discussing the key challenges in pan-cancer research and suggesting potential directions for future studies.

Computational models for pan-cancer classification based on multi-omics data

Abstract

AI Summary

Journal

Organization