Publication Date
1-1-2025
Journal
Nature Cancer
DOI
10.1038/s43018-024-00869-z
PMID
39663389
PMCID
PMC12036749
PubMedCentral® Posted Date
1-1-2026
PubMedCentral® Full Text Version
Author MSS
Abstract
Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.
Keywords
Humans, Proteogenomics, Neoplasms, Machine Learning, Protein Interaction Maps, Mutation, Neural Networks, Computer, Proteomics, Gene Regulatory Networks
Published Open-Access
yes
Recommended Citation
Shi, Zhiao; Lei, Jonathan T; Elizarraras, John M; et al., "Mapping the Functional Network of Human Cancer Through Machine Learning and Pan-Cancer Proteogenomics" (2025). Faculty, Staff and Students Publications. 6271.
https://digitalcommons.library.tmc.edu/baylor_docs/6271