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Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics. | LitMetric

AI Article Synopsis

  • Large-scale omics profiling has highlighted numerous somatic mutations and cancer-related proteins, making it difficult to understand their functions in cancer biology.
  • The FunMap network is developed using machine learning on data from 1,194 individuals across 11 cancer types, accurately linking over 10,500 protein-coding genes and identifying important functional protein modules.
  • This study positions FunMap as a valuable tool for interpreting complex cancer data, helping to predict the roles of lesser-known cancer-associated proteins and enhancing strategies for cancer treatment and research.

Article 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.

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Source
http://dx.doi.org/10.1038/s43018-024-00869-zDOI Listing

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