Identifying common transcriptome signatures of cancer by interpreting deep learning models, Genome Biology
Por um escritor misterioso
Last updated 12 junho 2024
Background Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified. It is not clear, however, whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types. Results Here, we agnostically identify transcriptomic features that are commonly shared between cancer types using 13,461 RNA-seq samples from 19 normal tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression, or splice junction use, to distinguish between normal and tumor samples. All three models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints. Importantly, we find that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer. Conclusions Our results highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features on which core cancer pathways might converge across a large array of solid tumor types.
Machine Learning Detects Pan-cancer Ras Pathway Activation in The
Frontiers Machine learning for pan-cancer classification based
Frontiers Machine Learning: A New Prospect in Multi-Omics Data
Cancers, Free Full-Text
Cancers, Free Full-Text
Compendiums of cancer transcriptomes for machine learning
The biological interpretation of deep neural network approach
Deep neural network prediction of genome-wide transcriptome
Gene expression based inference of cancer drug sensitivity
Integrated multiomics analysis and machine learning refine
Identifying common transcriptome signatures of cancer by
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