Identifying common transcriptome signatures of cancer by interpreting deep learning models, Genome Biology

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.

A Comparative Analysis of Single-Cell Transcriptome Identifies

Frontiers Identification of Gene Signature as Diagnostic and

Machine learning identifies signatures of macrophage reactivity

PDF) Identifying common transcriptome signatures of cancer by

A deep learning model to classify neoplastic state and tissue

Biology, Free Full-Text

Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548

Gene expression based inference of cancer drug sensitivity

Artificial intelligence in cancer target identification and drug