Modeling the relationship between gene expression and mutational signature

Abstract

Background: Mutational signatures computed from somatic mutations, allow an in-depth understanding of tumorigenesis and may illuminate early prevention strategies. Many studies have shown the regulation effects between somatic mutation and gene expression dysregulation.

Methods: We hypothesized that there are potential associations between mutational signature and gene expression. We capitalized upon RNA-seq data to model 49 established mutational signatures in 33 cancer types. Both accuracy and area under the curve were used as performance measures in five-fold cross-validation.

Results: A total of 475 models using unconstrained genes, and 112 models using protein-coding genes were selected for future inference purposes. An independent gene expression dataset on lung cancer smoking status was used for validation which achieved over 80% for both accuracy and area under the curve.

Conclusion: These results demonstrate that the associations between gene expression and somatic mutations can translate into the associations between gene expression and mutational signatures.

Graphical abstract

Keywords

mutational signature / gene expression / support vector machine / random forest / extreme gradient boost

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The Author (s). Published by Higher Education Press.