Modeling the relationship between gene expression and mutational signature

doi:10.15302/J-QB-022-0309

Quant. Biol.

2023, Vol. 11

Issue (1) : 31-43 https://doi.org/10.15302/J-QB-022-0309

RESEARCH ARTICLE

Modeling the relationship between gene expression and mutational signature

Limin Jiang, Hui Yu, Yan Guo(

)

Department of Internal Medicine, Comprehensive Cancer Center, University of New Mexico Albuquerque, NM 87109, USA

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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.

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

Corresponding Author(s): Yan Guo

Online First Date: 13 January 2023 Issue Date: 13 March 2023

Cite this article:

Limin Jiang,Hui Yu,Yan Guo. Modeling the relationship between gene expression and mutational signature[J]. Quant. Biol., 2023, 11(1): 31-43.

URL:

https://academic.hep.com.cn/qb/EN/10.15302/J-QB-022-0309
https://academic.hep.com.cn/qb/EN/Y2023/V11/I1/31

Fig.1 The flowchart of the study.

Fig.2 Mutational signatures resolved from TCGA somatic mutation data.

Fig.3 Results of classification models constructed using the all-gene pool.

Fig.4 Gene type composition of the features recruited by SVM models where the pool of all genes was used.

Fig.5 Results of classification models constructed using the pool of protein-coding-genes only.

Fig.6 Validation results on two validation strategies.

Fig.7 Heterogeneous tumors and tumor purity analysis against inferred results of 587 models.

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