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.

Author summary

To overcome the limitations of non-negative matrix factorization in the situation of sparse mutation, a method was designed in this paper to predict mutational signatures based on RNA-seq data. This method first was used to build the associations between gene expression and 49 established mutational signatures. Then, a total of 587 successful models covering 31 cancer types were obtained based on the condition of accuracies and AUCs (Area-Under-Curve) are both greater than 0.8. Finally, all successful models were assembled to form an online tool (EMSI) as a component of the MutEx analysis suite, and models can be visited at the website of innovebioinfo.

Graphical abstract

Keywords

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

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ABBREVIATIONS

AESA	Advanced expression survival analysis
ACC	Adrenocortical carcinoma
BLCA	Bladder urothelial carcinoma
BRCA	Breast invasive carcinoma
CESC	Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL	Cholangiocarcinoma
COAD	Colon adenocarcinoma
DLBC	Lymphoid neoplasm diffuse large B-cell lymphoma
eQTL	gene expression quantitative trait loci
ESCA	Esophageal carcinoma
GBM	Glioblastoma multiforme
HNSC	Head and neck squamous cell carcinoma
KICH	Kidney chromophobe
KIRC	Kidney renal clear cell carcinoma
KIRP	Kidney renal papillary cell carcinoma
LAML	Acute myeloid leukemia
LGG	Brain lower grade glioma
LIHC	Liver hepatocellular carcinoma
LUAD	Lung adenocarcinoma
LUSC	Lung squamous cell carcinoma
MESO	Mesothelioma
OV	Ovarian serous cystadenocarcinoma
PAAD	Pancreatic adenocarcinoma
PCPG	Pheochromocytoma and paraganglioma
PRAD	Prostate adenocarcinoma
READ	Rectum adenocarcinoma
RF	Random forest
ROC	Receiver operating characteristics
SARC	Sarcoma
SBS	Single base substitutions
SKCM	Skin cutaneous melanoma
STAD	Stomach adenocarcinoma
SVM	Support vector machine
TCGA	The Cancer Genome Atlas
TGCT	Testicular germ cell tumors
THCA	Thyroid carcinoma
THYM	Thymoma
UCEC	Uterine corpus endometrial carcinoma
UCS	Uterine carcinosarcoma
UV	Ultraviolet
UVM	Uveal melanoma
XGBoost	EXtreme Gradient Boosting

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/J-QB-022-0309

ACKNOWLEDGEMENTS

This work was supported by Cancer Center Support Grant from the National Cancer Institute (P30CA118100). The funder had no role in this study. This study is also supported by Bioinformatics Shared Resource, Analytical, and Translational Genomics Shared Resource at the University of New Mexico, Comprehensive cancer center.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Limin Jiang, Hui Yu and Yan Guo declare that they have no competing interests. The article does not contain any human or animal subjects performed by any of the authors.

OPEN ACCESS

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2023 The Author (s). Published by Higher Education Press.