한빛사논문
- 조회202
Bayarbaatar Amgalan1, Damian Wojtowicz1,2, Yoo‑Ah Kim1† and Teresa M. Przytycka1*†
1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, 20894 Bethesda, USA.
2Current address: Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, ul. Banacha 2, 02‑097 Warszawa, Poland.
†Yoo-Ah Kim and Teresa M. Przytycka contributed equally to this work.
*Corresponding author : Correspondence to Teresa M. Przytycka.
Abstract
Background
There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures.
Methods
To gain insights into these relationships, we developed a network-based method, named GENESIGNET that constructs an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes.
Results
Applying GENESIGNET to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GENESIGNET also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GENESIGNET also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway.
Conclusions
GENESIGNET provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The GENESIGNET method was implemented in python, and installable package, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/GeneSigNet.
논문정보
- Research article
- 2023년 03월 (BRIC 등록일 2023-06-09)
- 국외 연구진
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