当前位置： SCI文献检索 > Scientific Reports期刊下所有文献 > Identification of significantly mutated subnetworks in the breast cancer genome.

Identification of significantly mutated subnetworks in the breast cancer genome.

Abstract：

:Recent studies showed that somatic cancer mutations target genes that are in specific signaling and cellular pathways. However, in each patient only a few of the pathway genes are mutated. Current approaches consider only existing pathways and ignore the topology of the pathways. For this reason, new efforts have been focused on identifying significantly mutated subnetworks and associating them with cancer characteristics. We applied two well-established network analysis approaches to identify significantly mutated subnetworks in the breast cancer genome. We took network topology into account for measuring the mutation similarity of a gene-pair to allow us to infer the significantly mutated subnetworks. Our goals are to evaluate whether the identified subnetworks can be used as biomarkers for predicting breast cancer patient survival and provide the potential mechanisms of the pathways enriched in the subnetworks, with the aim of improving breast cancer treatment. Using the copy number alteration (CNA) datasets from the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study, we identified a significantly mutated yet clinically and functionally relevant subnetwork using two graph-based clustering algorithms. The mutational pattern of the subnetwork is significantly associated with breast cancer survival. The genes in the subnetwork are significantly enriched in retinol metabolism KEGG pathway. Our results show that breast cancer treatment with retinoids may be a potential personalized therapy for breast cancer patients since the CNA patterns of the breast cancer patients can imply whether the retinoids pathway is altered. We also showed that applying multiple bioinformatics algorithms at the same time has the potential to identify new network-based biomarkers, which may be useful for stratifying cancer patients for choosing optimal treatments.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Ajwad R,Domaratzki M,Liu Q,Feizi N,Hu P

doi

10.1038/s41598-020-80204-5

subject

Has Abstract

pub_date

2021-01-12 00:00:00

pages

642

issue

issn

2045-2322

pii

10.1038/s41598-020-80204-5

journal_volume

pub_type

杂志文章

在线工具

Identification of significantly mutated subnetworks in the breast cancer genome.