Network based stratification of major cancers by integrating somatic mutation and gene expression data

PloS One
Zongzhen HeYajun Liu

Abstract

The stratification of cancer into subtypes that are significantly associated with clinical outcomes is beneficial for targeted prognosis and treatment. In this study, we integrated somatic mutation and gene expression data to identify clusters of patients. In contrast to previous studies, we constructed cancer-type-specific significant co-expression networks (SCNs) rather than using a fixed gene network across all cancers, such as the network-based stratification (NBS) method, which ignores cancer heterogeneity. For each type of cancer, the gene expression data were used to construct the SCN network, while the gene somatic mutation data were mapped onto the network, propagated, and used for further clustering. For the clustering, we adopted an improved network-regularized non-negative matrix factorization (netNMF) (netNMF_HC) for a more precise classification. We applied our method to various datasets, including ovarian cancer (OV), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC) cohorts derived from the TCGA (The Cancer Genome Atlas) project. Based on the results, we evaluated the performance of our method to identify survival-relevant subtypes and further compared it to the NBS method, which adopts ...Continue Reading

References

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Citations

Jun 15, 2019·BMC Bioinformatics·N Özlem Özcan ŞimşekFikret Gürgen
Apr 25, 2019·BMC Cancer·Matthew Ruffalo, Ziv Bar-Joseph

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Software Mentioned

MATLAB
R survival
HC
netNMF
Humannet
STRING
SAM

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