In:
Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 16 ( 2021-08-25), p. 2405-2413
Abstract:
To better understand the molecular features of cancers, a comprehensive analysis using multi-omics data has been conducted. In addition, a pathway activity inference method has been developed to facilitate the integrative effects of multiple genes. In this respect, we have recently proposed a novel integrative pathway activity inference approach, iDRW and demonstrated the effectiveness of the method with respect to dichotomizing two survival groups. However, there were several limitations, such as a lack of generality. In this study, we designed a directed gene–gene graph using pathway information by assigning interactions between genes in multiple layers of networks. Results As a proof-of-concept study, it was evaluated using three genomic profiles of urologic cancer patients. The proposed integrative approach achieved improved outcome prediction performances compared with a single genomic profile alone and other existing pathway activity inference methods. The integrative approach also identified common/cancer-specific candidate driver pathways as predictive prognostic features in urologic cancers. Furthermore, it provides better biological insights into the prioritized pathways and genes in an integrated view using a multi-layered gene–gene network. Our framework is not specifically designed for urologic cancers and can be generally applicable for various datasets. Availability and implementation iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW. Supplementary information Supplementary data are available at Bioinformatics online.
Type of Medium:
Online Resource
ISSN:
1367-4803
,
1367-4811
DOI:
10.1093/bioinformatics/btab086
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
detail.hit.zdb_id:
1468345-3
SSG:
12
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