Kim SY, Choe EK, Shivakumar M, Kim D, Sohn KA. Multi-layered network-based pathway activity inference using directed random walks: application to predicting clinical outcomes in urologic cancer.
Bioinformatics 2021;
37:2405-2413. [PMID:
33543748 PMCID:
PMC8388033 DOI:
10.1093/bioinformatics/btab086]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 12/11/2020] [Accepted: 02/02/2021] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION
To better understand the molecular features of cancers, a comprehensive analysis using multiomics data has been conducted. Additionally, 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
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.
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