Kim So Yeon, Choe Eun Kyung, Shivakumar Manu, Kim Dokyoon, Sohn Kyung-Ah
Department of Software and Computer Engineering, Ajou University, Suwon 16499, South Korea.
Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Bioinformatics. 2021 Aug 25;37(16):2405-2413. doi: 10.1093/bioinformatics/btab086.
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.
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.
iDRW is implemented as the R software package. The source codes are available at https://github.com/sykim122/iDRW.
Supplementary data are available at Bioinformatics online.
为了更好地理解癌症的分子特征，已使用多组学数据进行了全面分析。此外，还开发了一种通路活性推断方法，以促进多个基因的综合作用。在这方面，我们最近提出了一种新颖的综合通路活性推断方法iDRW，并证明了该方法在将两个生存组进行二分方面的有效性。然而，存在一些局限性，例如缺乏通用性。在本研究中，我们通过在多层网络中指定基因之间的相互作用，利用通路信息设计了一个有向基因-基因图。
作为一项概念验证研究，使用泌尿系统癌症患者的三个基因组图谱对其进行了评估。与单独使用单个基因组图谱和其他现有的通路活性推断方法相比，所提出的综合方法实现了更好的结果预测性能。该综合方法还确定了常见/癌症特异性候选驱动通路，作为泌尿系统癌症的预测性预后特征。此外，它使用多层基因-基因网络，从综合视角为优先排序的通路和基因提供了更好的生物学见解。我们的框架并非专门为泌尿系统癌症设计，可普遍适用于各种数据集。
iDRW作为R软件包实现。源代码可在https://github.com/sykim122/iDRW获取。
补充数据可在《生物信息学》在线获取。
