NIBNA: a network-based node importance approach for identifying breast cancer drivers

Abstract

Motivation Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process; first, a condition-specific network is built by incorporating gene expression data and gene networks, second, the community structures in the network are estimated and third, a centrality-based metric is applied to compute node importance. Results We apply NIBNA to the BRCA dataset and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA’s performance in detecting epithelial-mesenchymal transition (EMT) drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes. Availability The source code can be accessed at: https://github.com/mandarsc/NIBNA. Supplementary information Supplementary data are available at Bioinformatics online.

HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers

“…Pearson's correlation coefficient is the commonly used measurement for the strength of the association between a pair of genes as it conforms well to the intuitive biological notion [24]. It is widely used to identify cancer drivers, prognostic genes, and key regulators [15,25,26]. Based on the expression profile of tumor data, Pearson's correlation coefficients between miRNAs, TFs, and mRNAs were calculated.…”

“…Then a miRNA-TF-mRNA network can be constructed, in which miRNAs, TFs, and mRNAs are nodes, and the correlation coefficients between nodes are edge weights. Following the suggestion of Pham et al [15], we constructed a cancer-specific network by integrating multiple databases, including PPIs, miRTarBase, TarBase, miRWalk, TargetScan, and TransmiR. The cancerspecific network was built through removing the edges of miRNA-TF-mRNA network that not exist in the above 6 databases.…”

“…In order to prove the performance of HWVoteRank in detecting cancer drivers in cancer-specific networks, we compared HWVoteRank with CBNA [14] and NIBNA [15], which are currently efficient methods for identifying both coding and non-coding cancer drivers based on expression data. Some well-known methods for identifying key nodes in weighted networks include weighted degree [41], weighted H-index [42], weighted coreness [43], and weighted betweenness [44].…”

“…We performed HWVoteRank to detect candidate drivers that are capable of typing breast cancer. Following the suggestions of Pham et al [14,15], we classified the BRCA dataset used in this study into five subtypes by Pam50 [57,58]. We obtained 221 samples of Luminal A, 165 samples of Luminal B, 158 samples of Basal, 108 samples of Her2, and 95 samples of Normal-like.…”

“…Although CBNA is a complement for tradition methods in predicting cancer drivers, only a small percentage of cancer drivers can be identified. In order to improve the performance, a network-based algorithm NIBNA [15] was developed. The importance score is calculated for each node in the constructed undirected network based on community detection and eigenvector centrality.…”