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

We propose a novel node importance-based network analysis framework to detect coding and non-coding cancer drivers

Mandar S. Chaudhary; Vu V.H. Pham; Thuc D. Le

2021

Scholarcy highlights

  • Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics
  • We propose a novel node importance-based network analysis 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
  • NIBNA 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
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