Aim: In exploiting cancer initialization and progression, a great challenge is to identify the driver genes.
Background: With advances in Next-Generation Sequencing (NGS) technologies, the identification of specific oncogenic genes has emerged through integrating multi-omics data. Although the existing computational models have identified many common driver genes, they rely on individual regulatory mechanisms or independent copy number variants, ignoring the dynamic function of genes in pathways and networks.
Objective: The molecular metabolic pathway is a critical biological process in tumor initiation, progression and maintenance. Establishing the role of genes in pathways and networks helps to describe their functional roles under physiological and pathological conditions at multiple levels.
Methods: We present a metabolic pathway based driver genes identification (pathDriver) to distinguish different cancer types/subtypes. In pathDriver, combined with protein-protein interaction network, the metabolic pathway is utilized to construct the pathway network. Then, the Interaction Frequency (IF) and Inverse Pathway Frequency (IPF) are used to evaluate the collaborative impact factor of genes in the pathway network. Finally, the cancer-specific driver genes are identified by calculating the scores of edges connected to genes in the pathway network.
Results: We applied it to 16 kinds of TCGA cancers for pan-cancer analysis.
Conclusion: The driving pathway identified biologically significant known cancer genes and the potential new candidate genes.