Abstract
Although there are huge volumes of genomic data, how to integrate and analyze cancer omics data and identify driver genes is still a challenging task. Many published approaches have made great achievements in distinguishing driver genes from passenger genes, but the identification accuracy of driver genes needs to be improved. In this paper, we adopt a semi-local centrality measure to assess the impact of gene mutations on the changes in gene expression patterns. We consider mutated gene as source node and differentially expressed genes as target nodes in the transcriptional network. Firstly, we get differentially expression genes in the cohort by comparing tumor sample expression profiles with normal sample. Secondly, we construct a local network for each mutation gene using DEGs and mutation genes according to protein-protein interaction (PPI) network. Thirdly, we calculate each mutation genes’ local centrality in the constructed network. Finally, we rank and select the driver genes from mutation genes according to its local centrality. We apply our method on five cancer datasets to identify influential genes in local network. Experimental results show that a stronger enrichment for true positive driver genes can be obtained.
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The authors acknowledge the paper materials used for experiments.
Funding
This research was funded by the National Natural Science Foundation of China (Nos. 61873001, 61672037, 61602142, 61861146002, and 61520106006), the Key Project of Anhui Provincial Education Department (No. KJ2017ZD01), the Natural Science Foundation of Anhui Province (1808085QF209).
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YH carried out the experiments, analyses presented in this work and wrote the manuscript. PJW carried out the data analysis. JX, HBW, JW and CHZ helped with project design, edited the manuscript and provided guidance and feedback throughout. All authors read and approved the final manuscript.
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Hui, Y., Wei, PJ., Xia, JF., Wang, HB., Wang, J., Zheng, CH. (2019). Discovering Driver Mutation Profiles in Cancer with a Local Centrality Score. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_26
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DOI: https://doi.org/10.1007/978-3-030-26969-2_26
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