Research On The Identification Of Gene Inclusion Relationships-oriented Cancer Driver Pathways

The identification of cancer driver pathways is of great significance for cancer research and provides theoretical support for understanding the pathogenesis of cancer.The accumulation of large amounts of omics data has made it possible to identify cancer driver pathways through computational methods,which is believed to provide key information for downstream research such as determining the pathogenesis of cancer and developing anticancer drugs.Currently,the main method for identifying cancer driver pathways is to utilize various genomics knowledge and combine pathway characteristics and gene associations in protein protein interaction(PPI)networks.Using multiple omics data for data fusion and denoising,and combining the relevant knowledge contained in protein protein interaction networks,a method considering coverage,mutual exclusion,and gene associations in protein protein interaction(PPI)networks(CPGASMCMN)was proposed.A novel measurement of mutual exclusivity is devised to exclude some gene sets with “inclusion” relationship.By introducing gene clustering based operators,a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model.Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods.The comparisons of models demonstrate that the SMCMN model does eliminate the “inclusion” relationship,and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases.In addition,the gene set recognized by the proposed CPGA-SMCMN method possesses more genes engaging in known cancer related pathways,as well as stronger connectivity in PPI network.All of which have been demonstrated through extensive contrast experiments among method CPGA-SMCMN and six state-of-the-art ones.Based on the identification of cancer individual driver pathways,considering the cooccurrence of patients,the interaction of genes,and the coverage and mutual exclusion characteristics of individual driver pathways,a patient gene co-occurrence co-interaction model(PGCO)is proposed,and a new algorithm ICPGA is improved to solve the PGCO model.The model uses a dual core simultaneous mutation collaborative approach to identify two individual driver pathways,and evaluates the collaborative performance between the two individual driver pathways based on a fitness function.A large number of experiments were conducted on real biological data from three cancers to compare the recognition efficiency of this method with previous methods.The experimental results show that the ICPGA-PGCO method identifies more genes in two gene sets that participate in known cancer related pathways,while the gene enrichment in the two gene sets is more balanced,and has stronger connectivity in the PPI network.All of this has been demonstrated through extensive experiments between the ICPGAPGCO method and three state-of-the-art methods.In summary,this article analyzed the coverage,mutual exclusion,and gene correlation of individual driver pathways based on three omics data: somatic mutation data,PPI network data,and copy number variation data.On this basis,it conducted in-depth research on the identification of cancer cooperative driver pathways,and designed SMCMN model,PGCO model,and CPGA and ICPGA algorithms,this provides an auxiliary tool for the research of cancer individual driver and cooperative driver pathways.