Integrated Analysis Of Re-sequencing Mutation Data Identifies Key Genes In Liver Cancer

Background:Recent years, genome or exome sequencing plays an increasingly important role in exploring the landscape of liver cancer, including hepatocellular carcinoma (HCC), cholangiocarcinoma and hepatoblastoma(HB), and thus a tremendous amount of mutations in protein-coding genes have been generated. These studies, however, just focused on specific candidate genes, and many significant mutations have been masked.Principal results:In this study, we conduct integrated analysis for mutation data of liver cancer from 13 different sources, identifying key genes and recurrent mutated genes.45 key genes has been located for HCC(mutation rate>5%):besides well-know cancer genes TP53, CTNNB1, AXIN1 and recently covered HCC associated genes COL11A1, USH2A, LRP1B, ALB, GRPR98 etc.,TNN, PCLO, RYR2, MUC4 are identified as recurrent mutated genes which have never been reported in HCC study. To further understand these key genes, we performed prognosis analysis for them, discovering 7 key genes related with prognosis both in survival analysis and cox proportional hazard model analysis. Finally, via gene co-expression network analysis and Enrichment Analysis did we locate 8 pathways and 26 functions which were significantly mutated in HCC samples, mainly concentrated on cell transfer, transcription, replication and immune response functions.Conclusions:The key genes and recurrent mutation identified by our study may provide a potential target for liver cancer therapy. Our work indicates integrated analysis for multiple studies with the same subject can increase the statistical power for the result, but also uncover the valuable biological rule, incidents and meaningful information which might otherwise be masked under single data sets. This kind of bioinformatics method fit with the impressing demand of making best use of proliferated biological data to expedite the process of deciphering human complicated disease like cancer.