Abstract
The rapid accumulation of multi-omics cancer data has created the opportunity for biological discovery and biomedical applications. In this study, we propose an approach that integrates multi-omics data to identify dysregulated pathways driving cancer subtypes, which simultaneously considers DNA methylation, DNA copy number, somatic mutation and gene expression profiles. After applying it to Breast Invasive Carcinoma (BRCA) in TCGA, we identify distinct top 30 dysregulated pathways for each breast cancer subtypes. The result suggests that dysregulated pathways of different subtypes display common and specific patterns. Furthermore, 44 differentially expressed genes with corresponding genetic and epigenetic dysregulation are retrieved from the subtype-specific pathways. Literature validation and functional enrichment analysis indicate that these genes are function associated with BRCA. Our method provides a new insight for identifying the driver of cancer subtypes through multi-omics data integration.
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Acknowledgements
This work was supported by the National NSFC (Grant No. 61532014 & No. 61432010 & No. 61672407 & No. 61772395), the Fundamental Research Funds for the Central Universities (No. JB150303) and the Fundamental Research Funds for young teacher (2017KY0264).
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Shi, K., Gao, L., Wang, B. (2018). Inferring Dysregulated Pathways of Driving Cancer Subtypes Through Multi-omics Integration. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_9
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DOI: https://doi.org/10.1007/978-3-319-94968-0_9
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