Yuanyuan Chen1,2, Haitao Li1, Xiao Sun3. 1. State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China. 2. College of Science, Nanjing Agricultural University, Nanjing, 210095, P. R. China. 3. State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China. xsun@seu.edu.cn.
Abstract
BACKGROUND: It is important to understand the functional impact of somatic mutation and methylation aberration at an individual level to implement precision medicine. Recent studies have demonstrated that the perturbation of gene interaction networks can provide a fundamental link between genotype (or epigenotype) and phenotype. However, it is unclear how individual mutations affect the function of biological networks, especially for individual methylation aberration. To solve this, we provided a sample-specific driver module construction method using the 2-order network theory and hub-gene theory to identify individual perturbation networks driven by mutations or methylation aberrations. RESULTS: Our method integrated multi-omics of breast cancer, including genomics, transcriptomics, epigenomics and interactomics, and provided new insight into the synergistic collaboration between methylation and mutation at an individual level. A common driver pattern of breast cancer was identified from a novel perspective of a driver module, which is correlated to the occurrence and development of breast cancer. The constructed driver module reflects the survival prognosis and degree of malignancy among different subtypes of breast cancer. Additionally, subtype-specific driver modules were identified. CONCLUSIONS: This study explores the driver module of individual cancer, and contributes to a better understanding of the mechanism of breast cancer driven by the mutations and methylation variations from the point of view of the driver network. This work will help identify new therapeutic combinations of gene mutations and drugs in humans.
BACKGROUND: It is important to understand the functional impact of somatic mutation and methylation aberration at an individual level to implement precision medicine. Recent studies have demonstrated that the perturbation of gene interaction networks can provide a fundamental link between genotype (or epigenotype) and phenotype. However, it is unclear how individual mutations affect the function of biological networks, especially for individual methylation aberration. To solve this, we provided a sample-specific driver module construction method using the 2-order network theory and hub-gene theory to identify individual perturbation networks driven by mutations or methylation aberrations. RESULTS: Our method integrated multi-omics of breast cancer, including genomics, transcriptomics, epigenomics and interactomics, and provided new insight into the synergistic collaboration between methylation and mutation at an individual level. A common driver pattern of breast cancer was identified from a novel perspective of a driver module, which is correlated to the occurrence and development of breast cancer. The constructed driver module reflects the survival prognosis and degree of malignancy among different subtypes of breast cancer. Additionally, subtype-specific driver modules were identified. CONCLUSIONS: This study explores the driver module of individual cancer, and contributes to a better understanding of the mechanism of breast cancer driven by the mutations and methylation variations from the point of view of the driver network. This work will help identify new therapeutic combinations of gene mutations and drugs in humans.
Authors: O Troyanskaya; M Cantor; G Sherlock; P Brown; T Hastie; R Tibshirani; D Botstein; R B Altman Journal: Bioinformatics Date: 2001-06 Impact factor: 6.937
Authors: Jing-Dong J Han; Nicolas Bertin; Tong Hao; Debra S Goldberg; Gabriel F Berriz; Lan V Zhang; Denis Dupuy; Albertha J M Walhout; Michael E Cusick; Frederick P Roth; Marc Vidal Journal: Nature Date: 2004-06-09 Impact factor: 49.962
Authors: Eswaran Devarajan; Aysegul A Sahin; Jack S Chen; Raghu R Krishnamurthy; Neeraj Aggarwal; Anne-Marie Brun; Anna Sapino; Fan Zhang; Dhawal Sharma; Xiao-He Yang; Ann D Tora; Kapil Mehta Journal: Oncogene Date: 2002-12-12 Impact factor: 9.867
Authors: Helen E Bryant; Niklas Schultz; Huw D Thomas; Kayan M Parker; Dan Flower; Elena Lopez; Suzanne Kyle; Mark Meuth; Nicola J Curtin; Thomas Helleday Journal: Nature Date: 2005-04-14 Impact factor: 69.504