Exploring big-data analysis using integrative systems biology approaches for kidney renal clear cell carcinoma studies

摘要

Synergistic integrating multiple genomic big-data at systems biology level can provide deeper insight on the molecular mechanisms relating to disease initiation and prognosis, and also guide many pathway-based biomarker identification and drug deliveries. NIH has initiated large number of genome-wide association study (GWAS), and whole genome/whole exome sequencing projects along with The Cancer Genome Atlas (TCGA) trying to identify the genomic mutations that are associated with cancer and other diseases. Those studies have produced rich data sets and have successfully identified large number of genomic mutations, including Single Nucleotide Polymorphisms (SNP), copy number variations (CNV) and structure variations (SV). However, most genomic mutations identified in those studies either have only a small disease risk effect, or are only present in a small fraction of the population in complex diseases such as cancer due the complexity and inter-patient heterogeneity. We leveraged the research by combining different genomic information including eQTL mapping, differential expression of genes and protein-protein interactions (PPI), to construct high-level gene networks for integrative genome-phoneme studies at a higher systems biology level.