Abstract
BackgroundThe mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research.ResultsIn this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges.ConclusionsThe identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients.
Highlights
Complex diseases, such as cancers, are kinds of evolutionary diseases [1, 2], which involve successive stages from early initiation to advanced end-stages
The main objective of this paper is to investigate a versatile working flow that can address the staging evolution processes of complex diseases, which including: (1) the identification of stage-specific cancer related genes, (2) the construction of their related dynamic modules, and (3) the generation of the stage related pathway networks
Data sources and preprocessing The Level 3 clinical information and genomic datasets were obtained from the FIREHOSE Broad Genome Data Analysis Centers (GDACs) [32]
Summary
Complex diseases, such as cancers, are kinds of evolutionary diseases [1, 2], which involve successive stages from early initiation to advanced end-stages. Take the Cancer Genome Atlas (TCGA) project for example, it has generated multi-omics datasets over the genomic, epigenetic and transcriptome levels together with clinical data for more than 30 human tumors [6,7,8,9] These multiple omics datasets provided many high-resolution molecular profiles, such as gene expression (microarray, RNA-seq), copy number variation (CNV or sCNA), DNA methylation, mRNA expression, somatic mutation, protein expression, as well as clinical information describing specific metrics, which including pathological stages, clinical stages, grade and age at diagnosis. They are highly variable in term of availability from disease to disease. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research
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