Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets.
Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state "signature". These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.
Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
Silpa
S
Suthram
Joel T
JT
Dudley
Annie P
AP
Chiang
Rong
R
Chen
Trevor J
TJ
Hastie
Atul J
AJ
Butte
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D004194:Disease / Q000145:classification*; D016503:Drug Delivery Systems / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016014:Linear Models; D020411:Oligonucleotide Array Sequence Analysis; D011897:Random Allocation; D018709:Statistics, Nonparametric