The progress of prostate cancer in pathway level explored by protein network with gene expression

Biological pathways are the crucial biological mechanisms in living cells. The huge volume of genomics and proteomics data requires computational methods for predicting or reconstructing pathways. Thus, the application of protein-protein interaction (PPI) or gene expression methods is insufficient to discover meaningful pathways. The integration of PPIs and gene profiles is a better approach to uncover the regulation of pathway and must be utilized well. Previous studies on this topic only focus on the gene level or some limited local groups. This study presents an approach to finding potential fragments of active pathways around known pathways between the various stages of diseases. The proposed method used a maximum score-based function that integrates genomics and proteomics information. This method quantified the strength of gene expression change and the degree of protein-protein interactions to illustrate global status as pathway maps. In this study, we use prostate cancer data as an example to explain which potential fragments of pathway co-constructed a pathway map of prostate cancer at different disease statuses. The resulting map shows a possible correspondence between known pathway and cancer-related genes that are not on the known pathway. Comparing distinct status pathway map reveals a global change of different disease states pathway level. The pathway map of different disease statuses can provide more insight in the progress of cancer.