Explore molecular interactions across cancer types with CancerNet

Cancer is a complex disease characterized by a large number of molecular interaction alterations. Knowledge of molecular interactions is quite useful for discovering the functions of molecules and the processes they are involved in. However, there is little systematic insight into the nature and scale of the potential interactions in human cancers.

 

 

To solve this gap, Dr. Ming Chen and his colleagues from the Institute of Bioinformatics in Zhejiang University constructed CancerNet, a database for decoding multilevel molecular interactions across diverse cancer types. Here, they describe the features of CancerNet.

The CancerNet database

 

CancerNet aims to provide cancer-specific molecular interaction networks across multiple cancer types. It includes 33 human cancer types. The interactions contain protein-protein interactions (PPIs), miRNA–target interactions and miRNA–miRNA synergistic interactions.

 

 

Experimentally detected PPIs were assembled from five major PPI databases and miRNA–target interactions were considered as the combination of the predicted targets from six algorithms and two experimentally validated data sets. Synergistic miRNA pairs were predicted according to the functions of target genes as well as their proximity in the PPI network. By integrating expression data in different cancer samples and information from Gene Ontology (GO) annotations, cancer-wide and cancer-specific molecular interactions were identified (Figure 1).

As a result, CancerNet offers a unique platform for assessing the roles of proteins and miRNAs, as well as their interactions across human cancers.

 

cancernet-omictools
Figure 1. Overall design of CancerNet.

 

A flexible and user-friendly query method is provided by CancerNet, users can query it using one molecule to retrieve its interaction partners per cancer, or using a pair of interacting molecules to retrieve the cancer types that the interaction appears in. For each molecule, two types of identifier can be used.

 

 

CancerNet output data provide lists of interactions and detail information about the interactions. Expression levels and functional similarity score are provided for the PPI and miRNA–miRNA result list, respectively. And for the miRNA–target result list, the Pearson correlation coefficient and P-value are offered.

 

 

In addition, a graphical network viewer was developed to visualize these molecular interactions. And a ‘GO Enrichment Analysis’ module was provided for miRNA–target list to explore the biological functions of target genes.

 

 

CancerNet can be freely accessed at http://bis.zju.edu.cn/CancerNet.

 

Reference

 

Meng et al. (2015). CancerNet: a database for decoding multilevel molecular interactions across diverse cancer types. Oncogenesis.