Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks

TF-miRNA-motif networks (TMMN)

This website represents the result of our study about identifying protein motif in cancer pathways.

It is known that many proteins are associated with human diseases. Cellular processes are controlled by various types of biochemical networks; such as protein-protein interaction networks (PPIN), signal transduction networks (STNs), gene regulatory networks (GRN), and metabolic networks (MN).
Biological networks are composed of functional related modules, so-called network motifs, which play an essential role in many biological processes. Network motifs, such as, auto-regulation loop (ARL, either catalytic or repression), feedback loop (FBL), feed-forward loop (FFL, either coherent or incoherent), bi-fan and single-input motif (SIM) are some of the well-known modules. It is known that such modules have interesting dynamical properties.

In this work, using cancer pathways as an illustration, we performed the following studies;(i) collect highly confident regulatory relations from cancer networks and STNs, (ii) analyze the abundance of five common types of net-work motifs, (iii) merge interconnected motif types to form CMS, (iv) perform GSEA for CMS, (v) construct TMMN, (vi) perform text mining to validate the motif results, and (vii) quantify crosstalking between cancer networks and STNs.

STNs play an essential role in cancer formation. Once a component of the STN is affected, the signal would propagate and get amplified; hence, induced anti-apoptosis effect, which leads to cancer eventually.

Among the cancer types we studied, bi-fan motif is the module most often found. Some of the identified motifs are reported in the literature, which suggest the importance of modular structure in cancer formation. Bi-fan bi-fan interaction is the dominant type of Coupled motif structures (CMS). Given the CMS, it enables reconstructing the global architecture of the whole network from a bottom-up approach. MiRNA-regulated network motifs were identified. Certain bi-fan motifs are highly regulated by miRNAs. Given that a network motif can perform specific biological function, one may expect miRNA-regulated motif may result in observed phenotypic effects.

We propose to build a TFˇVmiRNAˇVmotif networks (TMMN) for cancer diseases. To the best of our knowledge, TMMN is probably the first structure con-structed to address the relationships between TFs, miRNAs, CMS, cancer networks and STNs. and the functional annotations of the cancer network motifs are based on GSEA by implementing DAVID.