Tumor relevant protein functional interactions identified using bipartite graph analyses

Divya Lakshmi Venkatraman,Shubhada R Hegde,Deepshika Pulimamidi,Harsh G Shukla

doi:10.1038/s41598-021-00879-2

Divya Lakshmi Venkatraman, Shubhada R Hegde + Show 2 more

Open Access

https://doi.org/10.1038/s41598-021-00879-2

Copy DOI

Abstract

An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types. This approach allowed us to combine expression data to functional associations in many cancers simultaneously. Further, graph centrality measures suggested the importance of upregulated genes such as BIRC5, UBE2C, BUB1B, KIF20A and PTH1R in cancer. Pathway analysis of the high centrality network nodes suggested the importance of the upregulation of cell cycle and replication associated proteins in cancer. Some of the downregulated high centrality proteins include actins, myosins and ATPase subunits. Among the transcription factors, mini-chromosome maintenance proteins (MCMs) and E2F family proteins appeared prominently in regulating many differentially regulated genes. The projected unipartite networks of the up and downregulated genes were comprised of 37,411 and 41,756 interactions, respectively. The conclusions obtained by collating these interactions revealed pan-cancer as well as subtype specific protein complexes and clusters. Therefore, we demonstrate that incorporating expression data from multiple cancers into bipartite graphs validates existing cancer associated mechanisms as well as directs to novel interactions and pathways.

Highlights

An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions
While protein–protein interactions are represented as an undirected graph, directionality is attributed from one node to another in the gene regulatory networks, cell signaling networks and phosphorylation n etworks[15]
Expression of the Parathyroid Hormone 1 Receptor (PTH1R) was significantly reduced in hepatocellular carcinoma compared to normal liver tissues[26]

Summary

Introduction

An increased surge of -omics data for the diseases such as cancer allows for deriving insights into the affiliated protein interactions. We used bipartite network principles to build protein functional associations of the differentially regulated genes in 18 cancer types This approach allowed us to combine expression data to functional associations in many cancers simultaneously. Apart from studying individual cancer types, data from multiple cancers could be combined to understand common principles governing disease establishment and their inter-connectedness. This is useful in distinguishing pathways and proteins which are unique to a given cancer from the ones that are shared by multiple cancers. In a pan-cancer mode, somatic copy number variations were analysed to identify their common patterns of occurrence in cancer[13] Over the years, such studies proved widely informative in establishing relatedness between different cancers. Two genes are connected if they tend to be associated with the same d isease[19]

Methods

Results

Conclusion