Identification of hub subnetwork based on topological features of genes in breast cancer

Abstract

The aim of this study was to provide functional insight into the identification of hub subnetworks by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We applied a protein network-based approach to identify subnetworks which may provide new insight into the functions of pathways involved in breast cancer rather than individual genes. Five groups of breast cancer data were downloaded and analyzed from the Gene Expression Omnibus (GEO) database of high-throughput gene expression data to identify gene signatures using the genome-wide global significance (GWGS) method. A PPI network was constructed using Cytoscape and clusters that focused on highly connected nodes were obtained using the molecular complex detection (MCODE) clustering algorithm. Pathway analysis was performed to assess the functional relevance of selected gene signatures based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Topological centrality was used to characterize the biological importance of gene signatures, pathways and clusters. The results revealed that, cluster1, as well as the cell cycle and oocyte meiosis pathways were significant subnetworks in the analysis of degree and other centralities, in which hub nodes mostly distributed. The most important hub nodes, with top ranked centrality, were also similar with the common genes from the above three subnetwork intersections, which was viewed as a hub subnetwork with more reproducible than individual critical genes selected without network information. This hub subnetwork attributed to the same biological process which was essential in the function of cell growth and death. This increased the accuracy of identifying gene interactions that took place within the same functional process and was potentially useful for the development of biomarkers and networks for breast cancer.

Figure 1

Column chart of expression changes of the top 50 ranked gene signatures in the five breast cancer data sets. Each single colored bar represents the fold change of a gene signature in a specific breast cancer data set. Bars plotted above the x-axis denote upregulation, while those plotted below the x-axis denote downregulation. The gene signatures are ordered depending on their genome-wide global significance (GWGS) values.

Figure 2

Interactome of the 366 genes showing 366 nodes and 2,760 edges in the protein-protein interaction map encompassing four clusters in breast cancer. Genes were denoted as nodes in the graph and interactions between them were presented as edges. Green color indicates downregulated genes, red color indicates upregulated genes; the node size represents the degree value.

Figure 3

Best four interconnected clusters among the 366 genes and their interactions with neighboring genes. Green color indicates downregulated genes, while red color indicates upregulated genes; the node size represents the genome-wide global significance (GWGS) value. (A–D) cluster1, cluster2, cluster3 and cluster4, respectively.

Figure 4

Integrated centralities based analysis of clusters and pathways. (A–D) Comparisons of degree, stress centrality, betweenness centrality and closeness centrality among the four clusters and six significant pathways, respectively. Pathway1 to pathway6: cell cycle, oocyte meiosis, ECM-receptor interaction, progesterone-mediated oocyte maturation, complement and coagulation cascades and focal adhesion, respectively. There were significant differences between cluster1 and cluster2, cluster3 of degree analysis (P<0.0001). Degree of cell cycle was significant with ECM-receptor interaction (P<0.05), complement and coagulation cascades (P<0.01), and focal adhesion (P<0.01). All four values of complement and coagulation cascades pathway were the lowest. There were no significant differences among the other groups apart from pathway5. The significant level was analyzed by one-way ANOVA. ^*P<0.05, ^**P<0.01 and ^***P<0.0001.

Figure 5

Six subnetworks constructed of significant (P<0.01) KEGG enrichment pathways of breast cancer. Nodes and links represent human genes and gene interactions, respectively. (A) Cell cycle; (B) Oocyte meiosis; (C) ECM-receptor interaction; (D) Progesterone-mediated oocyte maturation; (E) Focal adhesion; (F) Complement and coagulation cascades.

Figure 6

Graphical representation of the hub subnetwork composed with the significant cluster and pathways intersection. The red circle represents cell cycle, the blue circle represents oocyte meiosis, and the green circle represents cluster1. Common genes could be observed clearly between any two groups. CDK1, CCNB1, ESPL1, CCNB2, CDC20 and BUB1 as common genes among these three groups composed the hub subnetwork which was essential in the function of cell growth and death biological process.