2.4. Network Construction and Hub Gene Identification

The Cytoscape [44] plugin GeneMANIA [45] v3.5.2 (https://apps.cytoscape.org/apps/genemania) (date accessed: 6 September 2021) was used to construct an interaction network of the DEGs. Cytoscape is an open-source platform-independent network visualization software. It offers several plugins/apps for various network analyses. GeneMANIA allows users to construct a weighted composite gene–gene functional interaction network from a gene list. Functional interactions between the 47 highly correlated common DEGs were predicted by GeneMANIA. In addition to the DEGs, 20 additional genes were used to create the interaction network using Homo sapiens as a source species. The functional associations in the network were evaluated using the following terms: co-expression, co-localization, genetic interactions, pathways, physical interactions, predicted interactions, and shared protein domains.

Networks can be used to display a wide range of biological data, including protein–protein interactions, gene regulation, cellular pathways, and signal transductions [10,46]. An interaction network is represented as a graph G=(V, E), where V and E are the sets of vertices (nodes/genes/proteins) and edges (links/functional associations/interactions), respectively [47]. Most biological networks have a scale-free topology and therefore are more robust than random networks. Scale-free networks have a power-law degree distribution, with a small number of highly connected nodes (“hubs”) and a large number of poorly connected nodes (“non-hubs”). Hubs play a significant role in the functional and modular architecture of interactomes. As a result, they are assumed to be more vital to life than non-hub nodes, according to the centrality-lethality rule [48]

The Cytoscape plugin cytoHubba [49] (https://apps.cytoscape.org/apps/cytohubba) (date accessed: 6 September 2021) was used to calculate the topological parameters of the network. CytoHubba offers 11 topological analysis methods, including six centrality measures. We selected degree centrality (DC), betweenness centrality (BC), bottleneck (BN), and closeness centrality (CC), in order to identify key/important nodes in the network. DC of a node u∊V(G) is defined as the number of its first neighbors. Nodes with high degrees are referred to as “hubs” [47]. BC is a measure of the number of non-redundant shortest paths that pass through a given node. Nodes with high BC are defined as “bottlenecks”, as these nodes act as bridges/connecting links between dense clusters; they control the information flow among other nodes in the network. The BC of a node u∊V(G) is computed as follows:

where σst is the total number of shortest paths from node ‘s’ to node ‘t’, and σst(u) is the number of those paths passing through u [47]. Both hubs and bottlenecks tend to be essential in protein interaction networks [48,50].

CC is defined as a measure of how fast the flow of information would be from a given node to all other nodes in a network, sequentially. Nodes with high CC are the closest to all other nodes in a network. The CC of a node u∊V(G) is computed as follows:

where duv is the distance (length of the shortest path) between nodes u and v, and N is the number of nodes in G [47].