2.3. GO Enrichment and KEGG Pathway Analysis of Module Genes

Genes within the identified modules of interest were extracted from the network and functional enrichment was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) webserver (accessed on 26 July 2022), which interprets and correlates the function of various genes based on previous literature [22]. Specifically, functional annotation was performed using the Gene Ontology (GO) database, a repository of the basic functions and characteristics of genes and their protein products, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to infer information regarding the biological, genomic, chemical, and systemic functions of various genes [23,24]. Only those genes present with functional significance and those present within the databases were included for the analysis. Results with p < 0.05 were considered to be statistically significant when screening for GO terms and KEGG pathways.

The constructed gene co-expression network was visualized using Cytoscape [25]. Hub genes within the identified modules were identified through the inter-connectivity value. Briefly, for each identified module, the module eigengene based connectivity (kME) was calculated using the “intramodular Connectivity” function from the WGCNA package [8]. Genes with the highest intra-modular connectivity values were considered to the hub genes.

Additionally, to account for non-protein coding genes, the genes of each preserved module were passed through the STRING database to construct a protein-protein interaction (PPI) network. The resulting PPI network was imported into Cytoscape and the hub genes were selected using cytoHubba. Degree, Maximum Neighborhood Component (MNC), Radiality Centrality, Stress Centrality, and Closeness Centrality were the algorithms used to evaluate the identified hub genes. Consistent genes were taken to be hub genes. The top three hub genes were taken for further downstream analysis.