Molecular signatures in diabetic foot ulcer by integrated gene expression profiling via bioinformatics analysis

Abstract

Introduction and Aim: Diabetic foot ulcers (DFUs) are a common and debilitating diabetic consequence leading to lower-limb amputations, long-term disability, and reduced lifespan. There is a lack of clinical diagnosis expertise because of no adequate diagnostic signs for DFU. As a result, the current research aims to find out how differently expressed genes (DEGs) affect the DFU.
 Materials and Methods: Bioinformatics analysis was used to evaluate DEGs using the GSE132187 dataset of the NCBI-GEO database, which contained samples from three hyperglycemic and three normoglycemic macrophage-like cell lines. Following the discovery of DEGs, Gene Ontology (GO) and KEGG pathway enrichment analysis were used to investigate how genes are classified into preset bins based on their functional properties. To discover hub DEGs in DFU, a protein-protein interaction (PPI) network was built and five topological parameters such as degree, stress, Closeness centrality, betweenness centrality, and radiality were evaluated.
 Results: We found 547 DEGs using the GSE132187 dataset, comprising 79 upregulated DEGs and 468 downregulated DEGs. There were 434 nodes and 1724 edges in the PPI network. The giant network uncovered six modules that are significantly enriched in biological processes like positive JNK cascade regulation, positive interferon-gamma production regulation, negative cell proliferation regulation, cellular response to zinc ion, cellular response to lipopolysaccharide, wound healing, and inflammatory response.
 Conclusion: Bioinformatics analysis revealed the major differentially expressed hub-genes implicated in DFUs. These findings suggested that these genes could be used as a DFU prognostic, diagnostic, or therapeutic targets.