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pmid:36819522

Bioinformatics analysis of differentially expressed genes in diabetic foot ulcer and preliminary experimental verification.

Fang Miao · Xixi Li · Chenglin Wang · Heju Yuan · Zhongming Wu

Annals of translational medicine · 2023 doi:10.21037/atm-22-6437

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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title
Bioinformatics analysis of differentially expressed genes in diabetic foot ulcer and preliminary experimental verification.
doi
10.21037/atm-22-6437
pmid
36819522
authors array · 5 items
[0]
Fang Miao
[1]
Xixi Li
[2]
Chenglin Wang
[3]
Heju Yuan
[4]
Zhongming Wu
journal
Annals of translational medicine
year
2023
abstract
Background: Molecular changes are closely related to the pathogenesis and healing process of diabetic foot ulcers (DFUs), and are crucial for the early prediction and intervention of DFU. Methods: Bioinformatics analysis was performed in this study to identify the key differentially expressed genes (DEGs) in DFU, analyze their functions and function modes, and conduct preliminary experimental verification to determine the potential pivotal genes in the pathogenesis of DFU. Two datasets, GSE68183 and GSE80178, were obtained from the Gene Expression Omnibus (GEO). DEGs were obtained using GEO2R. Six co-expressed DEGs (co-DEGs) were obtained by R language analysis. The co-DEGs were constructed by using STRING and Cytoscape 3.7.2 to construct a protein-protein interaction (PPI) network, and two hub genes, NHLRC3 and BNIP3, were identified. The BNIP3 gene was selected for further analysis. Co-DEGs were used for Gene Ontology (GO) function analysis using the WebGestalt database, and BNIP3-related biological processes focused on mitochondrial protein decomposition. GO function analysis of the BNIP3 gene and its interacting genes was carried out using the cluster profile package and org.hs.eg. db package of the R language and its biological process was enriched in the cell response to external stimuli and autophagy. Results: BNIP3 and its interacting genes were retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and KEGG pathway enrichment analysis was performed using the WebGestalt database. The results showed that BNIP3 was significantly correlated with mitochondrial autophagy and the FoxO signaling pathway. The miRDB and TargetScan databases were used to identify the relevant microRNAs (miRNAs) regulating the BNIP3 gene, and it was found that miRNA-182 may be involved in the targeted regulation of BNIP3. Western blot analysis was performed to determine the abnormal expression of BNIP3. Conclusions: Our study found that the BNIP3 gene may be a new biomarker and intervention target for DFU.
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Comparative genomic, microRNA, and tissue analyses reveal subtle differences between non-diabetic and diabetic foot skin [gene expression]
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Genomic Profiling of Diabetic Foot Ulcers Identifies miR-15b-5p as a Major Regulator that Leads to Suboptimal Inflammatory Response and Diminished DNA Repair Mechanisms
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Complete file
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Two datasets, GSE68183 and GSE80178, were obtained from the Gene Expression Omnibus (GEO). DEGs were obtained using GEO2R. Six co-expressed DEGs (co-DEGs) were obtained by R language analysis. The co-DEGs were constructed by using STRING and Cytoscape 3.7.2 to construct a protein-protein interaction (PPI) network, and two hub genes, NHLRC3 and BNIP3, were identified. The BNIP3 gene was selected for further analysis. Co-DEGs were used for Gene Ontology (GO) function analysis using the WebGestalt database, and BNIP3-related biological processes focused on mitochondrial protein decomposition. GO function analysis of the BNIP3 gene and its interacting genes was carried out using the cluster profile package and org.hs.eg. db package of the R language and its biological process was enriched in the cell response to external stimuli and autophagy.\nResults: BNIP3 and its interacting genes were retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and KEGG pathway enrichment analysis was performed using the WebGestalt database. The results showed that BNIP3 was significantly correlated with mitochondrial autophagy and the FoxO signaling pathway. The miRDB and TargetScan databases were used to identify the relevant microRNAs (miRNAs) regulating the BNIP3 gene, and it was found that miRNA-182 may be involved in the targeted regulation of BNIP3. 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