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Identification of genes and signaling pathways associated with diabetic neuropathy using a weighted correlation network analysis

View Article: PubMed Central - PubMed

ABSTRACT

Background:: The molecular mechanisms behind diabetic neuropathy remains to be investigated.

Methods:: This is a secondary study on microarray dataset (GSE24290) downloaded from Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI), which included 18 nerve tissue samples of progressing diabetic neuropathy (fibers loss ≥500 fibers/mm2) and 17 nerve tissue samples of nonprogressing diabetic neuropathy (fibers loss ≤100 fibers/mm2). Differentially expressed genes (DEGs) were screened between progressing and nonprogressing diabetic neuropathy. With the DEGs obtained, a weighted gene coexpression network analysis was conducted to identify gene clusters associated with diabetic neuropathy. Diabetes-related microRNAs (miRNAs) and their target genes were predicted and mapped to the genes in the gene clusters identified. Consequently, a miRNA–gene network was constructed, for which gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed. Potential drugs for treatment of diabetic neuropathy were also predicted.

Results:: Total 370 upregulated and 379 downregulated DEGs were screened between nonprogressing and progressing diabetic neuropathy. Has-miR-377, has-miR-216a, and has-miR-217 were associated with diabetes. Inflammation was the most significant GO term. The peroxisome proliferator-activated receptor (PPAR) pathway and the adenosine monophosphate (AMP)-activated protein kinase (AMPK) signaling pathway were significantly KEGG pathways significantly enriched with PPAR gamma (PPARG), stearoyl-CoA desaturase (SCD), cluster of differentiation 36 (CD36), and phosphoenolpyruvate carboxykinase 1 (PCK1).

Conclusion:: The study suggests that PPARG, SCD, CD36, PCK1, AMPK pathway, and PPAR pathway may be involved in progression of diabetic neuropathy.

No MeSH data available.


Module significance of different modules. Different modules are marked in different colors.
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Figure 3: Module significance of different modules. Different modules are marked in different colors.

Mentions: In order to guarantee the scale-free topology of the gene coexpression network, power = 6 was selected when the correlation coefficient between log (k) and log P (k) reached 0.9 for the first time (Fig. 1). The constructed gene clustering tree (cut height = 0.9) was shown in Fig. 2. Different modules were marked in different colors. All modules were with MS value >0.8 and MS P value <0.05. According to the MS value, top 3 modules ranked in descending order were selected, consisting of the blue module (156 DEGs), the brown module (55 DEGs), and the green module (47 DEGs) for further analysis (Fig. 3).


Identification of genes and signaling pathways associated with diabetic neuropathy using a weighted correlation network analysis
Module significance of different modules. Different modules are marked in different colors.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5134881&req=5

Figure 3: Module significance of different modules. Different modules are marked in different colors.
Mentions: In order to guarantee the scale-free topology of the gene coexpression network, power = 6 was selected when the correlation coefficient between log (k) and log P (k) reached 0.9 for the first time (Fig. 1). The constructed gene clustering tree (cut height = 0.9) was shown in Fig. 2. Different modules were marked in different colors. All modules were with MS value >0.8 and MS P value <0.05. According to the MS value, top 3 modules ranked in descending order were selected, consisting of the blue module (156 DEGs), the brown module (55 DEGs), and the green module (47 DEGs) for further analysis (Fig. 3).

View Article: PubMed Central - PubMed

ABSTRACT

Background:: The molecular mechanisms behind diabetic neuropathy remains to be investigated.

Methods:: This is a secondary study on microarray dataset (GSE24290) downloaded from Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI), which included 18 nerve tissue samples of progressing diabetic neuropathy (fibers loss &ge;500 fibers/mm2) and 17 nerve tissue samples of nonprogressing diabetic neuropathy (fibers loss &le;100 fibers/mm2). Differentially expressed genes (DEGs) were screened between progressing and nonprogressing diabetic neuropathy. With the DEGs obtained, a weighted gene coexpression network analysis was conducted to identify gene clusters associated with diabetic neuropathy. Diabetes-related microRNAs (miRNAs) and their target genes were predicted and mapped to the genes in the gene clusters identified. Consequently, a miRNA&ndash;gene network was constructed, for which gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed. Potential drugs for treatment of diabetic neuropathy were also predicted.

Results:: Total 370 upregulated and 379 downregulated DEGs were screened between nonprogressing and progressing diabetic neuropathy. Has-miR-377, has-miR-216a, and has-miR-217 were associated with diabetes. Inflammation was the most significant GO term. The peroxisome proliferator-activated receptor (PPAR) pathway and the adenosine monophosphate (AMP)-activated protein kinase (AMPK) signaling pathway were significantly KEGG pathways significantly enriched with PPAR gamma (PPARG), stearoyl-CoA desaturase (SCD), cluster of differentiation 36 (CD36), and phosphoenolpyruvate carboxykinase 1 (PCK1).

Conclusion:: The study suggests that PPARG, SCD, CD36, PCK1, AMPK pathway, and PPAR pathway may be involved in progression of diabetic neuropathy.

No MeSH data available.