Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets

Abstract

Background: Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN.

Methods: After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network.

Results: The identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, "immune system", "extracellular matrix organization", "hemostasis", "signal transduction", and "platelet activation" to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network.

Conclusion: By performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

Keywords: Biología de sistemas; Diabetic nephropathy; Diana terapéutica; Meta-analysis; Metaanálisis; Nefropatía diabética; Systems biology; Therapeutic target; Transcriptoma; Transcriptome.