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. 2022 May 9;22(1):119.
doi: 10.1186/s12902-022-01038-y.

Identification of miRNA-mRNA-TF regulatory networks in peripheral blood mononuclear cells of type 1 diabetes

Wanqiu Wang  1 , Huan Wang  1 , Yuhong Liu  2 , Liu Yang  3
Affiliations

Identification of miRNA-mRNA-TF regulatory networks in peripheral blood mononuclear cells of type 1 diabetes

Wanqiu Wang et al. BMC Endocr Disord. .

Abstract

Background: Type 1 diabetes (T1D) is a T lymphocyte-mediated and B lymphocyte-assisted autoimmune disease. We aimed to identify abnormally expressed genes in peripheral blood mononuclear cells (PBMCs) of T1D and explore their possible molecular regulatory network.

Methods: Expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Then, the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) were identified, and functional enrichment and immune cell infiltration analysis were performed. The starBase, miRTarBase, TarBase, JASPAR, ENCODE, and TRRUST databases constructed the miRNA-mRNA-TF regulatory network. The ROC curves were plotted to evaluate the sensitivity and specificity of miRNAs and mRNAs.

Result: A total of 216 DEGs directly or indirectly related to type I diabetes mellitus, natural killer cell-mediated cytotoxicity, Th1, and Th2 cell differentiation, and the IL-17 and TNF signaling pathways were obtained. The miRNA-mRNA-TF network indicates that miR-320a and SOX5 are the only miRNAs and TFs that both target ADM and RRAGD. The ROC curves showed that ADM (0.9375), RRAGD (0.8958), and hsa-mir-320a (0.9417) had high accuracy in T1D diagnosis.

Conclusion: The constructed regulatory networks, including miR-320a/ADM/SOX5 and miR-320a/RRAGD/SOX5, may provide new insight into the mechanisms of development and progression in T1D.

Keywords: Differentially expressed genes (DEGs); MicroRNAs; Regulatory networks; Transcription factors (TFs); Type 1 diabetes (T1D).

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The volcano map and heatmap. A, B The top 10 up- and downregulated DEGs in GSE55098. C, D The top 10 up- and downregulated DEmiRNAs in GSE55099. E, F The top 10 up- and downregulated DEGs in GSE33440
Fig. 2
Fig. 2
Immune cell infiltration and functional enrichment analysis. A The expression of multiple immune cell types between the T1D and no-T1D groups in GSE55098. B Bar graph of GO enrichment analysis. C Bar graph of KEGG pathway enrichment analysis. GO Gene Ontology, KEGG Kyoto Encyclopedia of Genes and Genomes. "N" and "T" means No-T1D and T1D patients respectively
Fig. 3
Fig. 3
Correlation analysis between immune cell abundance and hub genes. A ADM. B FFAR2. C ID3. D OGFRL1. E RRAGD. F TNF. G HLA-DQA1. H Venn diagram of seven hub genes in the GSE33440 and GSE55098 datasets
Fig. 4
Fig. 4
The receiver operating characteristic (ROC) curve and related area under the curve (AUC) value of seven hub genes and hsa-miR-320a
Fig. 5
Fig. 5
The predicted miRNA-mRNA-TF target regulatory network. A The miRNA-mRNA target regulatory network. B The TF-mRNA target regulatory network. C The correlation heatmap of seven hub gens
See this image and copyright information in PMC

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