Dataset Information

Identification of hub genes, pathways, and related transcription factors in systemic lupus erythematosus: A preliminary bioinformatics analysis.

ABSTRACT:

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multiple organ damage and the production of a variety of autoantibodies. The pathogenesis of SLE has not been fully defined, and it is difficult to treat. Our study aimed to identify candidate genes that may be used as biomarkers for the screening, diagnosis, and treatment of SLE.

Methods

We used the GEO2R tool to identify the differentially expressed genes (DEGs) in SLE-related datasets retrieved from the Gene Expression Omnibus (GEO). In addition, we also identified the biological functions of the DEGs by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Additionally, we constructed protein-protein interaction (PPI) networks to identify hub genes, as well as the regulatory network of transcription factors related to DEGs.

Results

Two datasets were identified for use from the GEO (GSE50772, GSE4588), and 34 up-regulated genes and 4 down-regulated genes were identified by GEO2R. Pathway analysis of the DEGs revealed enrichment of the interferon alpha/beta signaling pathway; GO analysis was mainly enriched in response to interferon alpha, regulation of ribonuclease activity. PPIs were constructed through the STRING database and 14 hub genes were selected and 1 significant module (score = 12.923) was obtained from the PPI network. Additionally, 11 key transcription factors that interacted closely with the 14 hub DEGs were identified from the gene transcription factor network.

Conclusions

Bioinformatic analysis is an effective tool for screening the original genomic data in the GEO database, and a large number of SLE-related DEGs were identified. The identified hub DEGs may be potential biomarkers of SLE.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8238284 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:We analyzed gene expression in peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLE) using public databases. The goal was to identify lupus biomarkers by determining whether differentially expressed genes are mediated by methylation, miRNA, or SNP. Two cDNA microarrays were subjected to integration analysis, and we calculated the mutually differentially expressed genes (|log2fold change (FC)| > 1, P < 0.05). These genes were analyzed using gene otology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. The differences in methylation sites for two methylation chips were calculated and the differentially methylated sites were annotated. These genes were compared to the differentially expressed genes. We obtained 135 differentially expressed microRNAs from the microRNA-chip results using PBMCs from SLE and healthy individuals. Predictive microRNA target genes were identified using GO, KEGG pathways, and PPI networks. The target genes identified were compared to the differentially expressed genes. We downloaded Chinese SLE genome-wide association study data from SLE-related literature, analyzed the loci with a P value < 0.05, and used annotated SLE-associated SNPs. We selected the genes corresponding to an SNP located on an exon and determined the intersection with the differentially expressed genes. We found 18 differentially expressed genes in both cDNA microarrays. The methylation chips had 50 corresponding methylation sites. On the basis of these results, we identified two genes, IFI44 and IFI44L. We further identified 135 differentially expressed microRNAs predicted to affect 5766 target genes. Two identified genes were in common with the differentially expressed genes. Finally, SNP annotated genes and cDNA chip genes overlap with identified MX1. Therefore, we used existing data to analyze the causes of differential gene expression in SLE, introducing new methods for determining biomarkers and therapeutic targets.