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. 2021 Jan 19;19(1):35.
doi: 10.1186/s12967-020-02698-x.

Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

Xingwang Zhao #  1 , Longlong Zhang #  2   3 , Juan Wang  1 , Min Zhang  1 , Zhiqiang Song  1 , Bing Ni  4 , Yi You  5
Affiliations

Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

Xingwang Zhao et al. J Transl Med. .

Erratum in

Abstract

Background: Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved.

Methods: The expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein-protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis.

Results: In total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co-expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE.

Conclusion: IFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE.

Keywords: Biomarkers; IFI27; Immune infiltration; Integrated bioinformatics; Systemic lupus erythematosus.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Gene expression, correlation and enrichment analysis, showing the significant function related to DEGs. a DEGs were identified from GSE81622 (SLE), GSE81622 (Lupus nephritis), GSE4588(CD4 T Cell), GSE4588(B Cell), GSE50772 and GSE144390 gene expression profiling datasets based on |fold change|≥ 1 and adjusted p value < 0.05. The six datasets share 6 overlapping DEGs. b Heatmap of DEGs derived from integrated analysis. Each circle represents one dataset and each sector represents one gene, the gradual color ranged from white to red represents the changing process of up-regulation. Up-regulated genes were marked in red, respectively. c GO term enrichment analysis of module genes. d Top 5 terms of KEGG analysis in biological pathway category (Ranged by p value). e Corrgrams were derived based on pearson value between DEGs, respectively. f Corrgrams were derived based on pearson value between six datasets, respectively. DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes
Fig. 2
Fig. 2
The volcano plot illustrates DEGs. The volcano plot illustrates DEGs between control and SLE after analysis of the a GSE81622(SLE), b GSE50772, c GSE144390, d GSE81622 (Lupus nephritis), e GSE4588(CD4 T Cell), f GSE4588(B Cell) dataset with GEO2R. DEGs, differentially expressed genes
Fig. 3
Fig. 3
Sankey diagram for the ceRNA network about DEGs. a CirRNAs-miRNAs-mRNAs network, b LncRNAs-miRNAs-mRNAs network, each rectangle represents a gene, and the connection degree of each gene is visualized based on the size of the rectangle
Fig. 4
Fig. 4
Detailed information relating to changes in the biological function of DEGs in datasets through the enrichment analyses. a Heatmap of enriched terms across input gene expressed matrix of six datasets, via the Metascape. b Network of enriched terms colored by cluster identity, where nodes that share the same cluster identity are typically close to each other. c Network of enriched terms and genes colored by datases, where terms containing more genes tend to have a more significant. d The gene–gene interaction network for DEGs was analyzed using the GeneMANIA database. The 20 most frequently changed neighboring genes are shown. Each node represents a gene. The node color represents the possible functions of the respective gene
Fig. 5
Fig. 5
Principal components analyses performed on all datasets. a GSE4588 (B Cell), b GSE4588 (CD4T Cell). c GSE50772, (d) GSE81622, e GSE144390 datasets. Principal component 1 (PC1) and principal component 2 (PC2) are used as the X-axis and Y-axis, respectively, to draw the scatter diagram, where each point represents a sample. The farther the two samples are from each other, the greater the difference is between the two samples in gene expression patterns
Fig. 6
Fig. 6
Gene set enrichment analysis (GSEA) was used to analyze the signaling pathways enrichment in different groups. GSEA used to validate the gene signatures of IFI27 in a GSE81622 (SLE), b GSE81622 (LN), c GSE50772, d GSE4588 (B cell), e GSE4588 (CD4 T cell), including response to type I interferon signaling and the protesome KEGG pathway. Normalized enrichment score (NES) indicated the analysis results across gene sets. False discovery rate (FDR) presented if a set was significantly enriched
Fig. 7
Fig. 7
Identification of weighted gene co‑expression network modules associated with SLE in four datasets. a GSE4588(B Cell), b GSE4588(CD4 T Cell), c GSE81622, d GSE50772, The eigengene adjacency heatmap of the correlation between module genes and groups of SLE and control. Every color represents one co-expression module. IFI27 in the module of chiefly enriched in correlated with the occurence of SLE
Fig. 8
Fig. 8
Violin diagram of the proportion of 22 types of immune cells. a GSE4588(CD4 T Cell), b GSE50772, showed the difference in infiltration between the two groups
Fig. 9
Fig. 9
Violin diagram of the proportion of 22 types of immune cells. a GSE81622(LN), b GSE81622(SLE), showed the difference in infiltration between the two groups
Fig. 10
Fig. 10
Correlation heat map of 22 types of immune cells. a GSE4588(B Cell), b GSE4588(CD4 T Cell), c GSE81622(LN), d GSE81622(SLE). The size of the colored squares represents the strength of the correlation. Red represents a positive correlation, blue represents a negative correlation. The darker the color, the stronger the correlation
Fig. 11
Fig. 11
Principal components analyses performed on all samples of five datasets. The first two principal components which explain the most of the data variation are shown, a GSE4588(B Cell), b GSE4588(CD4 T Cell), c GSE81622(SLE), d GSE81622(LN), e GSE50772. This was indicative of the difference between the immune phenotypes of the groups
Fig. 12
Fig. 12
The diagnostic performance of the sixgenes. The diagnostic performance of the calculated based on the six genes expression in SLE diagnosis in training a GSE50772, b GSE81622 and two validation datasets, respectively. A AUC > 0.9 indicated that the model had a good fitting effect. ROC, receiver operating characteristic; AUC, area under the ROC curve
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