Comprehensive bioinformatics analysis reveals the crosstalk genes and immune relationship between the systemic lupus erythematosus and venous thromboembolism

Abstract

Background: It is well known that patients with systemic lupus erythematosus (SLE) had a high risk of venous thromboembolism (VTE). This study aimed to identify the crosstalk genes between SLE and VTE and explored their clinical value and molecular mechanism initially.

Methods: We downloaded microarray datasets of SLE and VTE from the Gene Expression Omnibus (GEO) dataset. Differential expression analysis was applied to identify the crosstalk genes (CGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the shared genes. The shared diagnostic biomarkers of the two diseases were further screened from CGs using least absolute shrinkage and selection operator (Lasso) regression. Two risk scores for SLE and VTE were constructed separately to predict the likelihood of illness according to the diagnostic biomarkers using a logical regression algorithm. The immune infiltration levels of SEL and VTE were estimated via the CIBERSORT algorithm and the relationship of CGs with immune cell infiltration was investigated. Finally, we explored potential phenotype subgroups in SLE and VTE based on the expression level of CGs through the consensus clustering method and studied immune cell infiltration in different subtypes.

Result: A total of 171 CGs were obtained by the intersection of differentially expressed genes (DEGs) between SLE and VTE cohorts. The functional enrichment shown these CGs were mainly related to immune pathways. After screening by lasso regression, we found that three hub CGs (RSAD2, HSP90AB1, and FPR2) were the optimal shared diagnostic biomarkers for SLE and VTE. Based on the expression level of RSAD2 and HSP90AB1, two risk prediction models for SLE and VTE were built by multifactor logistic regression and systemically validated in internal and external validation datasets. The immune infiltration results revealed that CGs were highly correlated with multiple infiltrated immunocytes. Consensus clustering was used to respectively regroup SLE and VTE patients into C1 and C2 clusters based on the CGs expression profile. The levels of immune cell infiltration and immune activation were higher in C1 than in C2 subtypes.

Conclusion: In our study, we further screen out diagnostic biomarkers from crosstalk genes SLE and VTE and built two risk scores. Our findings reveal a close relationship between CGs and the immune microenvironment of diseases. This provides clues for further exploring the common mechanism and interaction between the two diseases.

Keywords: bioinformatics analysis; immune cells infiltration; systemic lupus erythematosus; transcriptomics; unsupervised clustering; venous thromboembolism.

Figure 1

Differential expression gene analysis. (A, B) Volcano plots showed differentially expressed genes (DEGs) in GSE19151 and GSE61635. (C) Venn plots of the crosstalk genes (CGs) between GSE19151 and GSE61635. (D) The distribution characteristics of samples based on PCA results in GSE19151. (E, F) The expression pattern of CGs in GSE61635 and GSE19151.

Figure 2

Function enrichment and pathway enrichment analysis. (A, B) GO and KEGG enrichment analyses of CGs. (C, D) GSVA analyses in GSE19151 and GSE61635. (E, F) GSEA analyses base on GSE19151 and GSE61635.

Figure 3

Identification of potential shared diagnostic CGs. (A, B) 10-fold cross-validation to select the optimal tuning parameter log (lambda) in the GSE61636 and GSE19151 database. (C) Venn diagram showing the optimal diagnostic biomarkers. (D-G) The expression level of the shared diagnostic biomarkers in GSE61636, GSE19151, GSE50772 and GSE48000. (H) ROC curve of the shared diagnostic CGs in GSE61636, GSE19151, GSE50772 and GSE48000. (I) Single-factor logistic regression of the shared diagnostic CGs. Statistical significance at the level of ns ≥ 0.05, * <0.05, *** <0.001 and **** <0.0001.

Figure 4

Construction of risk scores. (A) The correlation among the shared diagnostic CGs in GSE61635. (B) The multivariate logistic regression comprising of HSP90AB1 and RSAD2. (C) Calibration curves of the VTE and SLE risk models. (D) C-index of risk models and single variables. (E) ROC curves of the two risk models. (F, G) Nomogram predicting the probability of VTE and SLE. The left template for SLE and the right for VTE.

Figure 5

External verification of VTE and SLE risk scores. (A, B) The accuracy, precision, recall, and f-measure of VTE and SLE risk models in GSE4800 and GSE50772. (C, D) Calibration curves of the VTE and SLE risk scores in the GSE4800 and GSE50772. (E, F) ROC curves of the two risk models in the GSE4800 and GSE50772.

Figure 6

PPI network and gene expression validation analysis. (A) PPI network of the CGs. (B) The top 5 CGs ordered by the MCC, EPC, MNC and Degree algorithm. (C-F) The expression level of hub CGs in GSE19151, GSE48000, GSE61635, and GSE50772. Statistical significance at the level of ns ≥ 0.05, ** <0.01, *** <0.001 and **** <0.0001.

Figure 7

The immune infiltration landscape of SLE and VTE. (A, B) Relative immune cell abundance based on CIBERSORT between disease and normal groups in GSE61635 and GSE19151. (C, D) The heatmaps showed the correlation of hub CGs with immune cell infiltration. Statistical significance at the level of ns ≥ 0.05, * <0.05, ** <0.01, *** <0.001 and **** <0.0001.

Figure 8

Consensus clustering. (A) Consensus CDF when k = 2-5 and Relative alterations in the area under CDF curve based on GSE61635. (B) Consensus matrix heatmap of SLE cohorts when k = 2. (C) The heatmaps of CGs expression between C1 and C2 clusters of SLE subtypes. (D) Consensus CDF when k = 2-5 and relative alterations in the area under CDF curve based on GSE19151. (E) Consensus matrix heatmap of VTE cohorts when k = 2. (F) The heatmaps of CGs expression between C1 and C2 clusters of VTE subtypes.

Figure 9

Correlation of two CGs subtypes and immune cell infiltration. (A, B) The immune cell distribution in C1 and C2 of SLE CGs subtypes based on CIBERSORT and MCPcounter. (C, B) The immune cell distribution in C1 and C2 of VTE CGs subtypes based on CIBERSORT and MCPcounter. (E, F) GSVA showed the pathways with significantly different distribution in C1 and C2 of SLE and VTE subtypes. Statistical significance at the level of ns ≥ 0.05, * <0.05, ** <0.01, *** <0.001 and **** <0.0001.