Identification of PPARG as key gene to link coronary atherosclerosis disease and rheumatoid arthritis via microarray data analysis

Abstract

Background: Inflammation is the common pathogenesis of coronary atherosclerosis disease (CAD) and rheumatoid arthritis (RA). Although it is established that RA increases the risk of CAD, the underlining mechanism remained indefinite. This study seeks to explore the molecular mechanisms of RA linked CAD and identify potential target gene for early prediction of CAD in RA patients.

Materials and methods: The study utilized five raw datasets: GSE55235, GSE55457, GSE12021 for RA patients, and GSE42148 and GSE20680 for CAD patients. Gene Set Enrichment Analysis (GSEA) was used to investigate common signaling pathways associated with RA and CAD. Then, weighted gene co-expression network analysis (WGCNA) was performed on RA and CAD training datasets to identify gene modules related to single-sample GSEA (ssGSEA) scores. Overlapping module genes and differentially expressed genes (DEGs) were considered as co-susceptible genes for both diseases. Three hub genes were screened using a protein-protein interaction (PPI) network analysis via Cytoscape plug-ins. The signaling pathways, immune infiltration, and transcription factors associated with these hub genes were analyzed to explore the underlying mechanism connecting both diseases. Immunohistochemistry and qRT-PCR were conducted to validate the expression of the key candidate gene, PPARG, in macrophages of synovial tissue and arterial walls from RA and CAD patients.

Results: The study found that Fc-gamma receptor-mediated endocytosis is a common signaling pathway for both RA and CAD. A total of 25 genes were screened by WGCNA and DEGs, which are involved in inflammation-related ligand-receptor interactions, cytoskeleton, and endocytosis signaling pathways. The principal component analysis(PCA) and support vector machine (SVM) and receiver-operator characteristic (ROC) analysis demonstrate that 25 DEGs can effectively distinguish RA and CAD groups from normal groups. Three hub genes TUBB2A, FKBP5, and PPARG were further identified by the Cytoscape software. Both FKBP5 and PPARG were downregulated in synovial tissue of RA and upregulated in the peripheral blood of CAD patients and differential mRNAexpreesion between normal and disease groups in both diseases were validated by qRT-PCR.Association of PPARG with monocyte was demonstrated across both training and validation datasets in CAD. PPARG expression is observed in control synovial epithelial cells and foamy macrophages of arterial walls, but was decreased in synovial epithelium of RA patients. Its expression in foamy macrophages of atherosclerotic vascular walls exhibits a positive correlation (r = 0.6276, p = 0.0002) with CD68.

Conclusion: Our findings suggest that PPARG may serve as a potentially predictive marker for CAD in RA patients, which provides new insights into the molecular mechanism underling RA linked CAD.

Fig 1. Data analysis flowchart.

Schematic flowchart of data acquirement, processing, analysis, and validation.

Fig 2. GSEA analysis in the RA and CAD training datasets.

A-B Multi-GSEA plot showing 16 significantly differential pathways for CAD and 20 RA significantly differential pathways; C Venn diagram showing intersection of CAD and RA related pathways; D ssGSEA score of allograft rejection and Fc gamma receptor mediated phagocytosis displayed in boxplots.

Fig 3. Differential expression analysis of CAD and RA datasets.

A-B Volcano plots of DEGs in RA and CAD training datasets; C-F Bean plots of the 25 common DEGs in CAD and RA training and validation datasets.

Fig 4. WGCNA analysis for CAD and RA training datasets.

A and D A soft threshold determination plot was generated for CAD and RA training datasets, with an optimal power of 10 for CAD and 7 for RA. B and E WGCNA module identification and clustering dendrogram of DEGs in CAD and RA training datasets. C and F The module -trait correlation heatmap in WGCNA of CAD and RA training datasets. Each row represents a gene module, while each column represents ssGSEA score of a significantly differential pathway. The number within the heatmap indicates the correlation coefficient and p values. G Venn plot shows the intersection of module genes of WGCNA in CAD and RA datasets.

Fig 5. PCA and SVM analysis for 25 core genes in RA and CAD datasets.

A-D PCA scatter plot between normal and disease groups for training and validation datasets of RA and CAD. E-H ROC curves and their corresponding AUCs for SVM methods.

Fig 6. Biological function analysis, PPI network and hub genes of common DEGs in CAD and RA training datasets.

A-D GO analysis and KEGG pathway enrichment analysis of the 25 DEGs. E PPI network construction of the 17 DEGs. F Venn plot showing the intersection of the three hub genes identified by four plug-ins of cytoscape. G-H qRT-PCR analysis of mRNA expression of TUBB2A, FKBP5, and PPARG in RA and CAD tissues with their matched normal tissues as control. Each sample was repeated three times and the results are expressed as mean ± SD. (ns, non-significant, *p < 0.05, **p < 0.01, ***p < 0.001. Student’s t-test).

Fig 7. Assessment of diagnostic efficacy of three hub genes.

A-B The ROC curves of the training and validation cohorts of CAD; C-D The ROC curves of the training and validation cohorts of RA; E Gene-gene interaction network construction for TUBBB2A, FKBP5, and PPARG by GeneMANIA.

Fig 8. Correlation analysis, immune cell infiltration, and transcription network construction of PPARG.

A-D Lollipop diagrams showing the relationship of PPARG expression and 22 immune infiltration scores in RA and CAD datasets. E Transcription factor enrichment analysis by ChEA3 database and PPI network construction for TUBB2A, PPARG, and FKBP5. F Cytoscape analysis of first-order interactions involving PPARG relevant transcription factors.

Fig 9. Immunohistochemistry analysis of PPARG and CD68 expression on tissue samples from patients of CAD/RA.

A-C In the control group, there is little expression of CD68 and PPARG on the arterial wall; D-F Both CD68 and PPARG were observed to be co-expressed in the foamy histiocytes of CAD patients with strong brown-yellow cytoplasmic staining. H.E. staining showed cholesterol crystals observed in the upper left corner; G The percentage of positive areas for CD68 and PPARG were analyzed by Image J and presented with simple linear regression. H-I PPARG exhibited mild staining intensity in control synovial epithelium and little expression in the sub-synovial interstitial cells. J-K The synovial epithelium of RA patients exhibited significant infiltration of lymphocytes and plasma cells in the sub-synovial region with little expression of PPARG. Bar = 50 um, IHC and H.E. magnification, 400×; L Positive relationship of PPARG and FKBPF in CAD datasets GSE42148.