Bioinformatics analysis and reveal potential crosstalk genetic and immune relationships between atherosclerosis and periodontitis

Abstract

Periodontitis is an inflammatory and immune-related disease with links to several systemic diseases, and the pathological process of atherosclerosis also involves inflammatory and immune involvement. The aim of this study was to investigate the common immune cells and potential crosstalk genes between periodontitis (PD) and atherosclerosis (AS). By analyzing the weighted gene co-expression network of differentially immune infiltrating cells in two diseases to obtain important module genes, and taking the intersection of the module genes, we obtained 14 co-expressed immune-related genes, and evaluated the predictive value of 14 immune-related genes using three machine learning models.Two potential immune-related crosstalk genes (BTK and ITGAL) were finally obtained by taking intersections of WGCNA intersection genes, DEGs and IRGs.Then, the diagnostic column line graphs were constructed based on the 2 crosstalk genes, and the calibration curves, DCA curves and clinical impact curves indicated that the two genes had strong disease prediction ability, and we further validated the accuracy of the two potential crosstalk genes for disease diagnosis in the validation dataset.Single gene GSEA analysis showed that both genes are jointly involved in biological processes such as antigen presentation and immune regulation, and single sample GSEA analysis showed that macrophages and T cells play an important role in periodontitis in atherosclerosis.This study explored the genetic correlation between atherosclerosis and periodontitis using bioinformatics tools. BTK and ITGAL were found to be the most important crosstalk genes between the two diseases and may have an important role in the diagnosis and treatment of the diseases. Macrophage and T cell mediated inflammatory and immune responses may play an important role in periodontitis and atherosclerosis.

Figure 1

Analysis of immune infiltration in atherosclerosis and periodontitis.Barplot (A) and vioplot (C) show the distribution of 22 immune cells in atherosclerotic samples. Barplot (B) and vioplot (D) show the distribution of 22 immune cells in periodontitis samples. Con control; Treat diseases; P < 0.05;**P < 0.01; ***P < 0.001.

Figure 2

Weighted gene co-expression network analysis based on differential immune infiltration cells. Cluster dendrogram (A) and heatmap (C) show the correlation between modules and immune cells in atherosclerotic disease. Cluster dendrogram (B) and heatmap (D) show the correlation between modules and immune cells in periodontitis disease.

Figure 3

Using machine learning to build models of disease diagnosis. In the atherosclerotic sample, (A) boxplot of sample residuals. (B) Significance of variables in the RF, GLM, and SVM models. (C) Significance of variables in the GLM model. In the periodontitis sample, (D) boxplot of sample residuals. (E) Significance of variables in RF, GLM, and SVM models. (F) Significance of variables in the SVM model.

Figure 4

Acquisition of differentially expressed genes. (A) Heatmap showing the top 50 DEGs expressed in atherosclerotic samples. (B) Heatmap showing the top 50 DEGs expressed in periodontitis samples. (C) Volcano plot showing DEGs in atherosclerotic samples. (D) Volcano plot showing DEGs in periodontitis samples. (E) Venn shows intersecting genes of the WGCNA module in atherosclerosis and periodontitis. (F) Venn shows 2 core genes common to WGCNA and DEGs and IRGs. Con control; Treat diseases; DEG differentially expressed gene; WGCNA weighted gene co-expression network analysis; IRGs immune-related genes.

Figure 5

Construction and validation of a nomogram model for atherosclerosis diagnosis. (A) Nomogram of diagnostic biomarkers for the diagnosis of atherogenesis. (B) Evaluation of the predictive ability of the column line graph model by calibration curves. (C) Evaluation of the clinical application value of the columnar line graph model using DCA curves. (D) Clinical impact curves of the nomogram.

Figure 6

Construction and validation of a nomogram model for periodontitis diagnosis. (A) Nomogram of diagnostic biomarkers for the diagnosis of atherogenesis. (B) Evaluation of the predictive ability of the column line graph model by calibration curves. (C) Evaluation of the clinical application value of the columnar line graph model using DCA curves. (D) Clinical impact curves of the nomogram.

Figure 7

Expression pattern validation and diagnostic value. (A) Expression of BTK and ITGAL in GSE43292. (B) Expression of BTK and ITGAL in GSE10334. (C) ROC curve of the shared diagnostic genes in GSE100927. (D) ROC curve of the shared diagnostic genes in GSE16134. (E) ROC curve of the shared diagnostic genes in GSE43292. (F) ROC curve of the shared diagnostic genes in GSE10334. Con control, Treat diseases.