Identification of oxidative stress-related genes and potential mechanisms in atherosclerosis

Abstract

Atherosclerosis (AS) is the main cause of death in individuals with cardiovascular and cerebrovascular diseases. A growing body of evidence suggests that oxidative stress plays an essential role in Atherosclerosis pathology. The aim of this study was to determine genetic mechanisms associated with Atherosclerosis and oxidative stress, as well as to construct a diagnostic model and to investigate its immune microenvironment. Seventeen oxidative stress-related genes were identified. A four-gene diagnostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm based on these 17 genes. The area under the Receiver Operating Characteristic (ROC) curve (AUC) was 0.967. Based on the GO analysis, cell-substrate adherens junction and focal adhesion were the most enriched terms. KEGG analysis revealed that these overlapping genes were enriched in pathways associated with Alzheimer's disease and Parkinson's disease, as well as with prion disease pathways and ribosomes. Immune cell infiltration correlation analysis showed that the immune cells with significant differences were CD4 memory activated T cells and follicular helper T cells in the GSE43292 dataset and CD4 naïve T cells and CD4 memory resting T cells in the GSE57691 dataset. We identified 17 hub genes that were closely associated with oxidative stress in AS and constructed a four-gene (aldehyde dehydrogenase six family member A1 (ALDH6A1), eukaryotic elongation factor 2 kinase (EEF2K), glutaredoxin (GLRX) and l-lactate dehydrogenase B (LDHB)) diagnostic model with good accuracy. The four-gene diagnostic model was also found to have good discriminatory efficacy for the immune cell infiltration microenvironment of AS. Overall, these findings provide valuable information and directions for future research into Atherosclerosis diagnosis and aid in the discovery of biological mechanisms underlying AS with oxidative stress.

Keywords: GEO; LASSO; atherosclerosis; immune; oxidative stress.

FIGURE 1

Analysis flow chart.

FIGURE 2

Dataset merging, standardization and normalization. (A) Box plots of the expression spectrum matrix of GSE57691 and GSE43292 data sets before calibration; (B) Box plots of the expression spectrum matrix of GSE57691 and GSE43292 data sets after calibration; (C) Density plots of the expression spectrum matrix of GSE57691 and GSE43292 data sets before calibration; (D) Density plots of the expression spectrum matrix of GSE57691 and GSE43292 data sets after calibration.

FIGURE 3

Differential expression of oxidative stress-related genes. (A,B) Volcano plot of DEGs in GSE43292 and GSE57691; (C,D) Heat map of DEGs in GSE43292 and GSE57691; (E) Venn diagram of DEGs in the two datasets.

FIGURE 4

Construction of molecular interaction network of DEGs and screening of co-expressed hub gene. (A) STRING database for 67 intersection of DEGs; (B) CytoHubba plug-in screening of top-20 hub genes; (C) Cytoscape’s MCODE analysis of hub genes; (D) Venn diagram showing the intersection of two methods to obtain 17 closely related co-expressed genes; (E) TF and miRNA prediction of the 17-gene diagnostic model, the yellow color located in the middle represents the 17-gene oxidative stress-related diagnostic model, the middle purple layer represents its associated TF; the outer blue layer represents its targeted miRNA.

FIGURE 5

Diagnostic model. (A) LASSO logistic regression algorithm screening diagnostic marker lambda value visualization; (B) LASSO logistic regression algorithm screening diagnostic marker min value visualization; (C) Forest plot of seven meaningful oxidative stress-related arterial disease risk genes with significant biological significance; (D) Column line graph of a 4-gene diagnostic marker model composed of ALDH6A1, EEF2K, GLRX, and LDHB; (E) Calibration curve of non-correlated nomogram; (F) Decision curve analysis of the diagnostic model.

FIGURE 6

Validation of the diagnostic efficacy of 4-gene diagnostic model. (A) ROC curves of the diagnostic efficacy in the GSE43292 dataset (independent index); (B) ROC curves of the diagnostic efficacy in the GSE43292 dataset (joint index); (C) ROC curves of the diagnostic efficacy in the GSE57691 dataset (independent index); (D) ROC curves of the diagnostic efficacy in the GSE57691 dataset (joint index).

FIGURE 7

Differential expression in diagnostic models. (A) Box plots of differential expression of diagnostic models in the GSE43292; (B) Box plots of differential expression of diagnostic models in the GSE57691; (C) Correlation network plots between 4-gene diagnostic models in the GSE43292; (D) Correlation network plots between 4-gene diagnostic models in the GSE57691; (E) PCA clustering plot of differential oxidative stress genes between atherosclerosis and normal groups in the GSE43292 dataset; (F) PCA clustering plots. * is less than 0.05, ** is 0.01, *** is 0.001, **** is 0.0001, and no symbol means the difference is not significant.

FIGURE 8

Molecular typing of the 4-gene diagnostic model of atherosclerosis associated with oxidative stress in GSE57691 vs. GSE43292. (A) CDF plot of consistent clustering; (B) Delta plot of consistent clustering, reflecting the optimal number of classifications; (C) Heat map of the difference between 2-classification clustering groups; (D) Fractal plot of consistent clustering samples (E,F) Consistent clustering grouping of 4-gene diagnostic model, Volcano plot and heat map.

FIGURE 9

GO/KEGG enrichment analysis of the 4-gene diagnostic marker model in GSE57691 vs. GSE43292. (A,B) Bubble plot of GO/KEGG enrichment analysis; (C,D) Bar graph of GO/KEGG enrichment analysis; (E,F) String plot of GO enrichment analysis; circle plot of GO/KEGG enrichment analysis.

FIGURE 10

GSEA enrichment analysis of 4-gene diagnostic model. (A) KEGG_JAK_STAT_SIGNALING_PATHWAY; (B) BIOCARTA_PRC2_PATHWAY; (C) BIOCARTA_BCR_PATHWAY (D) REACTOME_TP53_REGULATES_METABOLIC_GENES; (E) REACTOME_TCR_SIGNALING; (F) PID_MTOR_4PATHWAY; (G) REACTOME_PTEN_REGULATION; (H) PID_PDGFRB_PATHWAY; (I) KEGG_SPLICEOSOME; (J) KEGG_TGF_BETA_SIGNALING_PATHWAY; (K) KEGG_NOTCH_SIGNALING_PATHWAY; (L) KEGG_NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY.

FIGURE 11

Molecular typing gsva enrichment analysis of consistent clustering of oxidative stress-related genes. (A) Heat map of inter-group differences in the ssGSEA immune gene set comparison between the consistent clustering subgroups; (B) Heat map of enrichment differences in the “h.all.v7.2. symbols.gmt” set; (C) Heat map of enrichment differences in the “c2. cp.v7.2. symbols.gmt” set between the consistent clustering subgroups.

FIGURE 12

Construction of immune signature subtypes and analysis of immune cell infiltration assessment. (A) Box plots of differential immune cell infiltration between normal and atherosclerotic samples in the GSE43292 dataset by the CIBERSORT method; (B) box plots of immune cell infiltration in the GSE57691 dataset by the CIBERSORT method; (C) ssGSEA analysis box plots of the differential infiltration of immune cells in the 4-gene diagnostic model clustering subgroups among samples from the GSE43292 dataset; (D) ssGSEA analysis of the box plots of the differential infiltration of immune cells in the GSE57691 dataset.

FIGURE 13

Correlation analysis between 4-gene diagnostic models of oxidative stress-related atherosclerosis in the GSE43292 and GSE57691 datasets (A–C) ALDH6A1 and EEF2K (r = 0.614, p = 2.56E-08); EEF2K and LDHB (r = 0.697, p = 4.21E-11); GLRX and LDHB (r = 0.704, p = 2.07E-11) in the GSE57691 dataset (D) ALDH6A1 and GLRX (r = -0.453, p = 0.00017) in the GSE43292 dataset.

FIGURE 14

Scatter plot of the correlation between gene models and immune cell infiltration in the GSE57691 dataset.

FIGURE 15

Scatter plot of the correlation between gene models and immune cell infiltration in the GSE43292 dataset.