Analyzing the pathogenesis of systemic lupus erythematosus complicated by atherosclerosis using transcriptome data

Exploring the association between rosacea and acne by integrated bioinformatics analysis

Clinically, rosacea occurs frequently in acne patients, which hints the existence of shared signals. However, the connection between the pathophysiology of rosacea and acne are not yet fully understood. This study aims to unveil molecular mechanism in the pathogenesis of rosacea and acne. We identified differentially expressed genes (DEGs) by limma and weighted gene co-expression network analysis and screened hub genes by constructing a protein-protein interaction network. The hub genes were verified in different datasets. Then, we performed a correlation analysis between the hub genes and the pathways. Finally, we predicted and verified transcription factors of hub genes, performed the immune cell infiltration analysis using CIBERSORT, and calculated the correlation between hub genes and immune cells. A total of 169 common DEGs were identified, which were mainly enriched in immune-related pathways. Finally, hub genes were identified as IL1B, PTPRC, CXCL8, MMP9, CCL4, CXCL10, CD163, CCR5, CXCR4, and TLR8. 9 transcription factors that regulated the expression of hub genes were identified. The infiltration of γδT cells was significantly increased in rosacea and acne lesions and positively linked with almost all hub genes. These identified hub genes and immune cells may play a crucial role in the development of rosacea and acne.

Immune-Related Genes in the Pathogenesis of Atherosclerosis: Based on Sex Differences

Purpose

Atherosclerosis is still a global public problem with increasing incidence rate and mortality. It has been found that gender factors play an important role in the progression of atherosclerosis. However, few people explore gender related atherosclerosis at the level of genes and immune cells. The purpose of this study was to determine genetic and immune cell differences between male and female samples.

Patients and Methods

This study aims to identify differential genes between male and female samples in the GSE43292 dataset. The focus will be on identifying immune-related genes (IRGs) among these differentially expressed genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis will be employed to explore the enrichment of IRGs in biological processes, molecular functions, cellular components, and pathways. Furthermore, a protein-protein interaction (PPI) network for the IRGs will be constructed using Cytoscape software. To estimate the degree of immune cell infiltration, single-sample gene set enrichment analysis (ssGSEA) will be conducted. Moreover, the identified IRGs will be validated using GSE28829 dataset. Finally, we validated in atherosclerotic mice.

Results

Seven IRGs (CCL13, IL1RN, FPR2, S100A8, CCL19, CXCL1, CXCL8) were identified as being overexpressed in male atherosclerosis. GO and KEGG analysis revealed that these IRGs are primarily enriched in inflammatory response pathways, cytokine signaling pathways, and cytokine- cytokine receptor interactions. Notably, when compared to females, there was a significant infiltration of immune cells in male specimens. Importantly, all seven IRGs demonstrated high diagnostic value in GSE28829 dataset. The use of animal samples supports our results.

Conclusion

This study demonstrates the effectiveness of seven IRGs and reveal sex differences in atherosclerosis. Notably, there is a significant presence of immune cells within the atherosclerotic plaque of men compared to women. These findings have potential implications for the development of personalized treatment approaches targeting gender-related atherosclerosis.

Integrating the characteristic genes of macrophage pseudotime analysis in single-cell RNA-seq to construct a prediction model of atherosclerosis

BACKGROUND

Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of macrophage phenotype transformation.

METHOD

Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSORT, ESTIMATE, support vector machine (SVM), random forest (RF), and weighted correlation network analysis (WGCNA) were applied to bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO).

RESULT

Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages and M2 macrophages can be transformed into M1 macrophages. The ROC curve values of the six genes in the test group were statistically significant (AUC (IL1RN): 0.899, 95% CI: 0.764-0.990; AUC (NRP1): 0.817, 95% CI: 0.620-0.971; AUC (TAGLN): 0.846, 95% CI: 0.678-0.971; AUC (SPARCL1): 0.825, 95% CI: 0.620-0.988; AUC (EMP2): 0.808, 95% CI: 0.630-0.947; AUC (ACTA2): 0.784, 95% CI: 0.591-0.938). The atherosclerosis prediction model showed significant statistical significance in both the train group (AUC: 0.909, 95% CI: 0.842-0.967) and the test group (AUC: 0.812, 95% CI: 0.630-0.966).

CONCLUSIONS

IL1RNHigh M1, NRP1High M2, ACTA2High M2/M1, EMP2High M1/M1, SPACL1High M2/M1 and TAGLNHigh M2/M1 macrophages play key roles in the occurrence and development of arterial atherosclerosis. These marker genes of macrophage phenotypic transformation can also be used to establish a model to predict the occurrence of atherosclerosis.