Polymorphism in the chemokine receptor 7 gene (CCR7) is associated with previous myocardial infarction in patients undergoing elective coronary angiography

Identification of Biomarkers Related to Immune Cell Infiltration with Gene Coexpression Network in Myocardial Infarction

Background

There is evidence that the immune system plays a key critical role in the pathogenesis of myocardial infarction (MI). However, the exact mechanisms associated with immunity have not been systematically uncovered.

Methods

This study used the weighted gene coexpression network analysis (WGCNA) and the CIBERSORT algorithm to analyze the MI expression data from the Gene Expression Omnibus database and then identify the module associated with immune cell infiltration. In addition, we built the coexpression network and protein-protein interactions network analysis to identify the hub genes. Furthermore, the relationship between hub genes and NK cell resting was validated by using another dataset GSE123342. Finally, receiver operating characteristic (ROC) curve analyses were used to assess the diagnostic value of verified hub genes.

Results

Monocytes and neutrophils were markedly increased, and T cell CD8, T cell CD4 naive, T cell CD4 memory resting, and NK cell resting were significantly decreased in MI groups compared with stable coronary artery disease (CAD) groups. The WGCNA results showed that the pink model had the highest correlation with the NK cell resting infiltration level. We identified 11 hub genes whose expression correlated to the NK cell resting infiltration level, among which, 7 hub genes (NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES) were successfully validated in GSE123342. And these 7 genes had diagnostic value to distinguish MI and stable CAD.

Conclusions

NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES may be a diagnostic biomarker and therapeutic target associated with NK cell resting infiltration in MI.

Recent Advances in GPCR-Regulated Leukocyte Responses during Acute Cardiac Injury

Following acute cardiac injury such as myocardial infarction (MI), the controlled activation and recruitment of various leukocytes to the site of tissue damage significantly impacts chronic changes to cardiac structure and function, and ultimately host survival. While recent research has focused primarily on how leukocytes respond to injury, understanding how to effectively modulate their responsiveness to dampen maladaptive inflammation and promote repair processes is not yet fully understood. The complex spatio-temporal migration and activation of leukocytes are largely controlled by various chemokines and their cognate receptors, belonging to the G protein-coupled receptor (GPCR) family. Beyond chemokine receptors, leukocytes express a host of additional GPCRs that have recently been shown to regulate their responsiveness to cardiac injury. In this minireview, we will briefly discuss the impact of chemokine receptors on leukocyte behaviour, with subsequent focus on the most recent advancements in understanding the impact and therapeutic potential of other GPCR classes on leukocyte responses after acute cardiac injury.

Immune Response-Related Genes - STAT4, IL8RA and CCR7 Polymorphisms in Lung Cancer: A Case-Control Study in China

Purpose

This study aimed to evaluate the associations between immune response-related genes – STAT4, IL8RA and CCR7 polymorphisms and risk of lung cancer.

Methods

Seven polymorphisms of STAT4, IL8RA and CCR7 were genotyped in 350 cases and 350 controls using a MassARRAY platform.

Results

The STAT4 rs1400656-G and rs7574865-T alleles may decrease the susceptibility to lung cancer (p_rs1400656= 0.020; p_rs7574865= 0.014); while IL8RA rs1008562-C and CCR7 rs3136685-T alleles may increase the risk of disease (p_rs1008562< 0.001; p_rs3136685= 0.018). The STAT4 rs1400656-GA and rs7574865-GT genotypes were determined as protective genotypes against lung cancer risk (p_rs1400656= 0.048; p_rs7574865= 0.042). However, IL8RA rs1008562-CG/GG and CCR7 rs3136685-TT genotypes were significantly associated with an elevated risk of disease (p_rs1008562< 0.0001; p_rs3136685= 0.020). Genetic model analysis revealed that STAT4 rs1400656 and rs7574865 were relate to a declining risk of disease under dominant and log-additive models (rs1400656: p_dominant = 0.014, p_log-additive= 0.016; rs7574865: p_dominant = 0.013, p_log-additive= 0.013). In contrast, IL8RA rs1008562 exhibited a strong correlation with an elevated risk of lung cancer under all three models (p_dominant < 0.0001, p_recessive = 0.011, p_log-additive< 0.0001). Moreover, CCR7 rs3136685 was correlated with an increased risk of disease under recessive and log-additive models (p_recessive = 0.007, p_log-additive= 0.019); and CCR7 rs17708087 was also identified as a risk factor in the dominant model (p = 0.038).

Conclusion

These results widen the scope of knowledge about the association between STAT4, IL8RA and CCR7 polymorphisms and risk of lung cancer.

Weighed Gene Coexpression Network Analysis Screens the Potential Long Noncoding RNAs and Genes Associated with Progression of Coronary Artery Disease

Background

Coronary artery disease (CAD) is a type of heart disease with a high morbidity rate. This study is aimed at identifying potential biomarkers closely related to the progression of CAD.

Materials and Methods

A microarray dataset of GSE59867 was downloaded from a public database, Gene Expression Omnibus, which included 46 cases of stable CAD without a history of myocardial infarction (MI), 30 cases of MI without heart failure (HF), and 34 cases of MI with HF. Differentially expressed long noncoding RNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified by the limma package, and functions of DEmRNAs were annotated by Gene Ontology and KEGG pathways. In addition, weighed gene coexpression network analysis (WGCNA) was used to construct a coexpression network of DEmRNAs, and a disease-related lncRNAs-mRNAs-pathway network was constructed. Finally, the datasets of GSE61145 and GSE57338 were used to verify the expression levels of the above highly correlated candidates.

Results

A total of 2362 upregulated mRNAs and 2816 downregulated mRNAs, as well as 235 upregulated lncRNAs and 113 downregulated lncRNAs were screened. These genes were significantly enriched in “cytokine-cytokine receptor interaction,” “RIG-I-like receptor signaling pathway,” and “natural killer cell-mediated cytotoxicity.” Five modules including 1201 DEmRNAs were enriched in WGCNA. A coexpression network including 19 DElncRNAs and 413 DEmRNAs was constructed. These genes were significantly enriched in “phosphatidylinositol signaling system,” “insulin signaling pathway,” and “MAPK signaling pathway”. Disease-related gene-pathway network suggested FASN in “insulin signaling pathway,” DGKZ in “phosphatidylinositol signaling system,” and TNFRSF1A in “MAPK signaling pathway” were involved in MI.

Conclusion

FASN, DGKZ, and TNFRSF1A were revealed to be CAD progression-associated genes by WGCNA coexpression network analysis.

Key genes associated with non-alcoholic fatty liver disease and acute myocardial infarction

Background

With increasing research on non-alcoholic fatty liver (NAFLD) and acute myocardial infarction (AMI), many studies show a tight correlation between NAFLD and AMI, but the underlying pathophysiology is still not clear. This study was performed to identify the potential hub genes and pathways related to these 2 diseases by using the bioinformatics method.

Material/Methods

The Gene Expression Omnibus (GEO) dataset GSE63067 of NAFLD patients and normal controls was downloaded from the GEO database. The GSE60993 and GSE66360 datasets for AMI patients and healthy controls were also obtained. Differentially expressed genes (DEGs) of NAFLD and AMI datasets and the common genes between them were obtained. Further GO and KEGG enrichment analyses for common genes were performed. To define the pathogenesis associated with both NAFLD and AMI, a protein–protein interaction (PPI) network was constructed. Finally, SPSS software was utilized to analyze the diagnostic value of hub genes in the NAFLD and AMI datasets, respectively.

Results

Seventy-eight common genes were obtained in NAFLD and AMI with the threshold of P-value <0.05. Thirty-one GO terms and 10 KEGG pathways were obtained. Also, the top 10 hub genes (TLR2, LILRB2, CXCL1, FPR1, TLR4, TYROBP, MMP9, FCER1G, CLEC4D, and CCR2) were selected with P<0.05.

Conclusions

The results of this study suggest that some novel genes play an important role in the occurrence and progression NAFLD and AMI. More experimental research and clinical trials are needed to verify our results.

Identification of potentially relevant genes for myocardial infarction using RNA sequencing data analysis

Myocardial infarction (MI) is a heart disease with high morbidity and mortality rates, thus it is critical to identify genes that serve roles during its pathogenesis. The objective of the present study was to identify potentially relevant genes during the progression of the disease. Blood samples from patients with MI and normal controls (n=3/group) were obtained, the RNA was extracted and cDNA libraries were established. RNA sequencing (RNA-seq) was performed on a HiSeq 2500 platform and fragments per kilobase of exon per million fragments mapped was utilized to calculate the gene expression value following preprocessing of the RNA-seq data. Electronic validation of several identified differentially expressed genes (DEGs) was performed on a Gene Expression Omnibus (GEO) dataset GSE59867 (390 cases and 46 healthy controls). Functional enrichment and protein-protein interaction network analysis was conducted for DEGs. A total of 977 DEGs, including 817 upregulated and 160 downregulated genes were identified in patients with MI. These DEGs were significantly enriched for ‘positive regulation of the immune system process,’ ‘inflammatory response,’ ‘regulation of I-kappaB-kinase/NF-kappaB signaling’ and ‘TNF signaling pathway’. A protein-protein interaction network of the top 40 DEGs was used to identify high degree genes, including interferon induced protein with tetratricopeptide repeats 3 (IFIT3), MX dynamin like GTPase 1 (MX1), major histocompatibility complex, class II, DQ α1 (HLA-DQA1), RAR related orphan receptor A (RORA), prostaglandin D2 synthase (PTGDS), cysteine rich protein 2 (CRIP2), collagen type VI α 2 chain (COL6A2) and S100 calcium binding protein P (S100P). The results of validation in the GEO dataset were consistent with the sequencing analysis. A total of eight genes, including IFIT3, MX1, HLA-DQA1, RORA, PTGDS, CRIP2, COL6A2 and S100P may therefore be considered as potentially relevant genes in the pathology of MI.