Difference of immune cell infiltration between stable and unstable carotid artery atherosclerosis

Jia, Guanqing; Shi, Licheng; Gu, Jianhua; Zhang, Dandan; Wang, Wenjun; Zhu, Xuanfeng; Liu, Jiannan

doi:10.1111/jcmm.17018

Cited by 20 publications

(20 citation statements)

References 40 publications

(77 reference statements)

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“…Previous studies have believed that atherosclerosis is caused by lipid deposition in the blood vessel wall, and as the study progresses [ 8 ]. It is gradually discovered that it is a chronic inflammatory disease of the arterial wall dominated by the aortic and middle arteries that is regulated by complex immune responses [ 9 ]. This chronic inflammatory response plays a key role in all stages of the atherosclerosis process, including atherosclerosis formation, progression, and plaque rupture eventually lead to thickening of the arterial wall lining, lumen narrowing, and even occlusion [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of immune-related biomarkers and construction of regulatory network in patients with atherosclerosis

Dong¹,

Jiang²,

Tian

et al. 2022

BMC Med Genomics

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Background More and more evidence has established the crucial roles of the innate and adaptive immune systems in driving atherosclerosis-associated chronic inflammation in arterial blood vessels. Thus, the goal of this research was to determine immune-related biomarkers in atherosclerosis. Methods In this study, we conducted analysis on the mRNA expression profile of atherosclerosis obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) between atherosclerosis and control samples and immune-related genes (IRGs) were intersected to obtain differentially expressed immune-related genes (DEIRGs). The protein–protein interaction (PPI) network was created by STRING database and hub genes were identified by the MCODE plug-in. Furthermore, the receiver operating characteristic (ROC) curve was executed to verify the diagnostic value of the hub genes, and microRNA (miRNA)-gene-transcription factor (TF) regulatory networks were used to explain the regulatory mechanism of hub genes in atherosclerosis. Finally, qRT-PCR was performed to identify the mRNA levels of the target genes. Results A total of 199 overlapping genes were screened out as DEIRGs by intersecting the DEGs and IRGs. Then, 6 hub genes with high diagnostic value (IFIH1, IFIT1, IFIT2, IFIT3, ISG15 and OAS3) were identified via PPI network and ROC curve. Finally, miRNA-gene-TF networks revealed the regulatory mechanism of diagnostic genes.We used the carotid artery of AS patients and normal human carotid artery plaque samples for qRT-PCR verification, and the results showed that the hub gene had the same trend. Conclusion Our study identified IFIH1, IFIT1, IFIT2, IFIT3, ISG15 and OAS3 as immune-related hub genes of atherosclerosis. These genes may serve as potential therapeutic targets for atherosclerosis patients.

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Section: Introductionmentioning

confidence: 99%

Identification of immune-related biomarkers and construction of regulatory network in patients with atherosclerosis

Dong¹,

Jiang²,

Tian

et al. 2022

BMC Med Genomics

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“…Previous studies have shown that inflammatory macrophages and foam cell formation are crucial factors in atherosclerotic plague progression ( 44 , 45 ). T cells target the vessel wall in line with macrophages and react to antigens in the arterial wall ( 46 ). After activation of T cells, proinflammatory mediators are produced, exaggerating the inflammatory response, and disease development is worsened ( 47 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes and their correlation with immune infiltration in coronary artery disease through bioinformatics and machine learning methods

Huang¹,

Zheng²,

Li³

et al. 2022

J Thorac Dis

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Background: Coronary artery disease (CAD) is a multifactorial disease and its pathogenesis remains unclear. We aimed to explore the optimal feature genes (OFGs) for CAD and to investigate the function of immune cell infiltration of CAD. It will be helpful for better understanding of the pathogenesis and the development of genetic prediction of CAD.Methods: Datasets related to CAD were obtained from the Gene Expression Omnibus (GEO) database.Cases from the datasets met diagnostic criteria including clinical symptoms, electrocardiographic (ECG) and angiographic evidence. We identified differentially expressed genes (DEGs) and conducted functional enrichment analysis. OFGs were obtained from the least absolute shrinkage and selection operator (LASSO) algorithm, support vector machine recursive feature elimination (SVM-RFE) algorithm, and random forest (RF) algorithm. CIBERSORT was used to compare immune infiltration between CAD patients and normal controls, and the correlation between OFGs and immune cells was analyzed.Results: DEGs were involved in the interleukin (IL)-17 signaling pathway, nuclear factor (NF)-kappa B signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Gene Ontology (GO) analysis revealed DEGs were enriched in lipopolysaccharide (LPS), tertiary granule, and pattern recognition receptor activity. Disease Ontology (DO) analysis suggested DEGs were enriched in lung disease, arteriosclerotic cardiovascular disease (CVD). Matrix metalloproteinase 9 (MMP9), Pellino E3 ubiquitin protein ligase 1 (PELI1), thrombomodulin (THBD), and zinc finger protein 36 (ZFP36) were screened by the intersection of OFGs obtained from LASSO, SVM-REF, and RF algorithms. CAD patients had a lower proportion of memory B cells (P=0.019), CD8 T cells (P<0.001), resting memory CD4 T cells (P<0.001), regulatory T cells (P=0.028), and gamma delta T cells (P<0.001) than normal controls, while the proportion of activated memory CD4 T cells (P=0.014), resting natural killer (NK) cells (P<0.001), monocytes (P<0.001), M0 macrophages (P=0.023), activated mast cells (P<0.001), and neutrophils (P<0.001) in CAD patients were higher than normal controls. MMP9, PELI1, THBD, and ZFP36 were correlated with immune cells.Conclusions: MMP9, PELI1, THBD, and ZFP36 may be predicted biomarkers for CAD. The OFGs and association between OFGs and immune infiltration may provide potential biomarkers for CAD prediction along with the better assessment of the disease.

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“…Proportion of Infiltrating Immune Cells. The CIBER-SORT was a widely used method to assess the composition of immune cells in tumor microenvironment [20]. In our study, the CIBERSORT algorithm was used to evaluate the infiltration level of 22 immune cells in each BC sample.…”

Section: Survival Analysismentioning

confidence: 99%

Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer

Wang

Guo

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Hypoxia was considered to be a prognostic indicator in a variety of solid tumors. This study aims at identifying the hypoxia-related genes (HRGs) in breast cancer (BC) and the feasibility of HRGs as a prognostic indicator. Methods. We downloaded the mRNA expression data of BC patients from TCGA and GEO databases. The LASSO Cox regression analysis was applied to screen the hub HRGs to establish a prognostic Risk Score. The independence of Risk Score was assessed by multivariate Cox regression analysis. And the immune checkpoint analysis was also performed. In addition, we also detected the expression level of hub HRGs in MCF-10A cells, MCF-7 cells, and SK-BR-3 cells by RT-qPCR. Results. Three HRGs were identified as hub genes with prognostic value in BC, including CA9, PGK1, and SDC1. The Risk Score constructed by these three genes could efficiently distinguish the prognosis of different BC patients and has been shown to be an independent prognostic indicator. In the high-risk group, patients had lower overall survival and poorer prognosis. In addition, the expression levels of five immune checkpoints (PD1, CTLA4, TIGIT, LAG3, and TIM3) in the high-risk group were significantly higher than those in the low-risk group. Moreover, the expression levels of PGK1 and SDC1 in BC cells were significantly increased. Conclusion. In this study, we established an efficiently model based on three optimal HRGs (CA9, PGK1, and SDC1) could clearly distinguish the prognosis of different BC patients.

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Difference of immune cell infiltration between stable and unstable carotid artery atherosclerosis

Cited by 20 publications

References 40 publications

Identification of immune-related biomarkers and construction of regulatory network in patients with atherosclerosis

Identification of immune-related biomarkers and construction of regulatory network in patients with atherosclerosis

Identification of hub genes and their correlation with immune infiltration in coronary artery disease through bioinformatics and machine learning methods

Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer

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