Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning

JNK Pathway-Associated Phosphatase Deficiency Facilitates Atherosclerotic Progression by Inducing T-Helper 1 and 17 Polarization and Inflammation in an ERK- and NF-κB Pathway-Dependent Manner

AIM

JNK pathway-associated phosphatase (JKAP) regulates T cell-mediated immunity and inflammation, which are involved in atherosclerosis pathogenesis. This study investigated the effects of JKAP on T-helper (Th) cell polarization, inflammation, and atherosclerotic progression.

METHODS

Serum JKAP levels were measured in 30 patients with coronary heart disease (CHD) and 30 controls. CHD blood naïve CD4＋ T cells were acquired, followed by JKAP overexpression and knockdown with or without treatment with PD98059 (ERK inhibitor) or BAY-11-7082 (NF-κB inhibitor) in vitro. CD4＋ T-cell conditional JKAP ablation mice were established in vivo, followed by the construction of an atherosclerosis model.

RESULTS

JKAP was reduced and negatively correlated with the Gensini score, CRP, Th1 cells, Th17 cells, and proinflammatory cytokines in patients with CHD. In vitro, JKAP overexpression suppressed Th1 and Th17 cell differentiation and proinflammatory cytokines, whereas JKAP knockdown exerted the opposite effect; however, JKAP modification did not affect Th2 cell differentiation. Interestingly, JKAP negatively regulated the ERK and NF-κB pathways; meanwhile, the PD98059 and BAY-11-7082 treatments repressed Th1 and Th17 cell differentiation, and attenuated the effect of JKAP knockdown on these indices. In vivo, conditional CD4＋ T-cell JKAP ablation increased Th1 and Th17 cell polarization in the spleen, lymph node, blood, and/or aortic root. Furthermore, CD4＋ T-cell conditional JKAP ablation exaggerated atherosclerotic lesions in the aorta, elevated CD4+ cell infiltration and proinflammatory cytokines in the aortic root, and activated the ERK and NF-κB pathways in the aortic root.

CONCLUSION

JKAP ablation facilitates atherosclerosis progression by promoting Th1 and 17 polarization and inflammation through regulation of the ERK and NF-κB pathways.

Genetic associations in ankylosing spondylitis: circulating proteins as drug targets and biomarkers

Background

Ankylosing spondylitis (AS) is a complex condition with a significant genetic component. This study explored circulating proteins as potential genetic drug targets or biomarkers to prevent AS, addressing the need for innovative and safe treatments.

Methods

We analyzed extensive data from protein quantitative trait loci (pQTLs) with up to 1,949 instrumental variables (IVs) and selected the top single-nucleotide polymorphism (SNP) associated with AS risk. Utilizing a two-sample Mendelian randomization (MR) approach, we assessed the causal relationships between identified proteins and AS risk. Colocalization analysis, functional enrichment, and construction of protein-protein interaction networks further supported these findings. We utilized phenome-wide MR (phenMR) analysis for broader validation and repurposing of drugs targeting these proteins. The Drug-Gene Interaction database (DGIdb) was employed to corroborate drug associations with potential therapeutic targets. Additionally, molecular docking (MD) techniques were applied to evaluate the interaction between target protein and four potential AS drugs identified from the DGIdb.

Results

Our analysis identified 1,654 plasma proteins linked to AS, with 868 up-regulated and 786 down-regulated. 18 proteins (AGER, AIF1, ATF6B, C4A, CFB, CLIC1, COL11A2, ERAP1, HLA-DQA2, HSPA1L, IL23R, LILRB3, MAPK14, MICA, MICB, MPIG6B, TNXB, and VARS1) that show promise as therapeutic targets for AS or biomarkers, especially MAPK14, supported by evidence of colocalization. PhenMR analysis linked these proteins to AS and other diseases, while DGIdb analysis identified potential drugs related to MAPK14. MD analysis indicated strong binding affinities between MAPK14 and four potential AS drugs, suggesting effective target-drug interactions.

Conclusion

This study underscores the utility of MR analysis in AS research for identifying biomarkers and therapeutic drug targets. The involvement of Th17 cell differentiation-related proteins in AS pathogenesis is particularly notable. Clinical validation and further investigation are essential for future applications.

Curcumin Equipped Nanozyme-Like Metal-Organic Framework Platform for the Targeted Atherosclerosis Treatment with Lipid Regulation and Enhanced Magnetic Resonance Imaging Capability

Atherosclerotic cardiovascular disease (ASCVD) has become the leading cause of death worldwide, and early diagnosis and treatment of atherosclerosis (AS) are crucial for reducing the occurrence of acute cardiovascular events. However, early diagnosis of AS is challenging, and oral anti-AS drugs suffer from limitations like imprecise targeting and low bioavailability. To overcome the aforementioned shortcomings, Cur/MOF@DS is developed, a nanoplatform integrating diagnosis and treatment by loading curcumin (Cur) into metal-organic frameworks with nanozymes and magnetic resonance imaging (MRI) properties. In addition, the surface-modification of dextran sulfate (DS) enables PCN-222(Mn) effectively target scavenger receptor class A in macrophages or foam cells within the plaque region. This nanoplatform employs mechanisms that effectively scavenge excessive reactive oxygen species in the plaque microenvironment, promote macrophage autophagy and regulate macrophage polarization to realize lipid regulation. In vivo and in vitro experiments confirm that this nanoplatform has outstanding MRI performance and anti-AS effects, which may provide a new option for early diagnosis and treatment of AS.

Screening and validation of atherosclerosis PAN-apoptotic immune-related genes based on single-cell sequencing

Background

Carotid atherosclerosis (CAS) is a complication of atherosclerosis (AS). PAN-optosome is an inflammatory programmed cell death pathway event regulated by the PAN-optosome complex. CAS's PAN-optosome-related genes (PORGs) have yet to be studied. Hence, screening the PAN-optosome-related diagnostic genes for treating CAS was vital.

Methods

We introduced transcriptome data to screen out differentially expressed genes (DEGs) in CAS. Subsequently, WGCNA analysis was utilized to mine module genes about PANoptosis score. We performed differential expression analysis (CAS samples vs. standard samples) to obtain CAS-related differentially expressed genes at the single-cell level. Venn diagram was executed to identify PAN-optosome-related differential genes (POR-DEGs) associated with CAS. Further, LASSO regression and RF algorithm were implemented to were executed to build a diagnostic model. We additionally performed immune infiltration and gene set enrichment analysis (GSEA) based on diagnostic genes. We verified the accuracy of the model genes by single-cell nuclear sequencing and RT-qPCR validation of clinical samples, as well as in vitro cellular experiments.

Results

We identified 785 DEGs associated with CAS. Then, 4296 module genes about PANoptosis score were obtained. We obtained the 7365 and 1631 CAS-related DEGs at the single-cell level, respectively. 67 POR-DEGs were retained Venn diagram. Subsequently, 4 PAN-optosome-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) were identified via machine learning. Cellular function tests on four genes showed that these genes have essential roles in maintaining arterial cell viability and resisting cellular senescence.

Conclusion

We obtained four PANoptosis-related diagnostic genes (CNTN4, FILIP1, PHGDH, and TFPI2) associated with CAS, laying a theoretical foundation for treating CAS.

Exploring MiR-484 Regulation by Polyalthia longifolia: A Promising Biomarker and Therapeutic Target in Cervical Cancer through Integrated Bioinformatics and an In Vitro Analysis

BACKGROUND

MiR-484, implicated in various carcinomas, holds promise as a prognostic marker, yet its relevance to cervical cancer (CC) remains unclear. Our prior study demonstrated the Polyalthia longifolia downregulation of miR-484, inhibiting HeLa cells. This study investigates miR-484's potential as a biomarker and therapeutic target in CC through integrated bioinformatics and an in vitro analysis.

METHODS

MiR-484 levels were analyzed across cancers, including CC, from The Cancer Genome Atlas. The limma R package identified differentially expressed genes (DEGs) between high- and low-miR-484 CC cohorts. We assessed biological functions, tumor microenvironment (TME), immunotherapy, stemness, hypoxia, RNA methylation, and chemosensitivity differences. Prognostic genes relevant to miR-484 were identified through Cox regression and Kaplan-Meier analyses, and a prognostic model was captured via multivariate Cox regression. Single-cell RNA sequencing determined cell populations related to prognostic genes. qRT-PCR validated key genes, and the miR-484 effect on CC proliferation was assessed via an MTT assay.

RESULTS

MiR-484 was upregulated in most tumors, including CC, with DEGs enriched in skin development, PI3K signaling, and immune processes. High miR-484 expression correlated with specific immune cell infiltration, hypoxia, and drug sensitivity. Prognostic genes identified were predominantly epidermal and stratified patients with CC into risk groups, with the low-risk group showing enhanced survival and immunotherapeutic responses. qRT-PCR confirmed FGFR3 upregulation in CC cells, and an miR-484 mimic reversed the P. longifolia inhibitory effect on HeLa proliferation.

CONCLUSION

MiR-484 plays a crucial role in the CC progression and prognosis, suggesting its potential as a biomarker for targeted therapy.

Enrichment of type 1 innate lymphoid cells in the course of human atherosclerotic plaque development suggests contribution to atherogenesis

Introduction

Innate lymphoid cells (ILCs) have been implicated in multiple pathologic conditions, including atherogenesis, as documented in experimental mice studies, however, their role in atherosclerosis in humans remains unexplored.

Methods

Here, we identify ILCs and their dynamics in early, advanced, and complicated human carotid- and aortic atherosclerotic plaques, using a multiplex immunohistochemical quadruple-staining technique with prototypic transcription factors T-bet, GATA3, or RORgt for identification of the ILC1, ILC2 and ILC3 subsets, respectively, in combination with lineage markers CD3, CD20/ CD79a and CD56 to exclude other lymphoid cell types. ILC subsets were quantified, and to put this in perspective, their numbers were expressed as percentage of the total number of infiltrated lymphoid cells and related to the frequency of conventional T cells, B cells, NK cells, and NKT cells.

Results

All ILC subsets were present in every different stage of atherogenesis. ILC1s were the most abundant ILC subset, and their numbers significantly increased in the course of plaque development, but paradoxically, their relative frequency was reduced because of a higher increment of T cells and B cells. The numbers of ILC2s and ILC3s also gradually increased, but this trend did not achieve significance. T cell subsets always significantly outnumbered their ILC counterparts, except for the early lesions where the proportion of ILC1s was markedly higher, albeit not significant.

Discussion

The high abundance of ILC1s in the early stages and further significant enrichment in later stages, suggest they may participate in the initiation and development of atherogenesis, and thus, may represent a novel target to prevent or treat atherosclerosis.

Prior Exposure to Experimental Preeclampsia Increases Atherosclerotic Plaque Inflammation in Atherogenic Mice-Brief Report

BACKGROUND

Women with a history of preeclampsia have evidence of premature atherosclerosis and increased risk of myocardial infarction and stroke compared with women who had a normotensive pregnancy. Whether this is due to common risk factors or a direct impact of prior preeclampsia exposure has never been tested in a mouse atherosclerosis model.

METHODS

Pregnant LDLR-KO (low-density lipoprotein receptor knockout; n=35) female mice were randomized in midgestation to sFlt1 (soluble fms-like tyrosine kinase 1)-expressing adenovirus or identical control adenovirus. Postpartum, mice were fed high-fat diet for 8 weeks to induce atherogenesis. Comparison between the control and preeclampsia models was made for metabolic parameters, atherosclerosis burden and composition by histology, plaque inflammation by flow cytometry, and aortic cytokines and inflammatory markers using a cytokine array.

RESULTS

In pregnant LDLR-KO mice, sFlt1 adenovirus significantly induced serum sFlt1, blood pressure, renal endotheliosis, and decreased pup viability. After 8 weeks of postpartum high fat feeding, body weight, fasting glucose, plasma cholesterol, HDL (high-density lipoprotein), and LDL (low-density lipoprotein) were not significantly different between groups with no change in aortic root plaque size, lipid content, or necrotic core area. Flow cytometry demonstrated significantly increased CD45+ aortic arch leukocytes and CD3+T cells and aortic lysate contained more CCL (CC motif chemokine ligand) 22 and fetuin A and decreased expression of IGFBP6 (insulin-like growth factor-binding protein 6) and CCL21 in preeclampsia-exposed mice compared with controls.

CONCLUSIONS

In atherogenic LDLR-KO mice, exposure to sFlt1-induced preeclampsia during pregnancy increases future atherosclerotic plaque inflammation, supporting the concept that preeclampsia directly exacerbates atherosclerotic inflammation independent of preexisting risk factors. This mechanism may contribute to ischemic vascular disease in women after preeclampsia pregnancy.

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

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.

Identification and validation of hub genes involved in foam cell formation and atherosclerosis development via bioinformatics

Background

Foam cells play crucial roles in all phases of atherosclerosis. However, until now, the specific mechanisms by which these foam cells contribute to atherosclerosis remain unclear. We aimed to identify novel foam cell biomarkers and interventional targets for atherosclerosis, characterizing their potential mechanisms in the progression of atherosclerosis.

Methods

Microarray data of atherosclerosis and foam cells were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expression genes (DEGs) were screened using the "LIMMA" package in R software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were both carried out. Hub genes were found in Cytoscape after a protein-protein interaction (PPI) enrichment analysis was carried out. Validation of important genes in the GSE41571 dataset, cellular assays, and tissue samples.

Results

A total of 407 DEGs in atherosclerosis and 219 DEGs in foam cells were identified, and the DEGs in atherosclerosis were mainly involved in cell proliferation and differentiation. CSF1R and PLAUR were identified as common hub genes and validated in GSE41571. In addition, we also found that the expression of CSF1R and PLAUR gradually increased with the accumulation of lipids and disease progression in cell and tissue experiments.

Conclusion

CSF1R and PLAUR are key hub genes of foam cells and may play an important role in the biological process of atherosclerosis. These results advance our understanding of the mechanism behind atherosclerosis and potential therapeutic targets for future development.

Development and Validation of a Propionate Metabolism-Related Gene Signature for Prognostic Prediction of Hepatocellular Carcinoma

Background

Studies have demonstrated that propionate metabolism-related genes (PMRGs) are associated with cancer progression. PMRGs are not known to be involved in Hepatocellular carcinoma (HCC).

Methods

In this study, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were accessed for HCC-related transcriptome data and clinical information. First, DE-PMRGs were derived by intersecting PMRGs and DEGs between HCC tissues and normal controls. The clusterProfiler R package was then used to enrich DE-PMRGs. In addition, biomarkers of HCC were identified, and a prognostic model was developed. Using functional analysis and tumor microenvironment analysis, new insights were obtained into HCC. The expression of biomarkers was validated using quantitative real-time polymerase chain reaction (qRT-PCR).

Results

132 DE-PMRGs were obtained by intersecting 3690 DEGs and 291 PMRGs. Steroid and organic acid metabolism were associated with these genes. For the construction of the risk model for HCC samples, five biomarkers were identified, including Acyl-CoA dehydrogenase short chain (ACADS), CYP19A1, formiminotransferase cyclodeaminase (FTCD), glucose-6-phosphate dehydrogenase (G6PD), and glutamic-oxaloacetic transaminase (GOT2). ACADS, FTCD, and GOT2 were positive factors, whereas CYP19A1 and G6PD were negative. HCC patients with AUC greater than 0.6 were predicted to survive 1/2/3/4/5 years, indicating decent efficiency of the model. The probability of 1/3/5-survival for HCC was also predicted by the nomogram using the risk score, pathologic T stage, and cancer status. Moreover, functional enrichment analysis revealed the high-risk genes were associated with invasion and epithelial-mesenchymal transition. Significantly, immune cell infiltration and immune checkpoint expression were linked to HCC development.

Conclusion

This study identified five biomarkers of propionate metabolism that can predict HCC prognosis. This finding may provide a deeper understanding of PMRG function in HCC.

Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease

Introduction

Cardiovascular disease (CVD) is a group of diseases involving the heart or blood vessels and represents a leading cause of death and disability worldwide. Carotid plaque is an important risk factor for CVD that can reflect the severity of atherosclerosis. Accordingly, developing a prediction model for carotid plaque formation is essential to assist in the early prevention and management of CVD.

Methods

In this study, eight machine learning algorithms were established, and their performance in predicting carotid plaque risk was compared. Physical examination data were collected from 4,659 patients and used for model training and validation. The eight predictive models based on machine learning algorithms were optimized using the above dataset and 10-fold cross-validation. The Shapley Additive Explanations (SHAP) tool was used to compute and visualize feature importance. Then, the performance of the models was evaluated according to the area under the receiver operating characteristic curve (AUC), feature importance, accuracy and specificity.

Results

The experimental results indicated that the XGBoost algorithm outperformed the other machine learning algorithms, with an AUC, accuracy and specificity of 0.808, 0.749 and 0.762, respectively. Moreover, age, smoke, alcohol drink and BMI were the top four predictors of carotid plaque formation. It is feasible to predict carotid plaque risk using machine learning algorithms.

Conclusions

This study indicates that our models can be applied to routine chronic disease management procedures to enable more preemptive, broad-based screening for carotid plaque and improve the prognosis of CVD patients.

Identification of Potential Abnormal Methylation-Modified Genes in Coronary Artery Ectasia

Background

Coronary artery ectasia (CAE) is an easily recognized abnormality of coronary artery anatomy and morphology. However, its pathogenesis remains unclear.

Objectives

This study aimed to identify abnormal methylation-modified genes in patients with CAE, which could provide a research basis for CAE.

Methods

Peripheral venous blood samples from patients with CAE were collected for RNA sequencing to identify differentially expressed genes (DEGs), followed by functional enrichment. Then, the DNA methylation profile of CAE was downloaded from GSE87016 (HumanMethylation450 BeadChip data, involving 11 cases and 12 normal controls) to identify differentially methylated genes (DMGs). Finally, after taking interaction genes between DEGs and DMGs, abnormal methylation-modified genes were identified, followed by protein-protein interaction analysis and expression validation using reverse transcriptase polymerase chain reaction.

Results

A total of 152 DEGs and 4318 DMGs were obtained from RNA sequencing and the GSE87016 dataset, respectively. After taking interaction genes, 9 down-regulated DEGs due to hypermethylation and 11 up-regulated DEGs due to hypomethylation were identified in CAE. A total of 10 core abnormal methylation-modified genes were identified, including six down-regulated DEGs due to hypermethylation (netrin G1, ADAM metallopeptidase domain 12, immunoglobulin superfamily member 10, sarcoglycan dela, Dickkopf WNT signaling pathway inhibitor 3, and GATA binding protein 6), and four up-regulated DEGs due to hypomethylation (adrenomedullin, ubiquitin specific peptidase 18, lymphocyte antigen 6 family member E, and MX dynamin-like GTPase 1). Some signaling pathways were identified in patients with CAE, including cell adhesion molecule, O-glycan biosynthesis, and the renin-angiotensin system.

Conclusions

Abnormal methylation-modified DEGs involved in signaling pathways may be involved in CAE development.

Identification of hypoxia-related genes and exploration of their relationship with immune cells in ischemic stroke

Ischemic stroke (IS) is a major threat to human health, and it is the second leading cause of long-term disability and death in the world. Impaired cerebral perfusion leads to acute hypoxia and glucose deficiency, which in turn induces a stroke cascade response that ultimately leads to cell death. Screening and identifying hypoxia-related genes (HRGs) and therapeutic targets is important for neuroprotection before and during brain recanalization to protect against injury and extend the time window to further improve functional outcomes before pharmacological and mechanical thrombolysis. First, we downloaded the GSE16561 and GSE58294 datasets from the NCBI GEO database. Bioinformatics analysis of the GSE16561 dataset using the limma package identified differentially expressed genes (DEGs) in ischemic stroke using adj. p. values < 0.05 and a fold change of 0.5 as thresholds. The Molecular Signature database and Genecards database were pooled to obtain hypoxia-related genes. 19 HRGs associated with ischemic stroke were obtained after taking the intersection. LASSO regression and multivariate logistic regression were applied to identify critical biomarkers with independent diagnostic values. ROC curves were constructed to validate their diagnostic efficacy. We used CIBERSORT to analyze the differences in the immune microenvironment between IS patients and controls. Finally, we investigated the correlation between HRGs and infiltrating immune cells to understand molecular immune mechanisms better. Our study analyzed the role of HRGs in ischemic stroke. Nineteen hypoxia-related genes were obtained. Enrichment analysis showed that 19 HRGs were involved in response to hypoxia, HIF-1 signaling pathway, autophagy, autophagy of mitochondrion, and AMPK signaling pathway. Because of the good diagnostic properties of SLC2A3, we further investigated the function of SLC2A3 and found that it is closely related to immunity. We have also explored the relevance of other critical genes to immune cells. Our findings suggest that hypoxia-related genes play a crucial role in the diversity and complexity of the IS immune microenvironment. Exploring the association between hypoxia-related critical genes and immune cells provides innovative insights into the therapeutic targets for ischemic stroke.

Identification of potential diagnostic biomarkers of atherosclerosis based on bioinformatics strategy

BACKGROUND

Atherosclerosis is the main pathological change in atherosclerotic cardiovascular disease, and its underlying mechanisms are not well understood. The aim of this study was to explore the hub genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis.

METHODS

Three microarray datasets from Gene Expression Omnibus (GEO) identified robust differentially expressed genes (DEGs) by robust rank aggregation (RRA). We performed connectivity map (CMap) analysis and functional enrichment analysis on robust DEGs and constructed a protein‒protein interaction (PPI) network using the STRING database to identify the hub gene using 12 algorithms of cytoHubba in Cytoscape. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of the hub genes.The CIBERSORT algorithm was used to perform immunocyte infiltration analysis and explore the association between the identified biomarkers and infiltrating immunocytes using Spearman's rank correlation analysis in R software. Finally, we evaluated the expression of the hub gene in foam cells.

RESULTS

A total of 155 robust DEGs were screened by RRA and were revealed to be mainly associated with cytokines and chemokines by functional enrichment analysis. CD52 and IL1RN were identified as hub genes and were validated in the GSE40231 dataset. Immunocyte infiltration analysis showed that CD52 was positively correlated with gamma delta T cells, M1 macrophages and CD4 memory resting T cells, while IL1RN was positively correlated with monocytes and activated mast cells. RT-qPCR results indicate that CD52 and IL1RN were highly expressed in foam cells, in agreement with bioinformatics analysis.

CONCLUSIONS

​This study has established that CD52 and IL1RN may play a key role in the occurrence and development of atherosclerosis, which opens new lines of thought for further research on the pathogenesis of atherosclerosis.

Nonnegative matrix factorization analysis and multiple machine learning methods identified IL17C and ACOXL as novel diagnostic biomarkers for atherosclerosis

BACKGROUND

Atherosclerosis is the common pathological basis for many cardiovascular and cerebrovascular diseases. The purpose of this study is to identify the diagnostic biomarkers related to atherosclerosis through machine learning algorithm.

METHODS

Clinicopathological parameters and transcriptomics data were obtained from 4 datasets (GSE21545, GSE20129, GSE43292, GSE100927). A nonnegative matrix factorization algorithm was used to classify arteriosclerosis patients in GSE21545 dataset. Then, we identified prognosis-related differentially expressed genes (DEGs) between the subtypes. Multiple machine learning methods to detect pivotal markers. Discrimination, calibration and clinical usefulness of the predicting model were assessed using area under curve, calibration plot and decision curve analysis respectively. The expression level of the feature genes was validated in GSE20129, GSE43292, GSE100927.

RESULTS

2 molecular subtypes of atherosclerosis was identified, and 223 prognosis-related DEGs between the 2 subtypes were identified. These genes are not only related to epithelial cell proliferation, mitochondrial dysfunction, but also to immune related pathways. Least absolute shrinkage and selection operator, random forest, support vector machine- recursive feature elimination show that IL17C and ACOXL were identified as diagnostic markers of atherosclerosis. The prediction model displayed good discrimination and good calibration. Decision curve analysis showed that this model was clinically useful. Moreover, IL17C and ACOXL were verified in other 3 GEO datasets, and also have good predictive performance.

CONCLUSION

IL17C and ACOXL were diagnostic genes of atherosclerosis and associated with higher incidence of ischemic events.

Atherosclerosis and Inflammation: Insights from the Theory of General Pathological Processes

Recent advances have greatly improved our understanding of the molecular mechanisms behind atherosclerosis pathogenesis. However, there is still a need to systematize this data from a general pathology perspective, particularly with regard to atherogenesis patterns in the context of both canonical and non-classical inflammation types. In this review, we analyze various typical phenomena and outcomes of cellular pro-inflammatory stress in atherosclerosis, as well as the role of endothelial dysfunction in local and systemic manifestations of low-grade inflammation. We also present the features of immune mechanisms in the development of productive inflammation in stable and unstable plaques, along with their similarities and differences compared to canonical inflammation. There are numerous factors that act as inducers of the inflammatory process in atherosclerosis, including vascular endothelium aging, metabolic dysfunctions, autoimmune, and in some cases, infectious damage factors. Life-critical complications of atherosclerosis, such as cardiogenic shock and severe strokes, are associated with the development of acute systemic hyperinflammation. Additionally, critical atherosclerotic ischemia of the lower extremities induces paracoagulation and the development of chronic systemic inflammation. Conversely, sepsis, other critical conditions, and severe systemic chronic diseases contribute to atherogenesis. In summary, atherosclerosis can be characterized as an independent form of inflammation, sharing similarities but also having fundamental differences from low-grade inflammation and various variants of canonical inflammation (classic vasculitis).

The Study of the Association of Polymorphisms in LSP1, GPNMB, PDPN, TAGLN, TSPO, and TUBB6 Genes with the Risk and Outcome of Ischemic Stroke in the Russian Population

To date, there has been great progress in understanding the genetic basis of ischemic stroke (IS); however, several aspects of the condition remain underexplored, including the influence of genetic factors on post-stroke outcomes and the identification of causative loci. We proposed that an analysis of the results obtained from animal models of brain ischemia could be helpful. To this end, we developed a bioinformatic approach for exploring single-nucleotide polymorphisms (SNPs) in human orthologs of rat genes expressed differentially after induced brain ischemia. Using this approach, we identified and analyzed 11 SNPs from 6 genes in 553 Russian individuals (331 patients with IS and 222 controls). We assessed the association of SNPs with the risk of IS and IS outcomes. We found that the SNPs rs858239 (GPNMB), rs907611 (LSP1), and rs494356 (TAGLN) were associated with different parameters of IS functional outcomes. In addition, the SNP rs1261025 (PDPN) was associated significantly with IS itself (p = 0.0188, recessive model). All these associations were demonstrated for the first time. Analysis of the literature suggests that they should be characterized as being inflammation related. This supports the pivotal role of inflammation in both the incidence of stroke and post-stroke outcomes. We believe the findings reported here will help with stroke prognosis in the future.

Heterogeneity of macrophages in atherosclerosis revealed by single-cell RNA sequencing

Technology at the single-cell level has advanced dramatically in characterizing molecular heterogeneity. These technologies have enabled cell subtype diversity to be seen in all tissues, including atherosclerotic plaques. Critical in atherosclerosis pathogenesis and progression are macrophages. Previous studies have only determined macrophage phenotypes within the plaque, mainly by bulk analysis. However, recent progress in single-cell technologies now enables the comprehensive mapping of macrophage subsets and phenotypes present in plaques. In this review, we have updated and discussed the definition and classification of macrophage subsets in mice and humans using single-cell RNA sequencing. We summarized the different classification methods and perspectives: traditional classification with an updated scoring system, inflammatory macrophages, foamy macrophages, and atherosclerotic-resident macrophages. In addition, some special types of macrophages were identified by specific markers, including IFN-inducible and cavity macrophages. Furthermore, we discussed macrophage subset-specific markers and their functions. In the future, these novel insights into the characteristics and phenotypes of these macrophage subsets within atherosclerotic plaques can provide additional therapeutic targets for cardiovascular diseases.

Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning

Introduction

Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored.

Methods

First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender.

Results

Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age.

Discussion

This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.

Novel immune cell infiltration-related biomarkers in atherosclerosis diagnosis

Background

Immune cell infiltration (ICI) has a close relationship with the progression of atherosclerosis (AS). Therefore, the current study was aimed to explore the role of genes related to ICI and to investigate potential mechanisms in AS.

Methods

Single-sample gene set enrichment analysis (ssGSEA) was applied to explore immune infiltration in AS and controls. Genes related to immune infitration were mined by weighted gene co-expression network analysis (WGCNA). The function of those genes were analyzed by enrichment analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The interactions among those genes were visualized in the protein-protein interaction (PPI) network, followed by identification of hub genes through Cytoscape software. A receiver operating characteristic (ROC) plot was generated to assess the performance of hub genes in AS diagnosis. The expressions of hub genes were measured by reverse transcription quantitative real-time PCR (RT-qPCR) in human leukemia monocyticcell line (THP-1) derived foam cells and macrophages, which mimic AS and control, respectively.

Results

We observed that the proportions of 27 immune cells were significantly elevated in AS. Subsequent integrative analyses of differential expression and WGCNA identified 99 immune cell-related differentially expressed genes (DEGs) between AS and control. Those DEGs were associated with tryptophan metabolism and extracellular matrix (ECM)-related functions. Moreover, by constructing the PPI network, we found 11 hub immune cell-related genes in AS. The expression pattern and receiver ROC analyses in two independent datasets showed that calsequestrin 2 (CASQ2), nexilin F-Actin binding protein (NEXN), matrix metallopeptidase 12 (MMP12), C-X-C motif chemokine ligand 10 (CXCL10), phospholamban (PLN), heme oxygenase 1 (HMOX1), ryanodine receptor 2 (RYR2), chitinase 3 like 1 (CHI3L1), matrix metallopeptidase 9 (MMP9), actin alpha cardiac muscle 1 (ACTC1) had good performance in distinguishing AS from control samples. Furthermore, those biomarkers were shown to be correlated with angiogenesis and immune checkpoints. In addition, we found 239 miRNAs and 47 transcription factor s (TFs), which may target those biomarkers and regulate their expressions. Finally, we found that RT-qPCR results were consistent with sequencing results.