Identification of Potential Key Genes Involved in the Carotid Atherosclerosis

Spatial Transcriptomic Approach to Understanding Coronary Atherosclerotic Plaque Stability

BACKGROUND

Coronary atherosclerotic plaques susceptible to acute coronary syndrome have traditionally been characterized by their surrounding cellular architecture. However, with the advent of intravascular imaging, novel mechanisms of coronary thrombosis have emerged, challenging our contemporary understanding of acute coronary syndrome. These intriguing findings underscore the necessity for a precise molecular definition of plaque stability. Considering this, our study aimed to investigate the vascular microenvironment in patients with stable and unstable plaques using spatial transcriptomics.

METHODS

Autopsy-derived coronary arteries were preserved and categorized by plaque stability (n=5 patients per group). We utilized the GeoMx spatial profiling platform and Whole Transcriptome Atlas to link crucial histological morphology markers in coronary lesions with differential gene expression in specific regions of interest, thereby mapping the vascular transcriptome. This innovative approach allowed us to conduct cell morphological and spatially resolved transcriptional profiling of atherosclerotic plaques while preserving crucial intercellular signaling.

RESULTS

We observed intriguing spatial and cell-specific transcriptional patterns in stable and unstable atherosclerotic plaques, showcasing regional variations within the intima and media. These regions exhibited differential expression of proinflammatory molecules (eg, IFN-γ [interferon-γ], MHC [major histocompatibility complex] class II, proinflammatory cytokines) and prothrombotic signaling pathways. By using lineage tracing through spatial deconvolution of intimal CD68+ (cluster of differentiation 68) cells, we characterized unique, intraplaque subpopulations originating from endothelial, smooth muscle, and myeloid lineages with distinct regional locations associated with plaque instability. In addition, unique transcriptional signatures were observed in vascular smooth muscle and CD68+ cells among plaques exhibiting coronary calcification.

CONCLUSIONS

Our study illuminates distinct cell-specific and regional transcriptional alterations present in unstable plaques. Furthermore, we characterize spatially resolved, in situ evidence supporting cellular transdifferentiation and intraplaque plasticity as significant contributors to plaque instability in human coronary atherosclerosis. Our results provide a powerful resource for the identification of novel mediators of acute coronary syndrome, opening new avenues for preventative and therapeutic treatments.

Transcriptomic and Multi-scale Network Analyses Reveal Key Drivers of Cardiovascular Disease

Cardiovascular diseases (CVDs) and pathologies are often driven by changes in molecular signaling and communication, as well as in cellular and tissue components, particularly those involving the extracellular matrix (ECM), cytoskeleton, and immune response. The fine-wire vascular injury model is commonly used to study neointimal hyperplasia and vessel stiffening, but it is not typically considered a model for CVDs. In this paper, we hypothesize that vascular injury induces changes in gene expression, molecular communication, and biological processes similar to those observed in CVDs at both the transcriptome and protein levels. To investigate this, we analyzed gene expression in microarray datasets from injured and uninjured femoral arteries in mice two weeks post-injury, identifying 1,467 significantly and differentially expressed genes involved in several CVDs such as including vaso-occlusion, arrhythmia, and atherosclerosis. We further constructed a protein-protein interaction network with seven functionally distinct clusters, with notable enrichment in ECM, metabolic processes, actin-based process, and immune response. Significant molecular communications were observed between the clusters, most prominently among those involved in ECM and cytoskeleton organizations, inflammation, and cell cycle. Machine Learning Disease pathway analysis revealed that vascular injury-induced crosstalk between ECM remodeling and immune response clusters contributed to aortic aneurysm, neovascularization of choroid, and kidney failure. Additionally, we found that interactions between ECM and actin cytoskeletal reorganization clusters were linked to cardiac damage, carotid artery occlusion, and cardiac lesions. Overall, through multi-scale bioinformatic analyses, we demonstrated the robustness of the vascular injury model in eliciting transcriptomic and molecular network changes associated with CVDs, highlighting its potential for use in cardiovascular research.

Integrating UPLC-Q-TOF-MS and Network Pharmacology to Explore the Potential Mechanisms of Paeonia lactiflora Pall. in the Treatment of Blood Stasis Syndrome

Paeonia lactiflora Pall. (PLP) is thought to promote blood circulation and remove blood stasis. This study used blood component analysis, network pharmacology, and molecular docking to predict the mechanism of PLP in the treatment of blood stasis syndrome (BSS). PLP was processed into Paeoniae Radix Alba (PRA) and Paeoniae Radix Rubra (PRR). PRA and PRR could significantly reduce whole blood viscosity (WBV) at 1/s shear rates and could increase the erythrocyte aggregation index (EAI), plasma viscosity (PV), and erythrocyte sedimentation rate (ESR) of rats with acute blood stasis. They prolonged the prothrombin time (PT), and PRR prolonged the activated partial thromboplastin time (APTT). PRA and PRR increased the thrombin time (TT) and decreased the fibrinogen (FBG) content. All the results were significant (p < 0.05). Ten components of Paeoniflorin, Albiflorin, Paeonin C, and others were identified in the plasma of rats using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). A protein-protein interaction network (PPI) analysis showed that AKT1, EGFR, SRC, MAPK14, NOS3, and KDR were key targets of PLP in the treatment of BSS, and the molecular docking results further verified this. This study indicated that PLP improves BSS in multiple ways and that the potential pharmacological mechanisms may be related to angiogenesis, vasoconstriction and relaxation, coagulation, and the migration and proliferation of vascular cells.

Unveiling Atherosclerotic Plaque Heterogeneity and SPP1+/VCAN+ Macrophage Subtype Prognostic Significance Through Integrative Single-Cell and Bulk-Seq Analysis

Background

Dysregulated macrophages are important causes of Atherosclerosis (AS) formation and increased plaque instability, but the heterogeneity of these plaques and the role of macrophage subtypes in plaque instability have yet to be clarified.

Methods

This study integrates single-cell and bulk-seq data to analyze atherosclerotic plaques. Unsupervised clustering was used to reveal distinct plaque subtypes, while survival analysis and gene set variation analysis (GSVA) methods helped in understanding their clinical outcomes. Enrichment of differential expression of macrophage genes (DEMGs) score and pseudo-trajectory analysis were utilized to explore the biological functions and differentiation stages of macrophage subtypes in AS progression. Additionally, CellChat and the BayesPrism deconvolution method were used to elucidate macrophage subtype interaction and their prognostic significance at single-cell resolution. Finally, the expression of biomarkers was validated in mouse experiments.

Results

Three distinct AS plaque subtypes were identified, with cluster 3 plaque subtype being particularly associated with higher immune infiltration and poorer prognosis. The DEMGs score exhibited a significant elevation in three macrophage subtypes (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages), associated with cluster 3 plaque subtype and highlighted the prognostic significance of these subtypes. Activation trajectory of the macrophage subtypes is divided into three states (Pre-branch, Cell fate 1, and Cell fate 2), and Cell fate 2 (SPP1+/VCAN+ macrophages, IL1B+ macrophages, and FLT3LG+ macrophages dominant) exhibiting the highest DEMGs score, distinct interactions with other cell components, and relating to poorer prognosis of ischemic events. This study also uncovered a unique SPP1+/VCAN+ macrophage subtype, rare in quantity but significant in influencing AS progression. Machine learning algorithms identified 10 biomarkers crucial for AS diagnosis. The validation of these biomarkers was performed using Mendelian Randomization analysis and in vitro methods, supporting their relevance in AS pathology.

Conclusion

Our study provides a comprehensive view of AS plaque heterogeneity and the prognostic significance of macrophage subtypes in plaque instability.

Identification of pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis based on comprehensive bioinformatics analysis and machine learning

Objective

Bioinformatics methods were applied to investigate the pivotal genes and regulatory networks associated with atherosclerotic carotid artery stenosis (ACAS) and provide new insights for the treatment of this disease.

Methods

The study utilized five ACAS datasets (GSE100927, GSE11782, GESE28829, GSE41571, and GSE43292) downloaded from the NCBI GEO database. The first four datasets were combined as the training set (n = 99), while GSE43292 (n = 64) was used as the validation set. Difference analysis and functional enrichment analysis were then performed on the training set. The pathogenic targets of ACAS were screened by protein-protein interaction networks and MCODE analyses, combined with three machine learning algorithms. The results were next verified by analysis of inter-group differences and ROC curve analysis. Next, immune-related function and immune cell correlation analyses were performed, and plaques of human ACAS were applied to verify the results via immunohistochemistry (IH) and immunofluorescence (IF). Finally, the competing endogenous RNAs (ceRNA) and transcription factors (TFs) regulatory networks of the characterized genes were constructed.

Results

A total of 177 differentially expressed genes were identified, including 67 genes downregulated and 110 genes upregulated. Gene set enrichment analysis revealed that five pathways were active in the experimental group, including xenograft rejection, autoimmune thyroid disease, graft-versus-host disease, leishmaniasis infection, and lysosomes. Four key genes were identified, with C3AR1 being upregulated and FBLN5, PPP1R12A, and TPM1 being downregulated. The analysis of inter-group differences demonstrated that the four characterized genes were differentially expressed in both the control and experimental groups. The ROC analysis showed that they had high AUC values in both the training and validation sets. Therefore, a predictive ACAS patient nomogram model based on the screened genes was established. Correlation analysis revealed a positive correlation between C3AR1 expression and neutrophils, which was further validated in IH and IF. One or multiple lncRNAs may compete with the characterized genes for binding miRNAs. Additionally, each characterized gene interacts with multiple TFs.

Conclusion

Four pivotal genes were screened, and relevant ceRNA and TFs were predicted. These molecules may exert a crucial role in ACAS and serve as potential biomarkers and therapeutic targets.

Identification of key genes for atherosclerosis in different arterial beds

Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

Identification of important genes related to HVSMC proliferation and migration in graft restenosis based on WGCNA

The great saphenous vein is the most commonly used vessel for coronary artery bypass grafting (CABG), but its use has been associated with a high restenosis rate at 10-year follow-up. This study sought to determine the key genes associated with vein graft restenosis that could serve as novel therapeutic targets. A total of 3075 upregulated and 1404 downregulated genes were identified after transcriptome sequencing of three pairs of restenosed vein grafts and intraoperative spare great saphenous veins. Weighted gene co-expression network analysis showed that the floralwhite module had the highest correlation with vein graft restenosis. The intersection of the floralwhite module gene set and the upregulated gene set contained 615 upregulated genes strongly correlated with vein graft restenosis. Protein-protein interaction network analysis identified six hub genes (ITGAM, PTPRC, TLR4, TYROBP, ITGB2 and CD4), which were obtained using the STRING database and CytoHubba. Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the common hub genes were mainly involved in the composition of the cell membrane; in biological processes such as neutrophil degranulation, receptor binding and intercellular adhesion, innate immune deficiency; and other signaling pathways. Finally, ITGB2 was selected as the target gene, and its expression was verified in tissues. The results showed that ITGB2 was significantly overexpressed in occluded vein grafts. To study the function of ITGB2 in HVSMCs, primary HVSMCs were cultured and successfully identified. EdU incorporation, wound healing and transwell assays showed that ITGB2 silencing significantly inhibited the proliferation and migration of HVSMCs stimulated by PDGF-BB. Overall, our study provides a basis for future studies on preventing restenosis following CABG.

ITGB2 is a central hub-gene associated with inflammation and early fibro-atheroma development in a swine model of atherosclerosis

Background and aim

The complex dynamic interplay between different biological pathways involved in atherosclerosis development has rendered the identification of specific therapeutic targets a challenging quest. We aimed to identify specific genes and mechanistic pathways associated with the early development of fibro-atheromas in a swine model of atherosclerosis.

Methods

The Wisconsin Miniature Swine™ model of Familial Hypercholesterolemia (WMS-FH, n = 11) and genetically related WMS controls (WMS-N, n = 11) were used. The infrarenal aorta was harvested from both groups for histopathologic and transcriptomic profiling at 12 months. Bioinformatic analysis was performed to identify hub genes and pathways central to disease pathophysiology. The expression of ITGB2, the top ranked hub gene, was manipulated in cell culture and the expression of interconnected genes was tested.

Results

Fibro-atheromatous lesions were documented in all WMS-FH aortic tissues and displayed internal elastic lamina (IEL) disruption, significant reduction of myofibroblast presence and disorganized collagen deposition. No fibro-atheromas were observed in the control group. A total of 266 differentially expressed genes (DEGs) were upregulated in WMS-FH aortic tissues, while 29 genes were downregulated. Top identified hub genes included ITGB2, C1QA, LCP2, SPI1, CSF1R, C5AR1, CTSS, MPEG1, C1QC, and CSF2RB. Overexpression of ITGB2 resulted in elevated expression of other interconnected genes expressed in porcine endothelial cells.

Conclusion

In a swine translational model of atherosclerosis, transcriptomic analysis identified ITGB2 as a central hub gene associated inflammation and early fibroatheroma development making it a potential therapeutic target at this stage of disease.

SEL1L3 as a link molecular between renal cell carcinoma and atherosclerosis based on bioinformatics analysis and experimental verification

BACKGROUND

Renal cancer, the most common type of kidney cancer, develops in the renal tubular epithelium. Atherosclerosis of the aorta is the primary cause of atherosclerosis. However, the underlying mechanisms remain unclear.

METHODS

The renal clear cell carcinoma RNA sequence profile was obtained from The Cancer Genome Atlas (TCGA) database, and the atherosclerosis datasets GSE28829 and GSE43292 based on GPL570 and GPL6244 was obtained from the Gene Expression Omnibus (GEO) database. The difference and hub genes were identified by the Limma protein-protein interaction (PPI) network in R software. Functional enrichment, survival, and immunoinfiltration analyses were performed. The role of SEL1L3 in the ErbB/PI3K/mTOR signaling pathway, apoptosis, invasion, cell cycle, and inflammation was analyzed using western blotting.

RESULTS

764 DEGs were identified from TCGA Kidney Renal Clear Cell Carcinoma (KIRC) dataset. A total of 344 and 117 DEGs were screened from the GSE14762 and GSE53757 datasets, respectively. Functional enrichment analysis results primarily indicated enrichment in the transporter complex, DNA-binding transcription activator activity, morphogenesis of the embryonic epithelium, stem cell proliferation, adrenal overactivity and so on. Fifteen common DEGs overlapped among the three datasets. The PPI network revealed that SEL1L3 was the core gene. Survival analysis showed that lower SEL1L3 expression levels led to a worse prognosis. Immune cell infiltration analysis showed that SEL1L3 expression was significantly correlated with antibody-drug conjugates (aDC), B cells, eosinophils, interstitial dendritic cells (iDC), macrophages, and more.

CONCLUSIONS

SEL1L3 plays an important role in renal clear cell carcinoma and atherosclerosis and may be a potential link between them.

Identification of useful biomolecular markers in kidney renal clear cell carcinoma: an in silico and in vitro analysis-based study

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is the most prevalent type of renal cell carcinoma (RCC), with a high incidence and mortality rate. There is a lack of sensitive biomarkers. Therefore, the discovery of accurate biomarkers for KIRC patients is critical to improve prognosis.

METHODS

We determined hub genes and their associated pathways involved in the pathogenesis of KIRC from the GSE66272 dataset consisting of KIRC (n = 26) and corresponding control (n = 26) samples and later validated the expression and methylation level of the identified hub genes on The Cancer Genomic Atlas (TCGA) datasets and Human RCC 786-O and normal HK-2 cell lines through RNA sequencing (RNA-seq), Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and targeted bisulfite sequencing (bisulfite-seq) analyses.

RESULTS

The identified up-regulated four hub genes include TYROBP (Transmembrane Immune Signaling Adaptor TYROBP), PTPRC (Protein tyrosine phosphatase, receptor type, C), LCP2 (Lymphocyte cytosolic protein 2), and ITGB2 (Integrin Subunit Beta 2). Moreover, the higher expression of TYROBP, PTPRC, LCP2, and ITGB2 in KIRC patients insignificantly correlates with a poor prognosis in KIRC patients. In addition, hub genes were involved in the "Fc epsilon RI signaling pathway, asthma, natural cell killer mediated cytotoxicity, T cell receptor signaling pathway, primary immunodeficiency, Fc gamma R-mediated phagocytosis, malaria, leukocyte transendothelial migration, and legionellosis" pathways and associated with the infiltration level of CD8+ T, CD4+ T, and macrophage cells.

CONCLUSION

Our integrated in silico and in vitro analysis identified important hub genes (TYROBP, PTPRC, LCP2, and ITGB2) involved in the pathogenesis of KIRC as possible diagnostic biomarkers.

Identification of the shared mechanisms and common biomarkers between Sjögren's syndrome and atherosclerosis using integrated bioinformatics analysis

Background

Sjögren's syndrome (SS) is a chronic autoimmune disease characterized by exocrine and extra-glandular symptoms. The literature indicates that SS is an independent risk factor for atherosclerosis (AS); however, its pathophysiological mechanism remains undetermined. This investigation aimed to elucidate the crosstalk genes and pathways influencing the pathophysiology of SS and AS via bioinformatic analysis of microarray data.

Methods

Microarray datasets of SS (GSE40611) and AS (GSE28829) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired using R software's "limma" packages, and the functions of common DEGs were determined using Gene Ontology and Kyoto Encyclopedia analyses. The protein-protein interaction (PPI) was established using the STRING database. The hub genes were assessed via cytoHubba plug-in and validated by external validation datasets (GSE84844 for SS; GSE43292 for AS). Gene set enrichment analysis (GSEA) and immune infiltration of hub genes were also conducted.

Results

Eight 8 hub genes were identified using the intersection of four topological algorithms in the PPI network. Four genes (CTSS, IRF8, CYBB, and PTPRC) were then verified as important cross-talk genes between AS and SS with an area under the curve (AUC) ≥0.7. Furthermore, the immune infiltration analysis revealed that lymphocytes and macrophages are essentially linked with the pathogenesis of AS and SS. Moreover, the shared genes were enriched in multiple metabolisms and autoimmune disease-related pathways, as evidenced by GSEA analyses.

Conclusion

This is the first study to explore the common mechanism between SS and AS. Four key genes, including CTSS, CYBB, IRF8, and PTPRC, were associated with the pathogenesis of SS and AS. These hub genes and their correlation with immune cells could be a potential diagnostic and therapeutic target.

Candidate SNP Markers Significantly Altering the Affinity of TATA-Binding Protein for the Promoters of Human Hub Genes for Atherogenesis, Atherosclerosis and Atheroprotection

Atherosclerosis is a systemic disease in which focal lesions in arteries promote the build-up of lipoproteins and cholesterol they are transporting. The development of atheroma (atherogenesis) narrows blood vessels, reduces the blood supply and leads to cardiovascular diseases. According to the World Health Organization (WHO), cardiovascular diseases are the leading cause of death, which has been especially boosted since the COVID-19 pandemic. There is a variety of contributors to atherosclerosis, including lifestyle factors and genetic predisposition. Antioxidant diets and recreational exercises act as atheroprotectors and can retard atherogenesis. The search for molecular markers of atherogenesis and atheroprotection for predictive, preventive and personalized medicine appears to be the most promising direction for the study of atherosclerosis. In this work, we have analyzed 1068 human genes associated with atherogenesis, atherosclerosis and atheroprotection. The hub genes regulating these processes have been found to be the most ancient. In silico analysis of all 5112 SNPs in their promoters has revealed 330 candidate SNP markers, which statistically significantly change the affinity of the TATA-binding protein (TBP) for these promoters. These molecular markers have made us confident that natural selection acts against underexpression of the hub genes for atherogenesis, atherosclerosis and atheroprotection. At the same time, upregulation of the one for atheroprotection promotes human health.

Potential miRNA biomarkers and therapeutic targets for early atherosclerotic lesions

Identification of potential therapeutic targets and biomarkers indicative of burden of early atherosclerosis that occur prior to advancement to life-threatening unstable plaques is the key to eradication of CAD prevalence and incidences. We challenged 16 baboons with a high cholesterol, high fat diet for 2 years and evaluated early-stage atherosclerotic lesions (fatty streaks, FS, and fibrous plaques, FP) in formalin-fixed common iliac arteries (CIA). We used small RNA sequencing to identify expressed miRNAs in CIA and in baseline blood samples of the same animals. We found 412 expressed miRNAs in CIA and 356 in blood samples. Eight miRNAs (miR-7975, -486-5p, -451a, -191-5p, -148a-3p, -17-5p, -378c, and -144-3p) were differentially expressed between paired fatty streak lesion and no-lesion sites of the tissue, and 27 miRNAs (e.g., miR-92a-3p, -5001, -342-3p, miR-28-3p, -21-5p, -221-3p, 146a-5p, and -16-5p) in fibrous plaques. The expression of 14 blood miRNAs significantly correlated with extent of lesions and the number of plaques. We identified coordinately regulated miRNA-gene networks in which miR-17-5p and miR-146a-5p are central hubs and miR-5001 and miR-7975 are potentially novel miRNAs associated with early atherosclerosis. In summary, we have identified miRNAs expressed in lesions and in blood that correlate with lesion burden and are potential therapeutic targets and biomarkers. These findings are a first step in elucidating miRNA regulated molecular mechanisms that underlie early atherosclerosis in a baboon model, enabling translation of our findings to humans.

The Profile and Clinical Significance of ITGB2 Expression in Non-Small-Cell Lung Cancer

Integrins are involved in extracellular and intracellular signaling and are often aberrantly expressed in tumors. Integrin beta 2 (ITGB2) has previously been demonstrated to be correlated with the host defense. However, the expression profile and role of ITGB2 in non-small-cell lung cancer (NSCLC) remain unclear. Here, we found that the genetic alterations in ITGB2 was predominated by gene mutation and copy number deletion using cBioPortal analysis, and its expression was downregulated in the NSCLC tissues, as validated by the UALCAN, TCGA, and GEO databases and our tissue samples. Kaplan-Meier (KM) plotter analysis revealed that patients with a lower ITGB2 expression had a shorter overall survival (OS) time (p = 0.01). Moreover, 1089 differentially expressed genes (DEGs) in the NSCLC tissues were screened using the TCGA database. The GO and KEGG enrichment analysis showed that the DEGs were closely associated with immune processes and cell adhesion. The protein-protein interaction (PPI) network revealed that 10 of 15 EMT-related genes among the DEGs might lead to the metastasis of NSCLC. Concomitantly, the expression of ITGB2 was positively correlated with the infiltration of Treg cells and Myeloid-derived suppressor cells (MDSC). Biologically, the ectopic expression of ITGB2 significantly inhibited the proliferation and metastasis of NSCLC cells. Mechanistically, we demonstrated that ITGB2 suppressed the expression of N-cadherin, Vimentin, Slug, Snail, and Twist, while it promoted E-cadherin expression, according to gain-of-function studies. In conclusion, ITGB2 can inhibit the proliferation and migration of NSCLC cells, leading to a poor prognosis, via epithelial-mesenchymal transition (EMT) signaling.

Identification of potential drug targets for vascular dementia and carotid plaques by analyzing underlying molecular signatures shared by them

Background

Vascular dementia (VaD) and carotid atherosclerotic plaques are common in the elderly population, conferring a heavy burden on families and society. Accumulating evidence indicates carotid atherosclerotic plaques to be a risk factor for VaD. However, the underlying mechanisms for this association are mainly unknown.

Materials and methods

We analyzed temporal cortex gene expression data of the GSE122063 dataset and gene expression data of the GSE163154 dataset to identify commonly differentially expressed genes (DEGs). Then we performed functional enrichment analysis, immune cell infiltration and evaluation, correlation analysis between differentially expressed immune-related genes (DEIRGs) and immune cells, receiver operating characteristic (ROC) analysis, and drug-gene analysis.

Results

We identified 41 overlapped DEGs between the VaD and carotid atherosclerosis plaque datasets. Functional enrichment analyses revealed that these overlapped DEGs were mainly enriched in inflammatory and immune-related processes. Immunocyte infiltration and evaluation results showed that M0 macrophages, M2 macrophages, and T cells gamma delta had a dominant abundance in carotid atherosclerosis plaque samples, and M0 macrophages showed a significantly different infiltration percentage between the early and advanced stage plaques group. Resting CD4 memory T cells, M2 macrophages, and naive B cells were the top three highest infiltrating fractions in VaD. Furthermore, B cells and NK cells showed a different infiltration percentage between VaD and matched controls. We identified 12 DEIRGs, and the result of correlation analysis revealed that these DEIRGs were closely related to differentially expressed immune cells. We identified five key DEIRGs based on ROC analysis. The drug-gene interaction analysis showed that four drugs (avacopan, CCX354, BMS-817399, and ASK-8007) could be potential drugs for VaD and carotid atherosclerotic plaques treatment.

Conclusion

Collectively, these findings indicated that inflammatory and immune-related processes be a crucial common pathophysiological mechanism shared by VaD and carotid plaques. This study might provide new insights into common molecular mechanisms between VaD and carotid plaques and potential targets for the treatment.

miR-22-3p as a potential biomarker for coronary artery disease based on integrated bioinformatics analysis

Background: Coronary artery disease (CAD) is a common cardiovascular disease that has attracted attention worldwide due to its high morbidity and mortality. Recent studies have shown that abnormal microRNA (miRNA) expression is effective in CAD diagnoses and processes. However, the potential relationship between miRNAs and CAD remains unclear.

Methods: Microarray datasets GSE105449 and GSE28858 were downloaded directly from the Gene Expression Omnibus (GEO) to identify miRNAs involved in CAD. Target gene prediction and enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results: There were nine differentially expressed miRNAs in CAD patients compared to the controls. A total of 352 genes were predicted and subjected to GO analysis, which showed that differentially expressed genes (DEGs) were mainly associated with axon guidance, neuron projection guidance, neuron-to-neuron synapses, and postsynaptic density. According to the KEGG pathway analysis, the most enriched pathways were those involved in transcriptional misregulation in cancer, growth hormone synthesis, secretion and action, endocrine resistance, axon guidance, and Cushing syndrome. Pathway analysis was mainly involved in the HIPPO and prion disease signaling pathways. Furthermore, a competing endogenous RNA (ceRNA) interaction network centered on miR-22-3p revealed eight related transcription factors in the cardiovascular system. The receiver operating characteristic (ROC) curve analysis suggested that miR-22-3p may be a better CAD predictor.

Conclusion: The results indicate that miR-22-3p may function in pathophysiological CAD processes. Our study potentiates miR-22-3p as a specific biomarker for diagnosing CAD.

Identification of Biomarkers of Autophagy-Related Genes Between Early and Advanced Carotid Atherosclerosis

Background

Accumulating evidence demonstrates that autophagy is important in inhibiting inflammation and cholesterol efflux. It suggested the autophagy may be a treatment of atherosclerosis. Thus, we screened autophagy-related mRNA to explore their mechanism of scientific basis for early diagnosis and therapy of atherosclerosis.

Methods

The GSE28829 datasets were assessed to analyze differentially expressed genes by GEO2R. And autophagy-related hub genes were identified by HADb. The biological function of autophagy-related DEmRNAs was examined by Metascape. The construction of a protein–protein network was explored by String. Cytohubba was utilized to screen hub genes. Analysis of DEmiRNA-mRNA pairs was executed by DIANA microT-CDS database. Finally, correlation analysis was carried out to identify the relationship between DEARGs and clinical and prognostic factors.

Results

A number of 1087 DEGs and 19 autophagy-related DEmRNAs were identified in advanced carotid atherosclerotic plaque compared with the early. The biological function containing development and growth was enriched. Moreover, we screened the top hub nodes with the highest degrees. MicroRNAs (miRNAs) are confirmed to participate in genesis and progression of atherosclerosis, so we further analyzed the miRNA–mRNA regulatory network genes with four hub genes to explore their potential mechanism in atherosclerosis. Then, we revealed co-expression of four key genes CTSB, ITGB1, CXCR4, TNFSF10 and autophagy-related genes. As for the clinical factors, hypertension factor showed higher expression of ITGB1. The probability of coronary heart disease factor was significantly increased with high expression of CTSB and CXCR4, as well as low expression of ITGB1 and TNFSF10. Diabetes factor tended to express distinguished levels of CTSB and ITGB1. TNFSF10 was highly expressed in both hyperlipidemia and ischemic stroke factor.

Conclusion

CTSB, ITGB1, CXCR4 and TNFSF10 may be critical in atherosclerosis development and were thought to be potential diagnostic biomarkers for atherosclerosis.

Differential Gene Expression and Immune Cell Infiltration in Carotid Intraplaque Hemorrhage Identified Using Integrated Bioinformatics Analysis

Background

Intraplaque hemorrhage (IPH) is an important feature of unstable plaques and an independent risk factor for cardiovascular events. However, the molecular mechanisms contributing to IPH are incompletely characterized. We aimed to identify novel biomarkers and interventional targets for IPH and to characterize the role of immune cells in IPH pathogenesis.

Methods

The microarray dataset GSE163154 which contain IPH and non-IPH plaque samples was obtained from the Gene Expression Omnibus (GEO). R software was adopted for identifying differentially expressed genes (DEGs) and conducting functional investigation. The hub genes were carried by protein-protein interaction (PPI) network and were validated by the GSE120521 dataset. CIBERSORT deconvolution was used to determine differential immune cell infiltration and the relationship of immune cells and hub genes. We confirmed expression of proteins encoded by the hub genes by immunohistochemistry and western blotting in 8 human carotid endarterectomy samples with IPH and 8 samples without IPH (non-IPH).

Results

We detected a total of 438 differentially expressed genes (DEGs), of which 248 were upregulated and 190 were downregulated. DEGs were mainly involved in inflammatory related pathways, including neutrophil activation, neutrophil degranulation, neutrophil-mediated immunity, leukocyte chemotaxis, and lysosomes. The hub genes found through the method of degree in the PPI network showed that ITGB2 and ITGAM might play an important role in IPH. Receiver operating characteristic (ROC) results also showed a good performance of these two genes in the test and validation dataset. We found that the proportions of infiltrating immune cells in IPH and non-IPH samples differed, especially in terms of M0 and M2 macrophages. Immunohistochemistry and western blotting analysis showed that expression levels of ITGB2 and ITGAM increased significantly in carotid atherosclerotic plaques with IPH.

Conclusion

ITGB2 and ITGAM are key hub genes of IPH and may play an important role in the biological process of IPH. Our findings advance our understanding of the underlying mechanisms of IPH pathogenesis and provide valuable information and directions for future research into novel targets for IPH diagnosis and immunotherapy.