Genes associated with inflammation and the cell cycle may serve as biomarkers for the diagnosis and prognosis of acute myocardial infarction in a Chinese population

Screening of Potential Circulating Diagnostic Biomarkers and Molecular Mechanisms of Systemic Lupus Erythematosus-Related Myocardial Infarction by Integrative Analysis

Background

The risk of acute myocardial infarction (AMI) is elevated in patients with systemic lupus erythematosus (SLE), and it is of great clinical value to identify potential molecular mechanisms and diagnostic markers of AMI associated with SLE by analyzing public database data and transcriptome sequencing data.

Methods

AMI and SLE-related sequencing datasets GSE62646, GSE60993, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database and divided into prediction and validation cohorts. To identify the key genes associated with AMI related to SLE, WGCNA and DEGs analysis were performed for the prediction and validation cohorts, respectively. The related signaling pathways were identified by GO/KEGG enrichment analysis. Peripheral blood mononuclear cells (PBMCs) from patients with AMI were collected for transcriptome sequencing to validate the expression of key genes in patients with AMI. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to screen diagnostic biomarkers. The diagnostic efficacy of biomarkers was validated by ROC analysis, and the CIBERSORTx platform was used to analyze the composition of immune cells in AMI and SLE.

Results

A total of 108 genes closely related to AMI and SLE were identified in the prediction cohort, and GO/KEGG analysis showed significantly enriched signaling pathways. The results of differential analysis in validation cohort were consistent with them. By transcriptional sequencing of PBMCs from peripheral blood of AMI patients, combined with the results of prediction and validation cohort analysis, seven genes were finally screened out. LASSO analysis finally identifies DYSF, LRG1 and CSF3R as diagnostic biomarkers of SLE-related-AMI. CIBERSORTx analysis revealed that the biomarkers were highly correlated with neutrophils.

Conclusion

Neutrophil degranulation and NETs formation play important roles in SLE-related AMI, and DYSF, LRG1 and CSF3R were identified as important diagnostic markers for the development and progression of SLE-related AMI.

Upregulation of HTRA1 mediated by the lncRNA NEAT1/miR-141-3p axis contributes to endometriosis development through activating NLRP3 inflammasome-mediated pyroptotic cell death and cellular inflammation

The present study identified a novel upstream long chain non-coding (lncRNA) NEAT1/miR-141-3p/HTRA1 axis that regulated the activation of NLR family pyrin domain containing 3 (NLRP3) inflammasome to modulate endometriosis (EM) development. Specifically, clinical data suggested that the expression of NLRP3 and apoptosis-associated speck-like protein containing CARD (ASC), the cleavage of caspase-1 and gasdermin D (GSDMD), and the production of inflammatory cytokines (interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and IL-18) were all significantly increased in the ectopic endometrium (EE) tissues, compared to the normal endometrium (NE) tissues. Then, through analyzing the datasets from GEO database (GSE2339, GSE58178, and GSE7305) using the GEO2R bioinformatics tools, we verified that HtrA Serine Peptidase 1 (HTRA1) was especially enriched in the EE tissues compared to the NE tissues. To further confirm the biological functions of HTRA1, HTRA1 was overexpressed or downregulated in primary human endometrial stromal cells (hESCs) isolated from NE tissues or EE tissues, respectively. The results showed that upregulation of HTRA1 activated NLRP3 inflammasome-mediated pyroptotic cell death and cellular inflammation in NE-derived hESCs, whereas silencing of HTRA1 played an opposite role in EE-derived hESCs. In addition, the lncRNA NEAT1/miR-141-3p axis was screened as the upstream regulator of HTRA1. Mechanistically, lncRNA NEAT1 sponged miR-141-3p to positively regulate HTRA1 in a competing endogenous RNA (ceRNA) mechanisms-dependent manner. The recovery experiments in hESCs from NE and EE tissues confirmed that lncRNA NEAT1 overexpression promoted NLRP3 inflammasome-mediated pyroptotic cell death through regulating the miR-141-3p/HTRA1 axis. Taken together, this study firstly uncovered the underlying mechanisms by which a novel lncRNA NEAT1/miR-141-3p/HTRA1-NLRP3 pathway contributed to the development of EM, which provided novel diagnostic and therapeutic biomarkers for this disease.

Mechanism Research of Platelet Core Marker Prediction and Molecular Recognition in Cardiovascular Events

BACKGROUND

Thrombosis triggered by platelet activation plays a vital role in the pathogenesis of cardiovascular and cerebrovascular diseases.

OBJECTIVE

This study aims to find platelet combined biomarkers for cardiovascular diseases and investigate the possibility of Concanavalin A (ConA) acting on platelets as a new pharmacological target.

METHODS

High-throughput Technology and bioinformatics analysis were combined and groups of microarray chip gene expression profiles for acute myocardial infarction (AMI) and sickle cell disease (SCD) were obtained using GEO database screening. R language limma package was used to obtain differentially expressed genes (DEGs). GO, KEGG, and other databases were utilized to perform the enrichment analysis of DEGs' functions, pathways, etc. PPI network was constructed using STRING database and Cytoscape software, and MCC algorithm was used to obtain the 200 core genes of the two groups of DEGs. Core targets were confirmed by constructing an intersection area screening. A type of molecular probe, ConA, was molecularly docked with the above core targets on the Zdock, HEX, and 3D-DOCK servers.

RESULTS

We found six core markers, CD34, SOCS2, ABL1, MTOR, VEGFA, and SMURF1, which were simultaneously related to both diseases, and the docking effect showed that VEGFA is the best-performing.

CONCLUSION

VEGFA is most likely to reduce its expression by binding to ConA, which could affect the downstream regulation of the PI3K/Akt signaling pathway during platelet activation. Some other core targets also have the opportunity to interact with ConA to affect platelet-activated thrombosis and trigger changes in cardiovascular events.

The crosstalk between STAT3 and microRNA in cardiac diseases and protection

Signal transducer and activator of transcription 3 (STAT3), an important transcription factor and signaling molecule, play an important role in cardiac disease and protection. As a transcription factor, STAT3 upregulates anti-oxidative and anti-apoptotic genes but suppresses anti-inflammatory and anti-fibrotic genes in cardiac disease and protection. As a signaling molecule, STAT3 is the downstream or upstream of other molecules for signaling transduction, also activated in cardiac disease and protection. MicroRNAs (miRNAs) are endogenous short non-coding RNAs that regulate mRNA expression at the transcriptional level and prevent protein translation. Recently, STAT3 is reported to be not only the target of miRNA but also the inhibitor or inducer of miRNA to modify the mRNA expression profiles in cardiomyocytes resulting in different effects on cardiac disease and protection. We summarize the current knowledge on STAT3 regulation of individual miRNAs and the modulation of STAT3 by miRNAs in cardiac diseases and protection.

Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases with high morbidity and mortality. Numerous studies have indicated that S100A12 may has an essential role in the occurrence and development of AMI, and in-depth studies are currently lacking. The purpose of this study is to investigate the effect of S100A12 on inflammation and oxidative stress and to determine its clinical applicability in AMI. Here, AMI datasets used to explore the expression pattern of S100A12 in AMI were derived from the Gene Expression Omnibus (GEO) database. The pooled standard average deviation (SMD) was calculated to further determine S100A12 expression. The overlapping differentially expressed genes (DEGs) contained in all included datasets were recognized by the GEO2R tool. Then, functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were carried out to determine the molecular function of overlapping DEGs. Gene set enrichment analysis (GSEA) was conducted to determine unrevealed mechanisms of S100A12. Summary receiver operating characteristic (SROC) curve analysis and receiver operating characteristic (ROC) curve analysis were carried out to identify the diagnostic capabilities of S100A12. Moreover, we screened miRNAs targeting S100A12 using three online databases (miRWalk, TargetScan, and miRDB). In addition, by comprehensively using enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT-qPCR), Western blotting (WB) methods, etc., we used the AC16 cells to validate the expression and underlying mechanism of S100A12. In our study, five datasets related to AMI, GSE24519, GSE60993, GSE66360, GSE97320, and GSE48060 were included; 412 overlapping DEGs were identified. Protein-protein interaction (PPI) network and functional analyses showed that S100A12 was a pivotal gene related to inflammation and oxidative stress. Then, S100A12 overexpression was identified based on the included datasets. The pooled standard average deviation (SMD) also showed that S100A12 was upregulated in AMI (SMD = 1.36, 95% CI: 0.70-2.03, p = 0.024). The SROC curve analysis result suggested that S100A12 had remarkable diagnostic ability in AMI (AUC = 0.90, 95% CI: 0.87-0.92). And nine miRNAs targeting S100A12 were also identified. Additionally, the overexpression of S100A12 was further confirmed that it maybe promote inflammation and oxidative stress in AMI through comprehensive in vitro experiments. In summary, our study suggests that overexpressed S100A12 may be a latent diagnostic biomarker and therapeutic target of AMI that induces excessive inflammation and oxidative stress. Nine miRNAs targeting S100A12 may play a crucial role in AMI, but further studies are still needed. Our work provides a positive inspiration for the in-depth study of S100A12 in AMI.

The Key Genes Underlying Pathophysiology Correlation Between the Acute Myocardial Infarction and COVID-19

Introduction

Accumulating evidences disclose that COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a marked effect on acute myocardial infarction (AMI). Nevertheless, the underlying pathophysiology correlation between the AMI and COVID-19 remains vague.

Materials and Methods

Bioinformatics analyses of the altered transcriptional profiling of peripheral blood mononuclear cells (PBMCs) in patients with AMI and COVID-19 were implemented, including identification of differentially expressed genes and common genes between AMI and COVID-19, protein–protein interactions, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, TF-genes and miRNA coregulatory networks, to explore their biological functions and potential roles in the pathogenesis of COVID-19-related AMI.

Conclusion

Our bioinformatic analyses of gene expression profiling of PBMCs in patients with AMI and COVID-19 provide us with a unique view regarding underlying pathophysiology correlation between the two vital diseases.

Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients

Background

Myocardial infarction (MI) is the most terrible appearance of cardiovascular disease. The incidence of heart failure, one of the complications of MI, has increased in the past few decades. Therefore, the identification of MI from angina patients and the determination of new diagnoses and therapies of MI are increasingly important. The present study was aimed at identifying differentially expressed genes and miRNAs as biomarkers for the clinical and prognosis factors of MI compared with angina using microarray data analysis.

Methods

Differentially expressed miRNAs and genes were manifested by GEO2R. The biological function of differentially expressed genes (DEGs) was examined by GO and KEGG. The construction of a protein-protein network was explored by STRING. cytoHubba was utilized to screen hub genes. Analysis of miRNA-gene pairs was executed by the miRWalk 3.0 database. The miRNA-target pairs overlapped with hub genes were seen as key genes. Logistic regressive analysis was performed by SPSS.

Results

A number of 779 DEGs were recorded. The biological function containing extracellular components, signaling pathways, and cell adhesion was enriched. Twenty-four hub genes and three differentially expressed miRNAs were noted. Eight key genes were demonstrated, and 6 out of these 8 key genes were significantly related to clinical and prognosis factors following MI.

Conclusions

CALCA, CDK6, MDM2, NRXN1, SOCS3, VEGFA, SMAD4, NCAM1, and hsa-miR-127-5p were thought to be potential diagnosis biomarkers for MI. Meanwhile, CALCA, CDK6, NRXN1, SMAD4, SOCS3, and NCAM1 were further identified to be potential diagnosis and therapy targets for MI.

Natural Killer Lytic-Associated Molecule (NKLAM): An E3 Ubiquitin Ligase With an Integral Role in Innate Immunity

Natural Killer Lytic-Associated Molecule (NKLAM), also designated RNF19B, is a unique member of a small family of E3 ubiquitin ligases. This 14-member group of ligases has a characteristic cysteine-rich RING-IBR-RING (RBR) domain that mediates the ubiquitination of multiple substrates. The consequence of substrate ubiquitination varies, depending on the type of ubiquitin linkages formed. The most widely studied effect of ubiquitination of proteins is proteasome-mediated substrate degradation; however, ubiquitination can also alter protein localization and function. Since its discovery in 1999, much has been deciphered about the role of NKLAM in innate immune responses. We have discerned that NKLAM has an integral function in both natural killer (NK) cells and macrophages in vitro and in vivo. NKLAM expression is required for each of these cell types to mediate maximal killing activity and cytokine production. However, much remains to be determined. In this review, we summarize what has been learned about NKLAM expression, structure and function, and discuss new directions for investigation. We hope that this will stimulate interest in further exploration of NKLAM.

Screening of a Novel Upregulated lncRNA, A2M-AS1, That Promotes Invasion and Migration and Signifies Poor Prognosis in Breast Cancer

Understanding of prognostic factors and therapeutic targets for breast cancer is imperative for guidance of patient care. We studied 1203 tumour samples from the Gene Expression Omnibus (GEO) to evaluate potential genes related to breast cancer. R software was used to analyse differentially expressed long noncoding RNAs (lncRNAs) in the RNA microarray expression profiles GSE45827 and GSE65216 and to identify a series of differentially expressed lncRNAs associated with human breast cancer. Of these lncRNAs, A2M-AS1, a lncRNA that has not been previously reported, was significantly upregulated in human breast cancer tissues compared with adjacent nontumour tissues. Importantly, A2M-AS1 upregulation was significantly associated with ER-negative, HER2-positive, and basal-like breast cancer and with poor recurrence-free survival and metastasis-free survival in breast cancer patients. After validating these results in 96 collected human breast cancer tissues and 64 paired adjacent noncancerous tissues, we further investigated the roles of A2M-AS1 in human ER-negative and basal-like breast cancer cells. The results revealed that A2M-AS1 significantly promotes human breast cancer cell proliferation, invasion, and migration. Additionally, bioinformatics analysis of genes coexpressed with A2M-AS1 in the context of human breast cancer combined with qRT-PCR and Western blot assays revealed that A2M-AS1 exerts regulatory effects on downstream factors in the cell adhesion molecule pathway, including CD2 and SELL. These results imply that A2M-AS1 might be a promising candidate prognostic factor and therapeutic target for breast cancer.

Genes associated with inflammation may serve as biomarkers for the diagnosis of coronary artery disease and ischaemic stroke

Background

The current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms.

Methods

Two array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (version 6.8; david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients.

Results

A total of 20 common DEGs (all upregulated) were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (JUN, degree = 9), C-X-C motif chemokine ligand 8 (CXCL8, degree = 9), tumour necrosis factor (TNF, degree = 9), suppressor of cytokine signalling 3 (SOCS3, degree = 8) and TNF alpha induced protein 3 (TNFAIP3, degree = 8) were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3.

Conclusions

The CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.

Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis

Background

Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI.

Methods

From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out.

Results

57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8.

Conclusion

This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

A novel lncRNA-miRNA-mRNA triple network identifies lncRNA TWF1 as an important regulator of miRNA and gene expression in coronary artery disease

Background

Long non-coding RNAs (lncRNAs) are involved in numerous physiological functions. Yet, their mechanisms in coronary artery disease (CAD) are not well understood.

Methods

The expression profile of genes associated to CAD was reannotated into the lncRNA-mRNA biphasic profile. The target microRNA data were used to design a global CAD triple network. Thereafter, we conducted a functional enrichment analysis and clustering using the triple network from the level of topology analyses. The expression of four non-coding RNAs (ncRNAs) was measured by qRT-PCR and the risk of CAD was calculated by nomogram. The prognostic value of three ncRNAs was evaluated using receiver operating characteristic (ROC) curve.

Results

A CAD lncRNA-miRNA-mRNA network was constructed which included 15 mRNAs, 3 miRNAs, 19 edges and one lncRNA. Nomogram showed that four ncRNAs were the risk of CAD. After RT-PCR validation in four ncRNAs between CAD and non-CAD samples, only three ncRNAs had significant meaning for further analysis. ROC curve showed that TWF1 presented an area under curve (AUC) of 0.862, the AUC of hsa -miR-142-3p was 0.856 and hsa -miR126-5p was 0.822. After the pairwise comparison, we found that TWF1 had significant statistical significance (P _TWF1-142 < 0.05 and P _TWF1-126 < 0.01). The results of functional enrichment analysis of interacting gene and microRNA showed that the shared lncRNA TWF1 may be a new factor for CAD.

Conclusions

This investigation on the regulatory networks of lncRNA-miRNA-mRNA in CAD suggests that a novel lncRNA, lncRNA TWF1 is a risk factor for CAD, and expands our understanding into the mechanisms involved in the pathogenesis of CAD.