Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis

GFOD1 regulates oxidative stress-induced damage in ADHD via NF-κB signaling pathway

Attention-deficit/hyperactivity disorder (ADHD) is a mental behavioral disorder that poses a serious health risk. Oxidative stress, which damages the function of neurons and astrocytes, has been discovered as a key factor contributing to ADHD pathology. A newly identified gene, Glucose-fructose oxidoreductase domain 1 (GFOD1), may be linked to the development of ADHD. It plays a role in regulating oxidative stress in ADHD; however, its exact role is unclear. This manuscript investigates the changes of GFOD1 expression and aim to correlate this with oxidative stress induced by NF-κB signaling pathway in the rat brains with ADHD and in vitro astrocytes. Our results revealed an increase in GFOD1 expression in the prefrontal cortex and cerebellar cortex of rats with ADHD, accompanied by neuronal injury and increased glial fibrillary acidic protein (GFAP) expression in astrocytes, concomitant with activation of the NF-κB p65/NOX2 signaling pathway. Along with this, GFOD1 overexpression in astrocytes resulted in an up-regulation of this signaling pathway similarly. Both ADHD rats and astrocytes in overexpressing GFOD1 showed elevated levels of reactive oxygen species (ROS) and Malondialdehyde (MDA), reduced activity of superoxide dismutase (SOD). Furthermore, treatment with the methylphenidate (MPH) did not affect GFOD1 expression. But it impacted the levels of oxidative stress mediated by the NF-κB p65/NOX2 signaling pathway. Overall, it is suggested that GFOD1 may contribute to increased levels of oxidative stress specifically in the prefrontal cortex and cerebellar cortex regions and astrocytes affected by ADHD via up-regulation of the NF-κB p65/NOX2/oxidative stress axis.

Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework

Objective

This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD.

Methods

This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP).

Results

The experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model.

Conclusion

This study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.

Molecular insight into the potential functional role of pseudoenzyme GFOD1 via interaction with NKIRAS2

The glucose-fructose oxidoreductase/inositol dehydrogenase/rhizopine catabolism protein (Gfo/Idh/MocA) family includes a variety of oxidoreductases with a wide range of substrates that utilize NAD or NADP as redox cofactor. Human contains two members of this family, namely glucose-fructose oxidoreductase domain-containing protein 1 and 2 (GFOD1 and GFOD2). While GFOD1 exhibits low tissue specificity, it is notably expressed in the brain, potentially linked to psychiatric disorders and severe diseases. Nevertheless, the specific function, cofactor preference, and enzymatic activity of GFOD1 remain largely unknown. In this work, we find that GFOD1 does not bind to either NAD or NADP. Crystal structure analysis unveils that GFOD1 exists as a typical homodimer resembling other family members, but lacks essential residues required for cofactor binding, suggesting that it may function as a pseudoenzyme. Exploration of GFOD1-interacting partners in proteomic database identifies NF-κB inhibitor-interacting Ras-like 2 (NKIRAS2) as one potential candidate. Co-immunoprecipitation (co-IP) analysis indicates that GFOD1 interacts with both GTP- and GDP-bound forms of NKIRAS2. The predicted structural model of the GFOD1-NKIRAS2 complex is validated in cells using point mutants and shows that GFOD1 selectively recognizes the interswitch region of NKIRAS2. These findings reveal the distinct structural properties of GFOD1 and shed light on its potential functional role in cellular processes.

piRNAs as emerging biomarkers and physiological regulatory molecules in cardiovascular disease

Cardiovascular diseases (CVD) represent one of the most considerable global health threats, owing to their high incidence and mortality rates. Despite the ongoing advancements in detection, prevention, treatment, and prognosis of CVD, which have resulted in a decline in both incidence and mortality rates, CVD remains a major public health concern. Therefore, novel diagnostic biomarkers and therapeutic interventions are imperative to minimise the risk of CVD. Non-coding RNAs (ncRNAs) have recently gained increasing attention, with PIWI-interacting RNAs (piRNAs) emerging as a class of small ncRNAs traditionally recognised for their role in silencing transposons within cells. Although the functional roles of PIWI proteins and piRNAs in human cells remain unclear, growing evidence suggests that these molecules are gradually becoming valuable biomarkers for the diagnosis and treatment of CVD. This review provides a comprehensive summary of the latest studies on piRNAs in CVD. This review discusses the roles of piRNAs in various cardiovascular subtypes, including myocardial hypertrophy, heart failure, myocardial infarction, and cardiac regeneration. The perceived insights may contribute novel perspectives for the diagnosis and treatment of CVD.

Propofol regulates the progression of hepatocellular carcinoma via the POLR2L/TGF-β signaling pathway

Background

Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality. Propofol has been reported to modulate tumorigenesis in HCC; the aim of this study was to investigate the effect of the interaction of propofol with POLR2L on HCC tumor progression in HCC.

Methods

The propofol-related GSE101724 dataset was analyzed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) to identify overlapping genes. Key genes were selected from The Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC)-DEGs for prognostic analysis. The impact of POLR2L on LIHC patient survival was assessed, followed by in vitro experiments to validated its effects on HCC cell behavior and signaling pathways.

Results

Fourteen overlapping genes were identified in the turquoise module (highest correlation) of up-regulated DEGs and GSE101724. Further analysis obtained 11 key overlapping genes from 14 overlapping genes and TCGA-LIHC-DEGs, among which HSPE1 and POLR2L showed significant prognostic correlation. Patients with LIHC have a worse chance of surviving when their POLR2L expression is elevated. Knockdown POLR2L significantly inhibited the proliferation, invasion, and migration of HCC cell lines. Downregulation of POLR2L was accompanied by induced apoptosis, cell cycle arrest, and modulation of the expression of apoptosis-related genes. Propofol was found to downregulate POLR2L expression, inhibiting cell proliferation and growth. Further, it was shown that propofol controlled the development of HCC by influencing the POLR2L/TGF-β signaling loop.

Conclusions

The results validated the predictive relevance of POLR2L in HCC and emphasized that propofol can regulate HCC progression through the POLR2L/TGF-β signaling pathway.

Differentially expressed platelet activation-related genes in dogs with stage B2 myxomatous mitral valve disease

BACKGROUND

Peripheral blood carries a reservoir of mRNAs that regulate cardiac structure and function potential. Although it is well recognized that the typical symptoms of Myxomatous Mitral Valve Disease (MMVD) stage B2 are long-standing hemodynamic disorder and cardiac structure remodeling caused by mitral regurgitation, the transcriptomic alterations in blood from such dogs are not understood.

RESULTS

In the present study, comparative high-throughput transcriptomic profiling of blood was performed from normal control (NC) and naturally-occurring MMVD stage B2 (MMVD) dogs. Using Weighted Gene Co-expression Network Analyses (WGCNA), Gene Ontology (GO), and Kyoto Encyclopedia of Gene and Genomes (KEGG), we identified that the turquoise module was the most highly correlated with echocardiographic features and found 64 differentially expressed genes (DEGs) that were significantly enriched in platelet activation related pathways. Therefore, from the turquoise module, we selected five DEGs (MDM2, ROCK1, RIPK1, SNAP23, and ARHGAP35) that, according to real-time qPCR, exhibited significant enrichment in platelet activation related pathways for validation. The results showed that the blood transcriptional abundance of MDM2, ROCK1, RIPK1, and SNAP23 differed significantly (P < 0.01) between NC and MMVD dogs. On the other hand, Correlation Analysis revealed that MDM2, ROCK1, RIPK1, and SNAP23 genes negatively regulated the heart structure parameters, and followed the same trend as observed in WGCNA.

CONCLUSION

We screened four platelet activation related genes, MDM2, ROCK1, RIPK1, and SNAP23, which may be considered as the candidate biomarkers for the diagnosis of MMVD stage B2. These findings provided new insights into MMVD pathogenesis.

Clinical value of combined plasma brain natriuretic peptide and serum cystatin C measurement on the prediction of heart failure in patients after acute myocardial infarction

This research investigated the predictive value of combined detection of brain natriuretic peptide (BNP) and cystatin C (Cys C) in heart failure after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI). Sixty-five AMI patients complicated by heart failure (HF) after PCI and 79 non-heart failure (non-HF) patients were involved in this research. The levels of Cys C and BNP were measured. Risk factors for heart failure in AMI patients after PCI were analyzed by multivariate logistic regression analysis. Efficacy of BNP and Cys C on predicting heart failure were analyzed by receiver operating characteristic (ROC) curve. Cys C and BNP levels were significantly higher in the HF group than in the non-HF group. BNP and Cys C levels were the independent influencing factors causing heart failure within one year after PCI. The area under the predicted curve (AUC) of Cys C, BNP, and combined Cys C and BNP were 0.763, 0.829, and 0.893, respectively. The combined detection of Cys C and BNP was highly valuable in predicting heart failure in AMI patients after PCI, which can be regarded as the serum markers for diagnosis and treatment of heart failure.

Bioinformatics analysis and identification of hub genes associated with female acute myocardial infarction patients by using weighted gene co-expression networks

To explore potential biomarkers of acute myocardial infarction (AMI) in females by using bioinformatics analysis. In this study, we explored potential biomarkers of AMI in females using bioinformatics analysis. We screened a total of 186 differentially expressed genes from the Gene Expression Omnibus. In the study, we found that weighted gene co-expression network analysis explored the co-expression network of genes and identified key modules. Simultaneously, we chose brown modules as key modules related to AMI. In this study, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that genes in the brown module were mainly enriched in "heparin" and 'complementation and coagulation cascade. Based on the protein-protein interaction network, we identified S100A9, mitogen-activated protein kinase (MAPK) 3, MAPK1, MMP3, interleukin (IL)-17A, and HSP90AB1 as hub gene sets. Whereas, polymerase chain reaction results showed that S100A9, MAPK3, MAPK1, MMP3, IL-17A, and HSP90AB1 were highly expressed compared with the control group. The IL-17 signaling pathway associated with an inflammatory response may be a potential biomarker and target for the treatment of women with myocardial infarction.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach

Acute myocardial infarction (AMI) is a severe disease with elevated morbidity and mortality rate worldwide. This is attributed to great losses of cardiomyocytes, which can trigger the alteration of gene expression patterns. Although several attempts have been made to assess the AMI biomarkers, to date their role in rescuing myocardial injury remains unclear. Therefore, the current study investigated three independent microarray-based gene expression datasets from AMI patients (n = 85) and their age-sex-matched healthy controls (n = 70), to identify novel gene signatures that might be involved in cardioprotection. The differentially expressed genes (DEGs) were analyzed using 'GEO2R', and weighted gene correlation network analysis (WGCNA) was performed to identify biomarkers/modules. We found 91 DEGs, of which the number of upregulated and downregulated genes were 22 and 5, respectively. Specifically, we found that the deregulated genes such as ADOR-A3, BMP6, VPS8, and GPx3, may be associated with AMI. WGCNA revealed four highly preserved modules among all datasets. The 'Enrichr' unveiled the presence of miR-660 and STAT1, which is known to affect AMI severity. Conclusively, these genes and miRNA might play a crucial role the rescue of cardiomyocytes from severe damage, which could be helpful in developing appropriate therapeutic strategies for the management of AMI.

Analysis of Time Series Gene Expression and DNA Methylation Reveals the Molecular Features of Myocardial Infarction Progression

Myocardial infarction (MI) is one of the deadliest diseases in the world, and the changes at the molecular level after MI and the DNA methylation features are not clear. Understanding the molecular characteristics of the early stages of MI is of significance for the treatment of the disease. In this study, RNA-seq and MeDIP-seq were performed on heart tissue from mouse models at multiple time points (0 h, 10 min, 1, 6, 24, and 72 h) to explore genetic and epigenetic features that influence MI progression. Analysis based on a single point in time, the number of differentially expressed genes (DEGs) and differentially methylated regions (DMRs) increased with the time of myocardial infarction, using 0 h as a control group. Moreover, within 10 min of MI onset, the cells are mainly in immune response, and as the duration of MI increases, apoptosis begins to occur. Analysis based on time series data, the expression of 1012 genes was specifically downregulated, and these genes were associated with energy metabolism. The expression of 5806 genes was specifically upregulated, and these genes were associated with immune regulation, inflammation and apoptosis. Fourteen transcription factors were identified in the genes involved in apoptosis and inflammation, which may be potential drug targets. Analysis based on MeDIP-seq combined with RNA-seq methodology, focused on methylation at the promoter region. GO revealed that the downregulated genes with hypermethylation at 72 h were enriched in biological processes such as cardiac muscle contraction. In addition, the upregulated genes with hypomethylation at 72 h were enriched in biological processes, such as cell-cell adhesion, regulation of the apoptotic signaling pathway and regulation of angiogenesis. Among these genes, the Tnni3 gene was also present in the downregulated model. Hypermethylation of Tnni3 at 72 h after MI may be an important cause of exacerbation of MI.

Transcription factor JDP2 activates PDE4B to participate in hypoxia/reoxygenation‑induced H9c2 cell injury

Myocardial ischemia/reperfusion (I/R) injury is a clinical challenge in the treatment of acute myocardial infarction (AMI). Phosphodiesterase 4B (PDE4B) expression is upregulated in AMI tissues. Thus, the present study aimed to investigate the role of PDE4B in myocardial I/R injury. H9c2 cardiomyocytes were subjected to hypoxia/reoxygenation (H/R) to establish an in vitro myocardial I/R model. PDE4B expression was detected via reverse transcription-quantitative PCR (RT-qPCR) and western blotting before and after transfection with PDE4B interference plasmids in H/R-stimulated H9c2 cells. Cell viability and cytotoxicity were assessed using the Cell Counting Kit-8 and lactate dehydrogenase assays, respectively. Furthermore, oxidative stress was assessed using malondialdehyde, superoxide dismutase and glutathione/glutathione oxidized ratio detection kits. Cell apoptosis was detected via a TUNEL assay and western blotting. c-Jun dimerization protein 2 (JDP2) expression was also detected via RT-qPCR and western blotting. The dual luciferase reporter and chromatin immunoprecipitation assays were performed to verify the interaction between JDP2 and PDE4B. Following co-transfection with PDE4B interference plasmid and JDP2 overexpression plasmid, cell viability, cytotoxicity, oxidative stress and cell apoptosis were assessed. The results demonstrated that PDE4B knockdown reversed H/R-induced loss of viability and cytotoxicity of H9c2 cells. H/R-induced oxidative stress and cardiomyocyte apoptosis were also alleviated by PDE4B knockdown. In addition, the transcription factor JDP2 was expressed at high levels in H/R-stimulated H9c2 cells, and JDP2 overexpression upregulated PDE4B expression. Notably, JDP2 overexpression partly reversed the ameliorative effect of PDE4B knockdown on H/R-induced H9c2 injury. Taken together, the results of the present study suggested that JDP2-activated PDE4B contributed to H/R-induced H9c2 cell injury.

Integrated RNA gene expression analysis identified potential immune-related biomarkers and RNA regulatory pathways of acute myocardial infarction

Background

Acute lesions are among the most important causes of death due to vascular lesions worldwide. However, there are no accurate genetic markers for Acute myocardial infarction (AMI). This project will use microarray integration analysis in bioinformatics analysis to find and validate relevant AMI gene markers.

Methods

Five microarray gene expression datasets were downloaded through the GEO database. We identified 50 significant DEGs by comparing and analyzing gene expression between 92 AMI and 57 standard samples. The BioGPS database screened differentially expressed genes specific to the immune system. DEGs were mainly involved in immune-related biological processes based on Enrichment analysis. Eight hub genes and three-gene cluster modules were subsequently screened using Cytoscape and validated using Box plot’s grouping comparison and ROC curves. Combined group comparison results and ROC curves analysis concluded that AQP9, IL1B, and IL1RN might be potential gene markers for the AMI process. We used the StarBase database to predict target miRNAs for eight essential genes. The expected results were used to screen and obtain target lncRNAs. Then Cytoscape was used to create CeRNA networks. By searching the literature in PubMed, we concluded that AQP9, IL1B, and IL1RN could be used as gene markers for AMI, while FSTL3-miR3303p-IL1B/IL1RN and ACSL4-miR5905p-IL1B could be used as RNA regulatory pathways affecting AMI disease progression.

Conclusions

Our study identified three genes that may be potential genetic markers for AMI’s early diagnosis and treatment. In addition, we suggest that FSTL3-miR-330-3p-IL1B/IL1RN and ACSL4-miR-590-5p-IL1B may be possible RNA regulatory pathways to control AMI disease progression.

CharID: a two-step model for universal prediction of interactions between chromatin accessible regions

Open chromatin regions (OCRs) allow direct interaction between cis-regulatory elements and trans-acting factors. Therefore, predicting all potential OCR-mediated loops is essential for deciphering the regulation mechanism of gene expression. However, existing loop prediction tools are restricted to specific anchor types. Here, we present CharID (Chromatin Accessible Region Interaction Detector), a two-step model that combines neural network and ensemble learning to predict OCR-mediated loops. In the first step, CharID-Anchor, an attention-based hybrid CNN-BiGRU network is constructed to discriminate between the anchor and nonanchor OCRs. In the second step, CharID-Loop uses gradient boosting decision tree with chromosome-split strategy to predict the interactions between anchor OCRs. The performance was assessed in three human cell lines, and CharID showed superior prediction performance compared with other algorithms. In contrast to the methods designed to predict a particular type of loops, CharID can detect varieties of chromatin loops not limited to enhancer-promoter loops or architectural protein-mediated loops. We constructed the OCR-mediated interaction network using the predicted loops and identified hub anchors, which are highlighted by their proximity to housekeeping genes. By analyzing loops containing SNPs associated with cardiovascular disease, we identified an SNP-gene loop indicating the regulation mechanism of the GFOD1. Taken together, CharID universally predicts diverse chromatin loops beyond other state-of-the-art methods, which are limited by anchor types, and experimental techniques, which are limited by sensitivities drastically decaying with the genomic distance of anchors. Finally, we hosted Peaksniffer, a user-friendly web server that provides online prediction, query and visualization of OCRs and associated loops.

The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis

Acute myocardial infarction (AMI), one of the most severe and fatal cardiovascular diseases, remains the main cause of mortality and morbidity worldwide. The objective of this study is to investigate the potential biomarkers for AMI based on bioinformatics analysis. A total of 2102 differentially expressed genes (DEGs) were screened out from the data obtained from the gene expression omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) explored the co-expression network of DEGs and determined the key module. The brown module was selected as the key one correlated with AMI. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses demonstrated that genes in the brown module were mainly enriched in ‘ribosomal subunit’ and ‘Ribosome’. Gene Set Enrichment Analysis revealed that ‘TNFA_SIGNALING_VIA_NFKB’ was remarkably enriched in AMI. Based on the protein–protein interaction network, ribosomal protein L9 (RPL9) and ribosomal protein L26 (RPL26) were identified as the hub genes. Additionally, the polymerase chain reaction (PCR) results indicated that the expression levels of RPL9 and RPL26 were both downregulated in AMI patients compared with controls, in accordance with the bioinformatics analysis. In summary, the identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of AMI.

Potential biomarkers of acute myocardial infarction based on co‑expression network analysis

Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in ‘RAGE receptor binding’ and ‘negative regulation of T cell cytokine production’. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in ‘cardiovascular diseases’, of AKR1C3 in ‘cardenolide biosynthesis’, of MGAM in ‘glycogenolysis’, of B3GNT5 in ‘glycosphingolipid biosynthesis’ and of ACSL1 in ‘icosapentaenoate biosynthesis II’. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.

Identification of key molecular markers of acute coronary syndrome using peripheral blood transcriptome sequencing analysis and mRNA-lncRNA co-expression network construction

Acute coronary syndrome (ACS) is a term used to describe major cardiovascular diseases, and treatment of in-stent restenosis in patients with ACS remains a major clinical challenge. Further investigation into molecular markers of ACS may aid early diagnosis, and the treatment of ACS and post-treatment recurrence. In the present study, total RNA was extracted from the peripheral blood samples of 3 patients with ACS, 3 patients with percutaneous coronary intervention (PCI)_non-restenosis, 3 patients with PCI_restenosis and 3 healthy controls. Subsequently, RNA library construction and high-throughput sequencing were performed. DESeq2 package in R was used to screen genes that were differentially expressed between the different samples. Moreover, the intersection of the differentially expressed mRNAs (DEmRNAs) and differentially expressed long noncoding RNAs (DElncRNAs) obtained. GeneCodis4.0 was used to perform function enrichment for DEmRNAs, and lncRNA-mRNA co-expression network was constructed. The GSE60993 dataset was utilized for diagnostic analysis, and the aforementioned investigations were verified using in vitro studies. Results of the present study revealed a large number of DEmRNAs and DElncRNAs in the different groups. We selected genes in the top 10 of differential expression and also involved in the co-expression of lncRNA-mRNA for diagnostic analysis in the GSE60993 dataset. The area under curve (AUC) of PDZK1IP1 (0.747), PROK2 (0.769) and LAMP3 (0.725) were all >0.7. These results indicated that the identified mRNAs and lncRNAs may act as potential clinical biomarkers, and more specifically, PDZK1IP1, PROK2 and LAMP3 may act as potential biomarkers for the diagnosis of ACS.

Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the “limma” package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and “cytoHubba” were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

Bioinformatic analysis for potential biological processes and key targets of heart failure-related stroke

This study aimed to uncover underlying mechanisms and promising intervention targets of heart failure (HF)-related stroke. HF-related dataset GSE42955 and stroke-related dataset GSE58294 were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub genes. Gene Ontology (GO) and pathway enrichment analyses were performed on genes in the key modules. Genes in HF- and stroke-related key modules were intersected to obtain common genes for HF-related stroke, which were further intersected with hub genes of stroke-related key modules to obtain key genes in HF-related stroke. Key genes were functionally annotated through GO in the Reactome and Cytoscape databases. Finally, key genes were validated in these two datasets and other datasets. HF- and stroke-related datasets each identified two key modules. Functional enrichment analysis indicated that protein ubiquitination, Wnt signaling, and exosomes were involved in both HF- and stroke-related key modules. Additionally, ten hub genes were identified in stroke-related key modules and 155 genes were identified as common genes in HF-related stroke. OTU deubiquitinase with linear linkage specificity(OTULIN) and nuclear factor interleukin 3-regulated(NFIL3) were determined to be the key genes in HF-related stroke. Through functional annotation, OTULIN was involved in protein ubiquitination and Wnt signaling, and NFIL3 was involved in DNA binding and transcription. Importantly, OTULIN and NFIL3 were also validated to be differentially expressed in all HF and stroke groups. Protein ubiquitination, Wnt signaling, and exosomes were involved in HF-related stroke. OTULIN and NFIL3 may play a key role in HF-related stroke through regulating these processes, and thus serve as promising intervention targets.

FCER1G and PTGS2 Serve as Potential Diagnostic Biomarkers of Acute Myocardial Infarction Based on Integrated Bioinformatics Analyses

This study aimed to explore the potential diagnostic biomarkers and mechanisms underlying acute myocardial infarction (AMI). We downloaded four datasets (GSE19339, GSE48060, GSE66360, and GSE97320) from the Gene Expression Omnibus database and combined them as an integrated dataset. A total of 153 differentially expressed genes (DEGs) were analyzed by the linear models for microarray analysis (LIMMA) package. Weighted gene co-expression network analysis was used to screen for the significant gene modules. The intersection of DEGs and genes in the most significant module was termed "common genes" (CGs). CGs were mainly enriched in "inflammatory response," "neutrophil chemotaxis," and "IL-17 signaling pathway" through functional enrichment analyses. Subsequently, 15 genes were identified as the hub genes in the protein-protein interaction network. The Fc fragment of IgE receptor Ig (FCER1G) and prostaglandin-endoperoxide synthase 2 (PTGS2) showed significantly increased expression in AMI patients and mice at the 12-h time point in our experiments. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of FCER1G and PTGS2. The area under ROC curve of FCER1G and PTGS2 was 77.6% and 80.7%, respectively. Moreover, the micro (mi)RNA-messenger (m)RNA network was also visualized; the results showed that miRNA-143, miRNA-144, and miRNA-26 could target PTGS2 in AMI progression.

Study on the interaction mechanism between C-reactive protein and platelets in the development of acute myocardial infarction

Background

Myocardial infarction (MI) is the single most critical event in coronary disease. Platelets are involved in the processes of acute MI (AMI). They lack nuclear DNA but retain megakaryocyte mRNAs, hence, their transcriptome could provide information preceding coronary events. However, their mechanisms are not clear. In this study, we obtained a gene expression atlas of platelets from patients after their very first AMI, and our purpose was to clarify the mechanisms of platelet involvement in the occurrence of AMI through bioinformatics analyses and animal models of AMI in vivo.

Methods

We obtained a gene expression atlas of platelets from patients after their very first AMI from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were retrieved using R language. Weighted gene co-expression network analysis (WGCNA) was implemented in order to construct a gene co-expression correlation network among DEGs. Animal models of AMI in vivo were constructed to confirm the results of the bioinformatics analysis.

Results

Gene integration analysis yielded 2,852 DEGs (P<0.05, |log2FC| >1). Bioinformatics analysis demonstrated a significant association between C-reactive protein (CRP) and Staphylococcus aureus infection (SAI) (P=0.015). Data from in vivo experiments showed that CRP increased significantly in AMI rats (P<0.001), and the expression of FCGR2B mRNA and HLA-DRB4 mRNA was elevated in response to the increase of CRP (P<0.001).

Conclusions

From the results of this study, we speculate that in the development of AMI, the increase in CRP activates platelets and induces platelets to play an anti-inflammatory role.

Identification of key gene modules and pathways of human platelet transcriptome in acute myocardial infarction patients through co-expression network

Acute myocardial infarction (AMI) seriously threatens human life. In this study we aimed to systemically analyze the function of key gene modules in human platelets in AMI. We used weighted gene co-expression network analysis (WGCNA) to construct a co-expression module, and analyzed the relationship between potential modules and clinical characteristics based on platelet RNA-seq RPKM count reads of 16 ST-segment elevation myocardial infarction (STEMI) patients and 16 non-STEMI (NSTEMI) patients provided by the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed with the DAVID tool. Hub genes were calculated by the Cytohubba package. A total of 3653 genes was selected to construct the co-expression modules. A significant correlation between BMI and the module with color of sky-blue in STEMI. In NSTEMI, there was a significant correlation between the sky blue module and CAD, the Salmon module and HT, and the Cyan module and HT. In STEMI, the Hub genes were mainly enriched in functions related to cell membrane signal transduction including Aqp1, Armcx1, Gsta4, Hist3h2a and Il17re. In NSTEMI, the Hub genes are related mainly to energy metabolism in the sky-blue module including Olr1, Nap1l3, Gfer, Dohh, Crispld1 and Ccdc8b; they are mainly related to extracellular space and calcium binding in the Cyan module, including Clec12b, Chd4, Asgr1, Armcx4, Chid1 and Alkbh7. The hub genes in the Salmon module include Ell3, Aldh1b1, Cavin4, Cabp4, Eif1ay and Dus3l. Our results provide a framework for co-expression gene modules in STEMI and NSTEMI patients, and identify key targets as biomarkers for patients with different subtypes of AMI.

HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BACKGROUND

Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH.

METHODS

The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve.

RESULTS

Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14.

CONCLUSIONS

Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

Biomarkers for Heart Failure Prognosis: Proteins, Genetic Scores and Non-coding RNAs

Heart failure (HF) is a complex disease in which cardiomyocyte injury leads to a cascade of inflammatory and fibrosis pathway activation, thereby causing decrease in cardiac function. As a result, several biomolecules are released which can be identified easily in circulating body fluids. The complex biological processes involved in the development and worsening of HF require an early treatment strategy to stop deterioration of cardiac function. Circulating biomarkers provide not only an ideal platform to detect subclinical changes, their clinical application also offers the opportunity to monitor disease treatment. Many of these biomarkers can be quantified with high sensitivity; allowing their clinical application to be evaluated beyond diagnostic purposes as potential tools for HF prognosis. Though the field of biomarkers is dominated by protein molecules, non-coding RNAs (microRNAs, long non-coding RNAs, and circular RNAs) are novel and promising biomarker candidates that encompass several ideal characteristics required in the biomarker field. The application of genetic biomarkers as genetic risk scores in disease prognosis, albeit in its infancy, holds promise to improve disease risk estimation. Despite the multitude of biomarkers that have been available and identified, the majority of novel biomarker candidates are not cardiac-specific, and instead may simply be a readout of systemic inflammation or other pathological processes. Thus, the true value of novel biomarker candidates in HF prognostication remains unclear. In this article, we discuss the current state of application of protein, genetic as well as non-coding RNA biomarkers in HF risk prognosis.

Distribution of transcripts of the GFOD gene family members gfod1 and gfod2 in the zebrafish central nervous system

The glucose-fructose oxidoreductase domain containing gene family (GFOD) is small and contains only two members in human (GFOD1 and GFOD2). Information about its function is scarce. As the name implies the proteins contain an enzyme-defining domain, however, if this is functional and has enzymatic activity remains to be shown. A single nucleotide polymorphism situated in an intron of GFOD1 was found to be associated with inattentive symptomology in patients with attention-deficit/hyperactivity disorder. Further, in a large schizophrenia genome-wide association study the GFOD2 locus was found to be associated with the psychiatric condition. Until now, however, it is unclear what specific functions are associated with the two GFOD-family members, if they might be involved in neurodevelopment and how this may relate to the development of psychiatric disorders. In order to gain first insights into the hypothesis that GFOD-family members are involved in brain development and/or function we performed RNA in situ hybridization on zebrafish (Danio rerio) tissues at different developmental stages. We found that both family members are expressed in the central nervous system at embryonic, larvae and adult stages. We were able to define subtle differences of expression of the two gfod genes and we showed that a subset of GABAergic neurons express gfod1. Taken together, we conclude that both gfod gene family members are expressed in overlapping as well as in distinct regions in the zebrafish central nervous system. Our data suggest that gfod1 and gfod2 are relevant both for the developing and adult zebrafish brain. This study paves the way for further functional analyses of this yet unexplored gene family.