Gene expression profiling reveals potential prognostic biomarkers associated with the progression of heart failure

Potential of plasma biomarkers for heart failure prediction, management, and prognosis: A multiomics perspective

Heart failure (HF) remains a major global health challenge, and more effective and comprehensive plasma biomarkers are needed to effectively treat HF patients. Multiomics studies have shown that DNA fragments, noncoding RNAs, proteins, and metabolites may be potential plasma biomarkers for HF. However, comprehensive reviews that focus on research on plasma biomarkers for HF from an omics perspective are lacking. This review summarizes the applications of various omics approaches in the exploration of biomarkers related to the risk assessment, diagnosis, subtype classification, medical management, and prognosis prediction of HF. Moreover, as heart transplantation and left ventricular assistant device (LVAD) implantation are terminal therapies for end-stage HF patients, this review also discusses the role of cell-free DNA as a biomarker for cardiac transplant rejection and omics studies of plasma biomarkers in patients who respond to LVAD therapy. Our findings suggest that future omics research on HF biomarkers should employ integrated multiomics methods and expand the sample size to increase the robustness of the results and that the identified biomarkers should be further validated in large cohorts.

Disulfidptosis-related gene in acute myocardial infarction and immune microenvironment analysis: A bioinformatics analysis and validation

Disulfidptosis is a newly discovered method of cell death. However, no studies have fully elucidated the role of disulfidptosis-related genes (DSRGs) in acute myocardial infarction (AMI). The potential role of DSRGs in AMI was analyzed through a comprehensive bioinformatics approach. Finally, hub genes were verified in vitro by qPCR. Sixteen DE-DSRGs were in the AMI. Thereafter, seven hub genes were determined by machine learning algorithms, which had potential diagnostic value in AMI. The risk model showed a robust diagnostic value (area under curve, AUC = 0.940). Prognostic analysis revealed the potential prognostic value of INF2 and CD2AP. Immune landscape analysis showed that hub genes were closely related to the immune microenvironment. By predictive analysis, we obtained four miRNAs, thirteen small molecule drugs, and five TFs closely related to hub genes. Experimental verification revealed that Slc3a2 and Inf2 were significantly up-regulated and Dstn was significantly down-regulated in the hypoxic model. Our study demonstrated that DSRGs are disorderedly expressed in AMI and identified seven hub genes through machine learning. In addition, a diagnostic model was constructed based on hub genes, providing a new perspective for the early diagnosis of AMI.

Bioinformatics analysis of the role of cuproptosis gene in acute myocardial infarction

BACKGROUND

Immune infiltration plays a vital role in the course of acute myocardial infarction (AMI). Cuproptosis is a new type of programmed cell death discovered recently. Currently, there is no study on the mechanism of cuproptosis gene regulating immune infiltration in AMI. Therefore, by integrating cuproptosis-related genes and GEO database-related microarray data, this study analyzed the association between cuproptosis genes and immune infiltration and built a risk model.

METHODS

The GSE59867 was used to extract cuproptosis gene expression profile. The R limma package was used to analyze the differentially expressed genes associated with AMI-Cuproptosis. The risk model was constructed according to AMI-cuproptosis differentially expressed genes. Prediction of AMI-cuproptosis-related gene drugs through Coremine Medical database. The upstream miRNAs were predicted using miRWalk, TargetScan, and miRDB libraries, and a miRNA-mRNA network was constructed.

RESULTS

Cuproptosis-related genes (DLST, LIAS, DBT, ATP7A, LIPT1, PDHB, GCSH, DLD, DLAT) were down-regulated in AMI patients. One (ATP7B) gene was up-regulated in AMI patients (P<0.05). These 10 Cuproptosis-related genes were significantly associated with immune cell infiltration. Based on these 10 differential genes, the AMI risk prediction model was constructed, and the AUC value was 0.825, among which the abnormal expression of DLST was a risk factor for AMI. Additionally, we also predicted DLAT upstream miRNAs and associated drug targets, finding that 9 miRNAs were upstream of DLST.

CONCLUSIONS

DLST is a potential cuproptosis gene associated with AMI, but its specific mechanism remains unclear and requires further investigation in future studies.

Screening for Heart Failure: Biomarkers to Detect Heightened Risk in the General Population

PURPOSE OF REVIEW

Heart failure (HF) represents a growing global burden of morbidity and mortality. Identifying individuals at risk for HF development is increasingly important, particularly given the advent of disease-modifying therapies for HF as well as its major risk factors such as obesity actalnd diabetes. We aim to review the key circulating biomarkers associated with future HF which may contribute to HF risk prediction.

RECENT FINDINGS

While current guidelines recommend the use of natriuretic peptides and cardiac troponins in HF risk stratification, there are a diverse array of other emerging protein, metabolic, transcriptomic, and genomic biomarkers of future HF development. These biomarkers not only lend insight into the underlying pathophysiology of HF, which spans inflammation to cardiac fibrosis, but also offer an opportunity to further refine HF risk in addition to established biomarkers. As evolving techniques in molecular biology enable an increased understanding of the complex biologic contributions to HF pathophysiology, there is an important opportunity to construct integrated clinical and multi-omic models to best capture HF risk. Moving forward, future studies should seek to understand the contributions of sex differences, underlying comorbidity burden, and HF subtypes to an individual's HF risk. Further studies are necessary to fully define the clinical utility of biomarker screening approaches to refine HF risk assessment, as well as to link risk assessment directly to preventive strategies for HF.

Identification and characterization of novel ferroptosis-related genes in acute myocardial infarction

BACKGROUND

Acute myocardial infarction (AMI) is a leading cause of death and morbidity worldwide. Ferroptosis, a form of regulated cell death, plays a critical role in modulating immune functions during AMI. This study aimed to identify ferroptosis-related hub genes that could serve as potential therapeutic targets in the progression of AMI.

METHODS

Bioinformatics was used to identify overlapping genes associated with ferroptosis and the infiltration of 22 immune cells by Cell-type Identification by Estimating Relative Subsets of RNA Transcript (CIBERSORT) analysis. The expression of ferroptosis-related genes in AMI was validated across independent datasets, clinical samples, and in vitro cellular experiments. The predictive value for heart failure was evaluated in the first dimension of principal component analysis (PCA) using receiver operating characteristic (ROC) analysis.

RESULTS

The study identified 11 key ferroptosis-related genes significantly correlated with immune cell abundance. CIBERSORT analysis highlighted immune dysregulation in AMI. JDP2, DUSP1, TLR4, NFS1, and SLC1A5 were identified as potential biomarkers for AMI progression. Additionally, JDP2, DUSP1, and DDIT4 demonstrated strong predictive value for long-term heart failure.

CONCLUSION

This study highlights the potential association of ferroptosis-related genes with the pathogenesis of AMI, suggesting a role in the molecular mechanisms that may underlie acute coronary events.

Effect of Methyl Glycoside on Apoptosis and Oxidative Stress in Hypoxia Induced-Reoxygenated H9C2 Cell Lines

This study focuses on key genes (Caspase-3, JAK2, BCL2L1 and MAPK8) and their modulation in response to hypoxia-induced stress using Methyl Glycoside (MG), a small molecule spectroscopically screened from Aganosma dichotoma. Hypoxia/reoxygenation (H/R) induced H9C2 cells, pre- treated with MG, were subjected to cell viability assay, free radical scavenging activities (catalase, GST, GSH-Px, SOD), caspase activity, mitochondrial membrane potential, and gene expression profiling through standard assays and molecular techniques. Results indicated that MG treatment, has potential protective effects against H/R induced stress in H9C2 cell lines. Cell viability assays showed that MG maintained cellular viability with significant protection (P < 0.05) observed from 10 µM. Free radical scavenging assays revealed that MG, enhanced detoxification mechanisms and exhibited potential antioxidant effect in a significantly (P < 0.05) in a dose dependant manner. MG pre-treatment in H9C2 cells protected cellular damage from caspase activity, cells exhibited high mitochondrial membrane potential, and gene expression profiles, including upregulation of anti-apoptotic BCL2L1 and modulation of stress-responsive genes like CASP3, JAK2 and MAPK8. Hence, MG exhibited concentration-dependent protective effects on viability, oxidative stress, and apoptosis-related pathways, laying the foundation for further exploration and translational applications in cardiovascular interventions.

Regulatory network analysis based on integrated miRNA-TF reveals key genes in heart failure

The etiology and pathophysiology of heart failure are still unknown. Increasing evidence suggests that abnormal microRNAs (miRNAs) and transcription factors (TFs) expression may be associated with the development of heart failure. Therefore, this study aims to explore key miRNAs, TFs, and related genes in heart failure to gain a greater understanding of the pathogenesis of heart failure. To search and download the dataset of mRNA chips related to heart failure from the GEO database (GSE59867, GSE9128, and GSE134766), we analyzed differential genes and screened the common differentially expressed genes on two chips using R language software. The binary interactions and circuits among miRNAs, TFs, and corresponding genes were determined by Pearson correlation coefficient. A regulatory network of miRNAs, TFs, and target genes was constructed based on bioinformatics. By comparing the sequences of patients with and without heart failure, five downregulated genes with hypermethylated mRNA and three upregulated genes with hypomethylated mRNA were identified. The miRNA-TF gene regulatory network consisted of 26 miRNAs, 22 TFs and six genes. GO and KEGG analysis results revealed that BP terms like cellular response to organic substance, cellular response to cytokine stimulus, and KEGG pathways like osteoclast differentiation, MAPK signaling pathway, and legionellosis were enriched of the DEGs. TMEM87A, PPP2R2A, DUSP1, and miR-92a have great potential as biomarkers for heart failure. The integrated analysis of the mRNA expression spectrum and microRNA-transcription factor-gene revealed the regulatory network of heart failure, which may provide clues to its alternative treatment.

Prognostic Value of a Multi-mRNA Signature for 1-Year All-Cause Death in Hospitalized Patients With Heart Failure With a Preserved Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction is a major global public health problem, while effective risk stratification tools are still lacking. We sought to construct a multi-mRNA signature to predict 1-year all-cause death.

METHODS

We selected 30 patients with heart failure with preserved ejection fraction who died during 1-year follow-up and 30 who survived in the discovery set. One hundred seventy-one and 120 patients with heart failure with preserved ejection fraction were randomly selected as a test set and a validation set, respectively. We performed mRNA microarrays in all patients.

RESULTS

We constructed a 5-mRNA signature for predicting 1-year all-cause death. The scores of the 5-mRNA signature were significantly associated with the 1-year risk of all-cause death in both the test set (hazard ratio, 2.72 [95% CI, 1.98-3.74]; P<0.001) and the validation set (hazard ratio, 3.95 [95% CI, 2.40-6.48]; P<0.001). Compared with a reference model, which included sex, ASCEND-HF (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) score, history of HF and NT-proBNP (N-terminal pro-B-type natriuretic peptide), the 5-mRNA signature had a better discrimination capability, with an increased area under the curve from 0.696 to 0.813 in the test set and from 0.712 to 0.848 in the validation set. A composite model integrating the 5-mRNA risk score and variables in the reference model demonstrated an excellent discrimination capability, with an area under the curve of 0.861 (95% CI, 0.784-0.939) in the test set and an area under the curve of 0.859 (95% CI, 0.755-0.963) in the validation set. The net reclassification improvement and integrated discrimination improvement indicated that the composite model significantly improved patient classification compared with the reference model in both sets (P<0.001).

CONCLUSIONS

The 5-mRNA signature is a promising predictive tool for 1-year all-cause death and shows improved prognostic power over the established risk scores and NT-proBNP in patients with heart failure with preserved ejection fraction.

Identification of Potential Crucial Biomarkers in STEMI Through Integrated Bioinformatic Analysis

BACKGROUND

ST-segment elevation myocardial infarction (STEMI) is one of the leading causes of fatal cardiovascular diseases, which have been the prime cause of mortality worldwide. Diagnosis in the early phase would benefit clinical intervention and prognosis, but the exploration of the biomarkers of STEMI is still lacking.

OBJECTIVES

In this study, we conducted a bioinformatics analysis to identify potential crucial biomarkers in the progress of STEMI.

METHODS

We obtained GSE59867 for STEMI and stable coronary artery disease (SCAD) patients. Differentially expressed genes (DEGs) were screened with the threshold of |log2fold change| > 0.5 and p <0.05. Based on these genes, we conducted enrichment analysis to explore the potential relevance between genes and to screen hub genes. Subsequently, hub genes were analyzed to detect related miRNAs and DAVID to detect transcription factors for further analysis. Finally, GSE62646 was utilized to assess DEGs specificity, with genes demonstrating AUC results exceeding 75%, indicating their potential as candidate biomarkers.

RESULTS

133 DEGs between SCAD and STEMI were obtained. Then, the PPI network of DEGs was constructed using String and Cytoscape, and further analysis determined hub genes and 6 molecular complexes. Functional enrichment analysis of the DEGs suggests that pathways related to inflammation, metabolism, and immunity play a pivotal role in the progression from SCAD to STEMI. Besides, related-miRNAs were predicted, has-miR-124, has-miR-130a/b, and has-miR-301a/b regulated the expression of the largest number of genes. Meanwhile, Transcription factors analysis indicate that EVI1, AML1, GATA1, and PPARG are the most enriched gene. Finally, ROC curves demonstrate that MS4A3, KLRC4, KLRD1, AQP9, and CD14 exhibit both high sensitivity and specificity in predicting STEMI.

CONCLUSIONS

This study revealed that immunity, metabolism, and inflammation are involved in the development of STEMI derived from SCAD, and 6 genes, including MS4A3, KLRC4, KLRD1, AQP9, CD14, and CCR1, could be employed as candidate biomarkers to STEMI.

Single cell RNA-seq identifies a FOS/JUN-related monocyte signature associated with clinical response of heart failure patients with mesenchymal stem cell therapy

Heart failure (HF) is a serious global health issue that demands innovative treatment approaches. In this study, we collected samples from 4 HF patients before and after MSC therapy and performed scRNA-seq. After the MSC therapy, the proportion of CD14+ monocytes decreased significantly in both the treatment response and non-response groups, with a more pronounced decrease in the treatment response group. The therapy-response and non-response group were clearly separated in the UMAP plot, while the CD14+ monocytes in the therapy-response group before and after MSC therapy were very similar, but there were significant differences in the non-response group. By further performing NMF analysis, we identified 11 subsets of CD14+ monocytes. More importantly, we identified a therapy-related CD14+ monocyte subpopulation. The predictive model based on CD14+ monocytes constructed by machine learning algorithms showed good performance. Moreover, genes such as FOS were highly enriched in the therapy-related CD14+ monocytes. The SCENIC analysis revealed potential regulatory factors for this treatment-responsive CD14+ monocytes, and FOS/JUN were identified as potential core indicators/regulators. Finally, HF patients were divided into three groups by NMF analysis, and the therapy-responsive CD14+ monocyte characteristics were differentially activated among the three groups. Together, this study identifies treatment-responsive CD14+ monocytes as a crucial biomarker for assessing the suitability of MSC therapy and determining which HF patients could benefit from it. This provides new clues for further investigating the therapeutic mechanisms of MSC therapy, offering beneficial insights for personalized treatment and improving prognosis in HF patients.

Characterization of the m6A regulators' landscape highlights the clinical significance of acute myocardial infarction

Objective

Acute myocardial infarction (AMI) is a severe cardiovascular disease that threatens human life and health globally. N6-methyladenosine (m6A) governs the fate of RNAs via m6A regulators. Nevertheless, how m6A regulators affect AMI remains to be deciphered. To solve this issue, an integrative analysis of m6A regulators in AMI was conducted.

Methods

We acquired transcriptome profiles (GSE59867, GSE48060) of peripheral blood samples from AMI patients and healthy controls. Key m6A regulators were used for LASSO, and consensus clustering was conducted. Next, the m6A score was also computed. Immune cell infiltration, ferroptosis, and oxidative stress were evaluated. In-vitro and in-vivo experiments were conducted to verify the role of the m6A regulator ALKBH5 in AMI.

Results

Most m6A regulators presented notable expression alterations in circulating cells of AMI patients versus those of controls. Based on key m6A regulators, we established a gene signature and a nomogram for AMI diagnosis and risk prediction. AMI patients were classified into three m6A clusters or gene clusters, respectively, and each cluster possessed the unique properties of m6A modification, immune cell infiltration, ferroptosis, and oxidative stress. Finally, the m6A score was utilized to quantify m6A modification patterns. Therapeutic targeting of ALKBH5 greatly alleviated apoptosis and intracellular ROS in H/R-induced H9C2 cells and NRCMs.

Conclusion

Altogether, our findings highlight the clinical significance of m6A regulators in the diagnosis and risk prediction of AMI and indicate the critical roles of m6A modification in the regulation of immune cell infiltration, ferroptosis, and oxidative stress.

Combining WGCNA and machine learning to identify mechanisms and biomarkers of ischemic heart failure development after acute myocardial infarction

Background

Ischemic heart failure (IHF) is a serious complication after acute myocardial infarction (AMI). Understanding the mechanism of IHF after AMI will help us conduct early diagnosis and treatment.

Methods

We obtained the AMI dataset GSE66360 and the IHF dataset GSE57338 from the GEO database, and screened overlapping genes common to both diseases through WGCNA analysis. Subsequently, we performed GO and KEGG enrichment analysis on overlapping genes to elucidate the common mechanism of AMI and IHF. Machine learning algorithms are also used to identify key biomarkers. Finally, we performed immune cell infiltration analysis on the dataset to further evaluate immune cell changes in AMI and IHF.

Results

We obtained 74 overlapping genes of AMI and IHF through WGCNA analysis, and the enrichment analysis results mainly focused on immune and inflammation-related mechanisms. Through the three machine learning algorithms of LASSO, RF and SVM-RFE, we finally obtained the four Hub genes of IL1B, TIMP2, IFIT3, and P2RY2, and verified them in the IHF dataset GSE116250, and the diagnostic model AUC = 0.907. The results of immune infiltration analysis showed that 8 types of immune cells were significantly different in AMI samples, and 6 types of immune cells were significantly different in IHF samples.

Conclusion

We explored the mechanism of IHF after AMI by WGCNA, enrichment analysis, and immune infiltration analysis. Four potential diagnostic candidate genes and therapeutic targets were identified by machine learning algorithms. This provides a new idea for the pathogenesis, diagnosis, and treatment of IHF after AMI.

Cardiac biopsies reveal differences in transcriptomics between left and right ventricle in patients with or without diagnostic signs of heart failure

New or mild heart failure (HF) is mainly caused by left ventricular dysfunction. We hypothesised that gene expression differ between the left (LV) and right ventricle (RV) and secondly by type of LV dysfunction. We compared gene expression through myocardial biopsies from LV and RV of patients undergoing elective coronary bypass surgery (CABG). Patients were categorised based on LV ejection fraction (EF), diastolic function and NT-proBNP into pEF (preserved; LVEF ≥ 45%), rEF (reduced; LVEF < 45%) or normal LV function. Principal component analysis of gene expression displayed two clusters corresponding to LV and RV. Up-regulated genes in LV included natriuretic peptides NPPA and NPPB, transcription factors/coactivators STAT4 and VGLL2, ion channel related HCN2 and LRRC38 associated with cardiac muscle contraction, cytoskeleton, and cellular component movement. Patients with pEF phenotype versus normal differed in gene expression predominantly in LV, supporting that diastolic dysfunction and structural changes reflect early LV disease in pEF. DKK2 was overexpressed in LV of HFpEF phenotype, potentially leading to lower expression levels of β-catenin, α-SMA (smooth muscle actin), and enhanced apoptosis, and could be a possible factor in the development of HFpEF. CXCL14 was down-regulated in both pEF and rEF, and may play a role to promote development of HF.

Identification of key biomarkers for predicting CAD progression in inflammatory bowel disease via machine-learning and bioinformatics strategies

The study aimed to identify the biomarkers for predicting coronary atherosclerotic lesions progression in patients with inflammatory bowel disease (IBD). Related transcriptome datasets were seized from Gene Expression Omnibus database. IBD-related modules were identified via Weighted Gene Co-expression Network Analysis. The 'Limma' was applied to screen differentially expressed genes between stable coronary artery disease (CAD) and acute myocardial infarction (AMI). Subsequently, we employed protein-protein interaction (PPI) network and three machine-learning strategies to further screen for candidate hub genes. Application of the receiver operating characteristics curve to quantitatively evaluate candidates to determine key diagnostic biomarkers, followed by a nomogram construction. Ultimately, we performed immune landscape analysis, single-gene GSEA and prediction of target-drugs. 3227 IBD-related module genes and 570 DEGs accounting for AMI were recognized. Intersection yielded 85 shared genes and mostly enriched in immune and inflammatory pathways. After filtering through PPI network and multi-machine learning algorithms, five candidate genes generated. Upon validation, CTSD, CEBPD, CYP27A1 were identified as key diagnostic biomarkers with a superior sensitivity and specificity (AUC > 0.8). Furthermore, all three genes were negatively correlated with CD4+ T cells and positively correlated with neutrophils. Single-gene GSEA highlighted the importance of pathogen invasion, metabolism, immune and inflammation responses during the pathogenesis of AMI. Ten target-drugs were predicted. The discovery of three peripheral blood biomarkers capable of predicting the risk of CAD proceeding into AMI in IBD patients. These identified biomarkers were negatively correlated with CD4+ T cells and positively correlated with neutrophils, indicating a latent therapeutic target.

Identification of diagnostic immune-related gene biomarkers for predicting heart failure after acute myocardial infarction

Post-myocardial infarction heart failure (HF) is a major public health concern. Previous studies have reported the critical role of immune response in HF pathogenesis. However, limited studies have reported predictive immune-associated biomarkers for HF. So we attempted to identify potential immune-related indicators for HF early diagnosis and therapy guidance. This study identified two potential immune-related hub genes (IRHGs), namely CXCR5 and FOS, using bioinformatic approaches. The expression levels of CXCR5 and FOS and their ability to predict long-term HF were analyzed. Functional enrichment analysis revealed that the hub genes were enriched in immune system processes, including the interleukin-17 and nuclear factor-kappa B signaling pathways, which are involved in the pathogenesis of HF. Quantitative real-time polymerase chain reaction revealed that the Fos mRNA levels, but not the Cxcr5 mRNA levels, were downregulated in the mice of the HF group. This study successfully identified two IRHGs that were significantly and differentially expressed in the HF group and could predict long-term HF, providing novel insights for future studies on HF and developing novel therapeutic targets for HF.

Role of serum cytokines in the prediction of heart failure in patients with coronary artery disease

AIMS

Coronary artery disease (CAD) is the most common cause of heart failure (HF). This study aimed to identify cytokine biomarkers for predicting HF in patients with CAD.

METHODS AND RESULTS

Twelve patients with CAD without HF (CAD-non HF), 12 patients with CAD complicated with HF (CAD-HF), and 12 healthy controls were enrolled for Human Cytokine Antibody Array, which were used as the training dataset. Then, differentially expressed cytokines among the different groups were identified, and crucial characteristic proteins related to CAD-HF were screened using a combination of the least absolute shrinkage and selection operator, recursive feature elimination, and random forest methods. A support vector machine (SVM) diagnostic model was constructed based on crucial characteristic proteins, followed by receiver operating characteristic curve analysis. Finally, two validation datasets, GSE20681 and GSE59867, were downloaded to verify the diagnostic performance of the SVM model and expression of crucial proteins, as well as enzyme-linked immunosorbent assay was also used to verify the levels of crucial proteins in blood samples. In total, 12 differentially expressed proteins were overlapped in the three comparison groups, and then four optimal characteristic proteins were identified, including VEGFR2, FLRG, IL-23, and FGF-21. After that, the area under the receiver operating characteristic curve of the constructed SVM classification model for the training dataset was 0.944. The accuracy of the SVM classification model was validated using the GSE20681 and GSE59867 datasets, with area under the receiver operating characteristic curve values of 0.773 and 0.745, respectively. The expression trends of the four crucial proteins in the training dataset were consistent with those in the validation dataset and those determined by enzyme-linked immunosorbent assay.

CONCLUSIONS

The combination of VEGFR2, FLRG, IL-23, and FGF-21 can be used as a candidate biomarker for the prediction and prevention of HF in patients with CAD.

M6A regulator methylation patterns and characteristics of immunity in acute ST-segment elevation myocardial infarction

M6A methylation is the most prevalent and abundant RNA modification in mammals. Although there are many studies on the regulatory role of m6A methylation in the immune response, the m6A regulators in the pathogenesis of acute ST-segment elevation myocardial infarction (STEMI) remain unclear. We comprehensively analysed the role of m6A regulators in STEMI and built a predictive model, revealing the relationship between m6A methylations and the immune microenvironment. Differential analysis revealed that 18 of 24 m6A regulators were significantly differentially expressed, and there were substantial interactions between the m6A regulator. Then, we established a classifier and nomogram model based on 6 m6A regulators, which can easily distinguish the STEMI and control samples. Finally, two distinct m6A subtypes were obtained and significantly differentially expressed in terms of infiltrating immunocyte abundance, immune reaction activity and human leukocyte antigen genes. Three hub m6A phenotype related genes (RAC2, RELA, and WAS) in the midnightblue module were identified by weighted gene coexpression network analysis, and were associated with immunity. These findings suggest that m6A modification and the immune microenvironment play a key role in the pathogenesis of STEMI.

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability

Acute myocardial infarction dominates coronary artery disease mortality. Identifying bio-signatures for plaque destabilization and rupture is important for preventing the transition from coronary stability to instability and the occurrence of thrombosis events. This computational systems biology study enrolled 2,235 samples from 22 independent bulks cohorts and 14 samples from two single-cell cohorts. A machine-learning integrative program containing nine learners was developed to generate a warning classifier linked to atherosclerotic plaque vulnerability signature (APVS). The classifier displays the reliable performance and robustness for distinguishing ST-elevation myocardial infarction from chronic coronary syndrome at presentation, and revealed higher accuracy to 33 pathogenic biomarkers. We also developed an APVS-based quantification system (APVSLevel) for comprehensively quantifying atherosclerotic plaque vulnerability, empowering early-warning capabilities, and accurate assessment of atherosclerosis severity. It unraveled the multidimensional dysregulated mechanisms at high resolution. This study provides a potential tool for macro-level differential diagnosis and evaluation of subtle genetic pathological changes in atherosclerosis.

Unlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosis

BACKGROUND

MicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation.

METHODS

In this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set.

RESULTS

We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data.

CONCLUSIONS

Our study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases.

Immune landscape and regulatory mechanisms in human atherosclerotic coronary plaques: Evidence from single-cell and bulk transcriptomics

Atherosclerosis is a chronic immuno-inflammatory disease, however, the immune landscape and regulatory mechanisms have not been clear. We detected seven principal immune cell clusters with distinct phenotypic and spatial characteristics using single-cell RNA-sequencing of aortic immune cells from patients with acute coronary syndrome and stable angina pectoris. Then we acquired 265 differentially expressed immune-related genes and the high scores were mainly found in T cells and monocytes, which were differentially regulated in atherosclerotic coronary plaques. The CCL signaling pathway was the most relevant pattern in the T cells and CCL5-CCR1 and CCL5-CCR5 ligand-receptor pairs played a vital role in the CCL signaling pathway. Further comparative analysis indicated MCH-I signaling was the most relevant pattern in the T cells and HLA ligand-related ligand-receptor pairs played a vital role. Functional analysis of the single-cell and bulk transcriptomics pointed to multiple pathways, such as antigen presentation and immune response. Nineteen common differentially expressed immune-related genes were found in both immune cells and the human peripheral blood mononuclear cells. Nine common differentially expressed transcription factors were differentially expressed in both T cell and monocyte clusters from the coronary plaques and human peripheral blood mononuclear cells and the network demonstrated that CEBPB might play an essential role in the transcriptional regulation of atherosclerosis as a hub transcription factor. The definition of immune cell diversity and heterogeneity by single-cell level analysis of aortic immune cell subsets not only unveils cell-type-specific pathways and new immune mechanisms but also discovers the functional correlation of immune cells in human atherosclerosis. Our findings provide great promise for the discovery of novel molecular mechanisms and precise therapeutic targets for atherosclerosis.

Identification of biomarkers and immune infiltration in acute myocardial infarction and heart failure by integrated analysis

The mortality of heart failure after acute myocardial infarction (AMI) remains high. The aim of the present study was to analyze hub genes and immune infiltration in patients with AMI and heart failure (HF). The study utilized five publicly available gene expression datasets from peripheral blood in patients with AMI who either developed or did not develop HF. The unbiased patterns of 24 immune cell were estimated by xCell algorithm. Single-cell RNA sequencing data were used to examine the immune cell infiltration in heart failure patients. Hub genes were validated by quantitative reverse transcription-PCR (RT-qPCR). In comparison with the coronary heart disease (CHD) group, immune infiltration analysis of AMI patients showed that macrophages M1, macrophages, monocytes, natural killer (NK) cells, and NKT cells were the five most highly activated cell types. Five common immune-related genes (S100A12, AQP9, CSF3R, S100A9, and CD14) were identified as hub genes associated with AMI. Using RT-qPCR, we confirmed FOS, DUSP1, CXCL8, and NFKBIA as the potential biomarkers to identify AMI patients at risk of HF. The study identified several transcripts that differentiate between AMI and CHD, and between HF and non-HF patients. These findings could improve our understanding of the immune response in AMI and HF, and allow for early identification of AMI patients at risk of HF.

Exploring an immune cells-related molecule in STEMI by bioinformatics analysis

BACKGROUND

ST-elevated myocardial infarction (STEMI) is the leading cause of mortality worldwide. The mortality rate of heart attacks has decreased due to various preventive factors and the development of early diagnostic resuscitation measures, but the long-term prognosis remains poor. The present study aimed to identify novel serum biomarkers in STEMI patients and explored a possible new mechanism of STEMI from an immune molecular angle with bioinformatics analysis.

METHODS

Gene expression profiles were obtained from Gene Expression Omnibus (GEO) database. Differential gene analysis, machine learning algorithms, gene set enrichment analysis, and immune cell infiltration analysis were conducted using R software.

RESULTS

We identified 146 DEGs (differentially expressed genes) in the integrated dataset between the STEMI and CAD (coronary artery disease) groups. Immune infiltration analysis indicated that eleven cell types were differentially infiltrated. Through correlation analysis, we further screened 25 DEGs that showed a high correlation with monocytes and neutrophils. Afterwards, five genes consistently selected by all three machine learning algorithms were considered candidate genes. Finally, we identified a hub gene (ADM) as a biomarker of STEMI. AUC curves showed that ADM had more than 80% high accuracy in all datasets.

CONCLUSIONS

In this study, we explored a potentially new mechanism of STEMI from an immune molecular perspective, which might provide insights into the pathogenesis of STEMI. ADM positively correlated with monocytes and neutrophils, suggesting its potential role in the immune response during STEMI. Additionally, we validated the diagnostic performance of ADM in two external datasets, which could help to develop new diagnostic tools or therapeutic strategies.

Identification of mitophagy-related genes with potential clinical utility in myocardial infarction at transcriptional level

Background

Myocardial infarction (MI) ranks among the most prevalent cardiovascular diseases. Insufficient blood flow to the coronary arteries always leads to ischemic necrosis of the cardiac muscle. However, the mechanism of myocardial injury after MI remains unclear. This article aims to explore the potential common genes between mitophagy and MI and to construct a suitable prediction model.

Methods

Two Gene Expression Omnibus (GEO) datasets (GSE62646 and GSE59867) were used to screen the differential expression genes in peripheral blood. SVM, RF, and LASSO algorithm were employed to find MI and mitophagy-related genes. Moreover, DT, KNN, RF, SVM and LR were conducted to build the binary models, and screened the best model to further external validation (GSE61144) and internal validation (10-fold cross validation and Bootstrap), respectively. The performance of various machine learning models was compared. In addition, immune cell infiltration correlation analysis was conducted with MCP-Counter and CIBERSORT.

Results

We finally identified ATG5, TOMM20, MFN2 transcriptionally differed between MI and stable coronary artery diseases. Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively. Additionally, functional analysis suggested that monocytes and neutrophils might be involved in mitochondrial autophagy after myocardial infarction.

Conclusion

The data showed that the transcritional levels of ATG5, TOMM20 and MFN2 in patients with MI were significantly different from the control group, which might be helpful to further accurately diagnose diseases and have potential application value in clinical practice.

Risk prediction model construction for post myocardial infarction heart failure by blood immune B cells

Background

Myocardial infarction (MI) is a common cardiac condition with a high incidence of morbidity and mortality. Despite extensive medical treatment for MI, the development and outcomes of post-MI heart failure (HF) continue to be major factors contributing to poor post-MI prognosis. Currently, there are few predictors of post-MI heart failure.

Methods

In this study, we re-examined single-cell RNA sequencing and bulk RNA sequencing datasets derived from the peripheral blood samples of patients with myocardial infarction, including patients who developed heart failure and those who did not develop heart failure after myocardial infarction. Using marker genes of the relevant cell subtypes, a signature was generated and validated using relevant bulk datasets and human blood samples.

Results

We identified a subtype of immune-activated B cells that distinguished post-MI HF patients from non-HF patients. Polymerase chain reaction was used to confirm these findings in independent cohorts. By combining the specific marker genes of B cell subtypes, we developed a prediction model of 13 markers that can predict the risk of HF in patients after myocardial infarction, providing new ideas and tools for clinical diagnosis and treatment.

Conclusion

Sub-cluster B cells may play a significant role in post-MI HF. We found that the STING1, HSPB1, CCL5, ACTN1, and ITGB2 genes in patients with post-MI HF showed the same trend of increase as those without post-MI HF.

Non-invasive STEMI-related biomarkers based on meta-analysis and gene prioritization

BACKGROUND AND AIMS

Acute ST-Segment Myocardial infarction (STEMI) is a common cardiovascular issue with a considerable burden of the disease. The underlying genetic basis and non-invasive markers were not well-established.

METHODS

Here, we implemented a systematic literature review and meta-analyses integration methods on 217 STEMI patients and 72 normal individuals to prioritize and detect the STEMI-related non-invasive markers. Five high-scored genes were experimentally assessed on 10 STEMI patients and 9 healthy controls. Finally, the presence of co-expressed nodes of top-score genes was explored.

RESULTS

The differential expression of ARGL, CLEC4E, and EIF3D were significant for Iranian patients. The ROC curve for gene CLEC4E revealed an AUC (95% CI) of 0.786 (0.686-0.886) in the prediction of STEMI. The Cox-PH model was fitted to stratify high/low risk heart failure progression (CI-index = 0.83, Likelihood-Ratio-Test = 3e-10). The SI00AI2 was a common biomarker between STEMI and NSTEMI patients.

CONCLUSIONS

In conclusion, the high-scored genes and prognostic model could be applicable for Iranian patients.

Blood Milieu in Acute Myocardial Infarction Reprograms Human Macrophages for Trauma Repair

Acute myocardial infarction (AMI) is accompanied by a systemic trauma response that impacts the whole body, including blood. This study addresses whether macrophages, key players in trauma repair, sense and respond to these changes. For this, healthy human monocyte-derived macrophages are exposed to 20% human AMI (n = 50) or control (n = 20) serum and analyzed by transcriptional and multiparameter functional screening followed by network-guided data interpretation and drug repurposing. Results are validated in an independent cohort at functional level (n = 47 AMI, n = 25 control) and in a public dataset. AMI serum exposure results in an overt AMI signature, enriched in debris cleaning, mitosis, and immune pathways. Moreover, gene networks associated with AMI and with poor clinical prognosis in AMI are identified. Network-guided drug screening on the latter unveils prostaglandin E2 (PGE2) signaling as target for clinical intervention in detrimental macrophage imprinting during AMI trauma healing. The results demonstrate pronounced context-induced macrophage reprogramming by the AMI systemic environment, to a degree decisive for patient prognosis. This offers new opportunities for targeted intervention and optimized cardiovascular disease risk management.

Dysregulated lncRNAs are involved in the progress of myocardial infarction by constructing regulatory networks

Long noncoding RNAs (lncRNAs) mediate important epigenetic regulation in a wide range of biological processes. However, the effect of all dysregulated lncRNAs in myocardial infarction (MI) is not clear. Whole transcriptome sequencing analysis was used to characterize the dynamic changes in lncRNA and mRNA expression. A gene network was constructed, and genes were classified into different modules using WGCNA. In addition, for all dysregulated lncRNAs, gene ontology analysis and cis-regulatory analysis were applied. The results demonstrated that a large number of the differentially co-expressed genes were primarily linked to the immune system process, inflammatory response, and innate immune response. The functional pathway analysis of the MEblue module included immune system process and apoptosis, and MEbrown included the T-cell receptor signal pathway by WGCNA. In addition, through cis-acting analysis of lncRNA regulation, the cis-regulated mRNAs were mainly enriched in immune system processes, innate immune responses, and VEGF signal pathways. We found that lncRNA regulation of mRNAs plays an important role in immune and inflammatory pathways. Our study provides a foundation to further understand the role and potential mechanism of dysregulated lncRNAs in the regulation of MI, in which many of them could be potential targets for MI.

Integrated whole-genome gene expression analysis reveals an atlas of dynamic immune landscapes after myocardial infarction

Introduction

Myocardial infarction (MI) is a deadly medical condition leading to irreversible damage to the inflicted cardiac tissue. Elevated inflammatory response marks the severity of MI and is associated with the development of heart failure (HF), a long-term adverse outcome of MI. However, the efficacy of anti-inflammatory therapies for MI remains controversial. Deciphering the dynamic transcriptional signatures in peripheral blood mononuclear cells (PBMCs) is a viable and translatable route to better understand post-MI inflammation, which may help guide post-MI anti-inflammatory treatments.

Methods

In this work, integrated whole-genome gene expression analysis was performed to explore dynamic immune landscapes associated with MI.

Results

GSEA and GSVA showed that pathways involved in the inflammatory response and metabolic reprogramming were significantly enriched in PBMCs from MI patients. Based on leukocyte profiles generated by xCell algorithm, the relative abundance of monocytes and neutrophils was significantly increased in PBMCs from MI patients and had positive correlations with typical inflammation-associated transcripts. Mfuzz clustering revealed temporal gene expression profiles of PBMCs during the 6-month post-MI follow-up. Analysis of DEGs and gene sets indicated that PBMCs from HF group were characterized by elevated and lasting expression of genes implicated in inflammation and coagulation. Consensus clustering generated 4 metabolic subtypes of PBMCs with molecular heterogeneity in HF patients.

Discussion

In summary, integrated whole-genome gene expression analysis here outlines a transcriptomic framework that may improve the understanding of dynamic signatures present in PBMCs, as well as the heterogeneity of PBMCs in MI patients with or without long-term clinical outcome of HF. Moreover, the work here uncovers the diversity and heterogeneity of PBMCs from HF patients, providing novel bioinformatic evidence supporting the mechanistic implications of metabolic reprogramming and mitochondrial dysfunction in the post-MI inflammation and HF. Therefore, our work here supports the notion that individualized anti-inflammatory therapies are needed to improve the clinical management of post-MI patients.

Ten quick tips for computational analysis of medical images

Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients' conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational-medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.

5mC modification patterns provide novel direction for early acute myocardial infarction detection and personalized therapy

Background

Most deaths from coronary artery disease (CAD) are due to acute myocardial infarction (AMI). There is an urgent need for early AMI detection, particularly in patients with stable CAD. 5-methylcytosine (5mC) regulatory genes have been demonstrated to involve in the progression and prognosis of cardiovascular diseases, while little research examined 5mC regulators in CAD to AMI progression.

Method

Two datasets (GSE59867 and GSE62646) were downloaded from Gene Expression Omnibus (GEO) database, and 21 m5C regulators were extracted from previous literature. Dysregulated 5mC regulators were screened out by "limma." The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm were employed to identify hub 5mC regulators in CAD to AMI progression, and 43 clinical samples (Quantitative real-time PCR) were performed for expression validation. Then a logistic model was built to construct 5mC regulator signatures, and a series of bioinformatics algorithms were performed for model validation. Besides, 5mC-associated molecular clusters were studied via unsupervised clustering analysis, and correlation analysis between immunocyte and 5mC regulators in each cluster was conducted.

Results

Nine hub 5mC regulators were identified. A robust model was constructed, and its prominent classification accuracy was verified via ROC curve analysis (area under the curve [AUC] = 0.936 in the training cohort and AUC = 0.888 in the external validation cohort). Besides, the clinical effect of the model was validated by decision curve analysis. Then, 5mC modification clusters in AMI patients were identified, along with the immunocyte infiltration levels of each cluster. The correlation analysis found the strongest correlations were TET3-Mast cell in cluster-1 and TET3-MDSC in cluster-2.

Conclusion

Nine hub 5mC regulators (DNMT3B, MBD3, UHRF1, UHRF2, NTHL1, SMUG1, ZBTB33, TET1, and TET3) formed a diagnostic model, and concomitant results unraveled the critical impact of 5mC regulators, providing interesting epigenetics findings in AMI population vs. stable CAD.

Identification of GLS as a cuproptosis-related diagnosis gene in acute myocardial infarction

Acute myocardial infarction (AMI) has the characteristics of sudden onset, rapid progression, poor prognosis, and so on. Therefore, it is urgent to identify diagnostic and prognostic biomarkers for it. Cuproptosis is a new form of mitochondrial respiratory-dependent cell death. However, studies are limited on the clinical significance of cuproptosis-related genes (CRGs) in AMI. In this study, we systematically assessed the genetic alterations of CRGs in AMI by bioinformatics approach. The results showed that six CRGs (LIAS, LIPT1, DLAT, PDHB, MTF1, and GLS) were markedly differentially expressed between stable coronary heart disease (stable_CAD) and AMI. Correlation analysis indicated that CRGs were closely correlated with N6-methyladenosine (m6A)-related genes through R language “corrplot” package, especially GLS was positively correlated with FMR1 and MTF1 was negatively correlated with HNRNPA2B1. Immune landscape analysis results revealed that CRGs were closely related to various immune cells, especially GLS was positively correlated with T cells CD4 memory resting and negatively correlated with monocytes. Kaplan–Meier analysis demonstrated that the group with high DLAT expression had a better prognosis. The area under curve (AUC) certified that GLS had good diagnostic value, in the training set (AUC = 0.87) and verification set (ACU = 0.99). Gene set enrichment analysis (GSEA) suggested that GLS was associated with immune- and hypoxia-related pathways. In addition, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, competing endogenous RNA (ceRNA) analysis, transcription factor (TF), and compound prediction were performed to reveal the regulatory mechanism of CRGs in AMI. Overall, our study can provide additional information for understanding the role of CRGs in AMI, which may provide new insights into the identification of therapeutic targets for AMI.

Bioinformatics analysis identifies ferroptosis‑related genes in the regulatory mechanism of myocardial infarction

Since ferroptosis is considered to be a notable cause of cardiomyocyte death, inhibiting ferroptosis has become a novel strategy in reducing cardiac cell death and improving cardiopathic conditions. Therefore, the aim of the present study was to search for ferroptosis-related hub genes and determine their diagnostic value in myocardial infarction (MI) to aid in the diagnosis and treatment of the disease. A total of 10,286 DEGs were identified, including 6,822 upregulated and 3.464 downregulated genes in patients with MI compared with healthy controls. After overlapping with ferroptosis-related genes, 128 ferroptosis-related DEGs were obtained. WGCNA successfully identified a further eight functional modules, from which the blue module had the strongest correlation with MI. Blue module genes and ferroptosis-related differentially expressed genes were overlapped to obtain 20 ferroptosis-related genes associated with MI. Go and KEGG analysis showed that these genes were mainly enriched in cellular response to chemical stress, trans complex, transferring, phosphorus-containing groups, protein serine/threonine kinase activity, FoxO signaling pathway. Hub genes were obtained from 20 ferroptosis-related genes through the PPI network. The expression of hub genes was found to be down-regulated in the MI group. Finally, the miRNAs-hub genes and TFs-hub genes networks were constructed. The GSE141512 dataset and the use of RT-qPCR assays on patient blood samples were used to confirm these results. The results showed that ATM, PIK3CA, MAPK8, KRAS and SIRT1 may play key roles in the development of MI, and could therefore be novel markers or targets for the diagnosis or treatment of MI.

Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

Objective

Energy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.

Methods

Transcriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.

Results

A total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.

Conclusion

This study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Epi-Drugs in Heart Failure

Unveiling the secrets of genome's flexibility does not only foster new research in the field, but also gives rise to the exploration and development of novel epigenetic-based therapies as an approach to alleviate disease phenotypes. A better understanding of chromatin biology (DNA/histone complexes) and non-coding RNAs (ncRNAs) has enabled the development of epigenetic drugs able to modulate transcriptional programs implicated in cardiovascular diseases. This particularly applies to heart failure, where epigenetic networks have shown to underpin several pathological features, such as left ventricular hypertrophy, fibrosis, cardiomyocyte apoptosis and microvascular dysfunction. Targeting epigenetic signals might represent a promising approach, especially in patients with heart failure with preserved ejection fraction (HFpEF), where prognosis remains poor and breakthrough therapies have yet to be approved. In this setting, epigenetics can be employed for the development of customized therapeutic approaches thus paving the way for personalized medicine. Even though the beneficial effects of epi-drugs are gaining attention, the number of epigenetic compounds used in the clinical practice remains low suggesting that more selective epi-drugs are needed. From DNA-methylation changes to non-coding RNAs, we can establish brand-new regulations for drug targets with the aim of restoring healthy epigenomes and transcriptional programs in the failing heart. In the present review, we bring the timeline of epi-drug discovery and development, thus highlighting the emerging role of epigenetic therapies in heart failure.

Extracellular vesicles derived from human bone marrow mesenchymal stem cells protect rats against acute myocardial infarction-induced heart failure

Extracellular vesicles (EVs) derived from human bone marrow mesenchymal stem cells (BMSCs) are suggested to promote angiogenesis in a rat model of acute myocardial infarction (AMI). This study aimed to explore the underlying mechanism of BMSCs-EVs in AMI-induced heart failure (HF). BMSCs were isolated and verified, and EVs were purified and identified. After establishment of AMI-induced HF models, rats were treated with BMSCs-EVs and/or overexpressing (ov)/knocking down (kd) bone morphogenetic protein 2 (BMP2). Cardiac function, myocardial histopathological changes, angiogenesis, and vascular regeneration density were measured. Levels of pro-angiogenesis factors and cardiomyocyte apoptosis were detected. The viability and angiogenesis of hypoxic human umbilical vein endothelial cells (HUVECs) were measured. After BMSCs-EV treatment, the cardiac function of HF rats was improved, myocardial fibrosis and inflammatory cell infiltration were decreased, angiogenesis was increased, and cardiomyocyte apoptosis was inhibited. BMP2 was significantly upregulated in the myocardium. Ov-BMP2-BMSCs-EVs alleviated myocardial fibrosis and inflammatory cell infiltration, and promoted angiogenesis of HF rats, and improved the activity and angiogenesis of hypoxic HUVECs, while kd-BMP2-BMSCs-EVs showed limited protection against AMI-induced HF. BMSCs-EVs deliver BMP2 to promote angiogenesis and improve cardiac function of HF rats.

PRKAR1A and SDCBP Serve as Potential Predictors of Heart Failure Following Acute Myocardial Infarction

Background and Objectives

Early diagnosis of patients with acute myocardial infarction (AMI) who are at a high risk of heart failure (HF) progression remains controversial. This study aimed at identifying new predictive biomarkers of post-AMI HF and at revealing the pathogenesis of HF involving these marker genes.

Methods and Results

A transcriptomic dataset of whole blood cells from AMI patients with HF progression (post-AMI HF, n = 16) and without progression (post-AMI non-HF, n = 16) was analyzed using the weighted gene co-expression network analysis (WGCNA). The results indicated that one module consisting of 720 hub genes was significantly correlated with post-AMI HF. The hub genes were validated in another transcriptomic dataset of peripheral blood mononuclear cells (post-AMI HF, n = 9; post-AMI non-HF, n = 8). PRKAR1A, SDCBP, SPRED2, and VAMP3 were upregulated in the two datasets. Based on a single-cell RNA sequencing dataset of leukocytes from heart tissues of normal and infarcted mice, PRKAR1A was further verified to be upregulated in monocytes/macrophages on day 2, while SDCBP was highly expressed in neutrophils on day 2 and in monocytes/macrophages on day 3 after AMI. Cell-cell communication analysis via the "CellChat" package showed that, based on the interaction of ligand-receptor (L-R) pairs, there were increased autocrine/paracrine cross-talk networks of monocytes/macrophages and neutrophils in the acute stage of MI. Functional enrichment analysis of the abovementioned L-R genes together with PRKAR1A and SDCBP performed through the Metascape platform suggested that PRKAR1A and SDCBP were mainly involved in inflammation, apoptosis, and angiogenesis. The receiver operating characteristic (ROC) curve analysis demonstrated that PRKAR1A and SDCBP, as well as their combination, had a promising prognostic value in the identification of AMI patients who were at a high risk of HF progression.

Conclusion

This study identified that PRKAR1A and SDCBP may serve as novel biomarkers for the early diagnosis of post-AMI HF and also revealed their potentially regulatory mechanism during HF progression.

Identification of Genes Linking Natural Killer Cells to Apoptosis in Acute Myocardial Infarction and Ischemic Stroke

Natural killer (NK) cells are a type of innate lymphoid cell that are involved in the progression of acute myocardial infarction and ischemic stroke. Although multiple forms of programmed cell death are known to play important roles in these diseases, the correlation between NK cells and apoptosis-related genes during acute myocardial infarction and ischemic stroke remains unclear. In this study, we explored the distinct patterns of NK cell infiltration and apoptosis during the pathological progression of acute myocardial infarction and ischemic stroke using mRNA expression microarrays from the Gene Expression Omnibus database. Since the abundance of NK cells correlated positively with apoptosis in both diseases, we further examined the correlation between NK cell abundance and the expression of apoptosis-related genes. Interestingly, APAF1 and IRAK3 expression correlated negatively with NK cell abundance in both acute myocardial infarction and ischemic stroke, whereas ATM, CAPN1, IL1B, IL1R1, PRKACA, PRKACB, and TNFRSF1A correlated negatively with NK cell abundance in acute myocardial infarction. Together, these findings suggest that these apoptosis-related genes may play important roles in the mechanisms underlying the patterns of NK cell abundance and apoptosis in acute myocardial infarction and ischemic stroke. Our study, therefore, provides novel insights for the further elucidation of the pathogenic mechanism of ischemic injury in both the heart and the brain, as well as potential useful therapeutic targets.

Identification of Potential Diagnostic Biomarkers From Circulating Cells During the Course of Sleep Deprivation-Related Myocardial Infarction Based on Bioinformatics Analyses

Background

Myocardial infarction (MI) is the leading cause of death from non-infectious diseases worldwide and results in rapid deterioration due to the sudden rupture of plaques associated with atherosclerosis, a chronic inflammatory disease. Sleep is a key factor that regulates immune homeostasis of the body. The imbalance in circulating immune cells caused by sleep deprivation (SD) may represent a risk factor leading to the rapid deterioration of plaques and MI. Therefore, it is of profound significance to identify diagnostic biomarkers for preventing SD-related MI.

Methods

In the present study, we identified coexpressed differentially expressed genes (co-DEGs) between peripheral blood mononuclear cells from MI and SD samples (compared to controls) from a public database. LASSO regression analysis was applied to identify significant diagnostic biomarkers from co-DEGs. Moreover, receiver operating characteristic (ROC) curve analysis was performed to test biomarker accuracy and diagnostic ability. We further analyzed immune cell enrichment in MI and SD samples using the CIBERSORT algorithm, and the correlation between biomarkers and immune cell composition was assessed. We also investigated whether diagnostic biomarkers are involved in immune cell signaling pathways in SD-related MI processes.

Results

A total of 10 downregulated co-DEGs from the sets of MI-DEGs and SD-DEGs were overlapped. After applying LASSO regression analysis, SYTL2, KLRD1, and C12orf75 were selected and validated as diagnostic biomarkers using ROC analysis. Next, we found that resting NK cells were downregulated in both the MI samples and SD samples, which is similar to the changes noted for SYTL2. Importantly, SYTL2 was strongly positively correlated not only with resting NK cells but also with most genes related to NK cell markers in the MI and SD datasets. Moreover, SYTL2 was highly associated with genes in NK cell signaling pathways, including the MAPK signaling pathway, cytotoxic granule movement and exocytosis, and NK cell activation. Furthermore, GSEA and KEGG analyses provided evidence that the DEGs identified from MI samples with low vs. high SYTL2 expression exhibited a strong association with the regulation of the immune response and NK cell-mediated cytotoxicity.

Conclusion

In conclusion, SYTL2, KLRD1, and C12orf75 represent potential diagnostic biomarkers of MI. The association between SYTL2 and resting NK cells may be critically involved in SD-related MI development and occurrence.

Identifying Potential New Gene Expression-Based Biomarkers in the Peripheral Blood Mononuclear Cells of Hepatitis B-Related Hepatocellular Carcinoma

OBJECTIVE

The analysis of the gene expression of peripheral blood mononuclear cells (PBMCs) is important to clarify the pathogenesis of hepatocellular carcinoma (HCC) and the detection of suitable biomarkers. The purpose of this investigation was to use RNA-sequencing to screen the appropriate differentially expressed genes (DEGs) in the PBMCs for the HCC.

METHODS

The comprehensive transcriptome of extracted RNA of PBMC (n = 20) from patients with chronic hepatitis B (CHB), liver cirrhosis, and early stage of HCC (5 samples per group) was carried out using RNA-sequencing. All raw RNA-sequencing data analyses were performed using conventional RNA-sequencing analysis tools. Next, gene ontology (GO) analyses were carried out to elucidate the biological processes of DEGs. Finally, relative transcript abundance of selected DEGs was verified using qRT-PCR on additional validation groups.

RESULTS

Specifically, 13, 1262, and 1450 DEGs were identified for CHB, liver cirrhosis, and HCC, when compared with the healthy controls. GO enrichment analysis indicated that HCC is closely related to the immune response. Seven DEGs (TYMP, TYROBP, CD14, TGFBI, LILRA2, GNLY, and GZMB) were common to HCC, cirrhosis, and CHB when compared to healthy controls. The data revealed that the expressions of these 7 DEGs were consistent with those from the RNA-sequencing results. Also, the expressions of 7 representative genes that had higher sensitivity were obtained by receiver operating characteristic analysis, which indicated their important diagnostic accuracy for HBV-HCC.

CONCLUSION

This study provides us with new horizons into the biological process and potential prospective clinical diagnosis and prognosis of HCC in the near future.

Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients

Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF. Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867. Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log₂FC| ≥ 0.8 and adjusted p < 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF. Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

Predictive Biomarkers for Postmyocardial Infarction Heart Failure Using Machine Learning: A Secondary Analysis of a Cohort Study

Background

There are few biomarkers with an excellent predictive value for postacute myocardial infarction (MI) patients who developed heart failure (HF). This study aimed to screen candidate biomarkers to predict post-MI HF.

Methods

This is a secondary analysis of a single-center cohort study including nine post-MI HF patients and eight post-MI patients who remained HF-free over a 6-month follow-up. Transcriptional profiling was analyzed using the whole blood samples collected at admission, discharge, and 1-month follow-up. We screened differentially expressed genes and identified key modules using weighted gene coexpression network analysis. We confirmed the candidate biomarkers using the developed external datasets on post-MI HF. The receiver operating characteristic curves were created to evaluate the predictive value of these candidate biomarkers.

Results

A total of 6,778, 1,136, and 1,974 genes (dataset 1) were differently expressed at admission, discharge, and 1-month follow-up, respectively. The white and royal blue modules were most significantly correlated with post-MI HF (dataset 2). After overlapping dataset 1, dataset 2, and external datasets (dataset 3), we identified five candidate biomarkers, including FCGR2A, GSDMB, MIR330, MED1, and SQSTM1. When GSDMB and SQSTM1 were combined, the area under the curve achieved 1.00, 0.85, and 0.89 in admission, discharge, and 1-month follow-up, respectively.

Conclusions

This study demonstrates that FCGR2A, GSDMB, MIR330, MED1, and SQSTM1 are the candidate predictive biomarker genes for post-MI HF, and the combination of GSDMB and SQSTM1 has a high predictive value.

Crosstalk between Circulatory Microenvironment and Vascular Endothelial Cells in Acute Myocardial Infarction

Background

The reason of high mortality of acute myocardial infarction (AMI) was the lack of exploring the cellular and molecular mechanism of AMI. Therefore, we explored the crosstalk among cells, as well as its potential molecular mechanism of mediating AMI.

Methods

The gene expression profile of peripheral blood, endothelial, platelets and mononuclear cells were applied to differentially expressed genes (DEGs) analysis. ClusterProfiler and the package of gene set enrichment analysis (GSEA) were applied to explore the potential functional pathways of DEGs in 3 types of intravascular cells (endothelial, platelets and mononuclear cells) and peripheral blood. Subsequently, we extracted the surface receptors, secreted proteins and extracellular matrix from the up-regulated DEGs to explore their potential interactions mechanism of AMI by crosstalk and pivot analysis.

Findings

A total 11 common regulated DEGs (CDEGs) were identified, which might be potential biomarkers for AMI diagnosis. The abnormal pathways involved in DEGs of 3 types of intravascular cells and peripheral blood were shown, which also verified by GSEA. Afterwards, it was found that there was crosstalk in 3 types of intravascular cells and peripheral blood. Furthermore, we constructed a cell–cell interaction map among cells in AMI regulated by exosome lncRNA, which was involved in the development of AMI. Finally, we identified 8 hub genes, which might be potential biomarkers of AMI.

Interpretation

The result of this study can not only be used as a reference for subsequent experiments and further exploration, but also contribute to the development of novel cell and molecular therapies.

An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data

Myocardial infarctions and heart failure are the cause of more than 17 million deaths annually worldwide. ST-segment elevation myocardial infarctions (STEMI) require timely treatment, because delays of minutes have serious clinical impacts. Machine learning can provide alternative ways to predict heart failure and identify genes involved in heart failure. For these scopes, we applied a Random Forests classifier enhanced with feature elimination to microarray gene expression of 111 patients diagnosed with STEMI, and measured the classification performance through standard metrics such as the Matthews correlation coefficient (MCC) and area under the receiver operating characteristic curve (ROC AUC). Afterwards, we used the same approach to rank all genes by importance, and to detect the genes more strongly associated with heart failure. We validated this ranking by literature review and gene set enrichment analysis. Our classifier employed to predict heart failure achieved MCC = +0.87 and ROC AUC = 0.918, and our analysis identified KLHL22, WDR11, OR4Q3, GPATCH3, and FAH as top five protein-coding genes related to heart failure. Our results confirm the effectiveness of machine learning feature elimination in predicting heart failure from gene expression, and the top genes found by our approach will be able to help biologists and cardiologists further our understanding of heart failure.

Identification of MYH6 as the potential gene for human ischaemic cardiomyopathy

The present study aimed to explore the potential hub genes and pathways of ischaemic cardiomyopathy (ICM) and to investigate the possible associated mechanisms. Two microarray data sets (GSE5406 and GSE57338) were downloaded from the Gene Expression Omnibus (GEO) database. The limma package was used to analyse the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology (DO) and Gene Ontology (GO) annotation analyses were performed. A protein‐protein interaction (PPI) network was set up using Cytoscape software. Significant modules and hub genes were identified by the Molecular Complex Detection (MCODE) app. Then, further functional validation of hub genes in other microarrays and survival analysis were performed to judge the prognosis. A total of 1065 genes were matched, with an adjusted p < 0.05, and 17 were upregulated and 25 were downregulated with|log₂ (fold change)|≥1.2. After removing the lengthy entries, GO identified 12 items, and 8 pathways were enriched at adjusted p < 0.05 (false discovery rate, FDR set at <0.05). Three modules with a score >8 after MCODE analysis and MYH6 were ultimately identified. When validated in GSE23561, MYH6 expression was lower in patients with CAD than in healthy controls (p < 0.05). GSE60993 data suggested that MYH6 expression was also lower in AMI patients (p < 0.05). In the GSE59867 data set, MYH6 expression was lower in CAD patients than in AMI patients and lower in heart failure (HF) patients than in non‐HF patients. However, there was no difference at different periods within half a year, and HF was increased when MYH6 expression was low (p < 0.05–0.01). We performed an integrated analysis and validation and found that MYH6 expression was closely related to ICM and HF. However, whether this marker can be used as a predictor in blood samples needs further experimental verification.

Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis

Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.

Uncovering Potential lncRNAs and mRNAs in the Progression From Acute Myocardial Infarction to Myocardial Fibrosis to Heart Failure

Background: Morbidity and mortality of heart failure (HF) post-myocardial infarction (MI) remain elevated. The aim of this study was to find potential long non-coding RNAs (lncRNAs) and mRNAs in the progression from acute myocardial infarction (AMI) to myocardial fibrosis (MF) to HF.

Methods: Firstly, blood samples from AMI, MF, and HF patients were used for RNA sequencing. Secondly, differentially expressed lncRNAs and mRNAs were obtained in MF vs. AMI and HF vs. MF, followed by functional analysis of shared differentially expressed mRNAs between two groups. Thirdly, interaction networks of lncRNA-nearby targeted mRNA and lncRNA-co-expressed mRNA were constructed in MF vs. AMI and HF vs. MF. Finally, expression validation and diagnostic capability analysis of selected lncRNAs and mRNAs were performed.

Results: Several lncRNA-co-expressed/nearby targeted mRNA pairs including AC005392.3/AC007278.2-IL18R1, AL356356.1/AL137145.2-PFKFB3, and MKNK1-AS1/LINC01127-IL1R2 were identified. Several signaling pathways including TNF and cytokine–cytokine receptor interaction, fructose and mannose metabolism and HIF-1, hematopoietic cell lineage and fluid shear stress, and atherosclerosis and estrogen were selected. IL1R2, IRAK3, LRG1, and PLAC4 had a potential diagnostic value for both AMI and HF.

Conclusion: Identified AC005392.3/AC007278.2-IL18R1, AL356356.1/AL137145.2-PFKFB3, and MKNK1-AS1/LINC01127-IL1R2 lncRNA-co-expressed/nearby targeted mRNA pairs may play crucial roles in the development of AMI, MF, and HF.