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

A novel machine learning-derived four-gene signature predicts STEMI and post-STEMI heart failure

High mortality and morbidity rates associated with ST-elevation myocardial infarction (STEMI) and post-STEMI heart failure (HF) necessitate proper risk stratification for coronary artery disease (CAD). A prediction model that combines specificity and convenience is highly required. This study aimed to design a monocyte-based gene assay for predicting STEMI and post-STEMI HF. A total of 1,956 monocyte expression profiles and corresponding clinical data were integrated from multiple sources. Meta-results were obtained through the weighted gene co-expression network analysis (WGCNA) and differential analysis to identify characteristic genes for STEMI. Machine learning models based on the decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms were trained and validated. Five genes overlapped and were subjected to the model proposal. The discriminative performance of the DT model outperformed the other two methods. The established four-gene panel (HLA-J, CFP, STX11, and NFYC) could discriminate STEMI and HF with an area under the curve (AUC) of 0.86 or above. In the gene set enrichment analysis (GSEA), several cardiac pathogenesis pathways and cardiovascular disorder signatures showed statistically significant, concordant differences between subjects with high and low expression levels of the four-gene panel, affirming the validity of the established model. In conclusion, we have developed and validated a model that offers the hope for accurately predicting the risk of STEMI and HF, leading to optimal risk stratification and personalized management of CAD, thereby improving individual outcomes.

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction

The ST-elevation Myocardial Infarction (STEMI) and Non-ST-elevation Myocardial Infarction (NSTEMI) might occur because of coronary artery stenosis. The gene biomarkers apply to the clinical diagnosis and therapeutic decisions in Myocardial Infarction. The aim of this study was to introduce, enrich and estimate timely the blood gene profiles based on the high-throughput data for the molecular distinction of STEMI and NSTEMI. The text mining data (50 genes) annotated with DisGeNET data (144 genes) were merged with the GEO gene expression data (5 datasets) using R software. Then, the STEMI and NSTEMI networks were primarily created using the STRING server, and improved using the Cytoscape software. The high-score genes were enriched using the KEGG signaling pathways and Gene Ontology (GO). Furthermore, the genes were categorized to determine the NSTEMI and STEMI gene profiles. The time cut-off points were identified statistically by monitoring the gene profiles up to 30 days after Myocardial Infarction (MI). The gene heatmaps were clearly created for the STEMI (high-fold genes 69, low-fold genes 45) and NSTEMI (high-fold genes 68, low-fold genes 36). The STEMI and NSTEMI networks suggested the high-score gene profiles. Furthermore, the gene enrichment suggested the different biological conditions for STEMI and NSTEMI. The time cut-off points for the NSTEMI (4 genes) and STEMI (13 genes) gene profiles were established up to three days after Myocardial Infarction. The study showed the different pathophysiologic conditions for STEMI and NSTEMI. Furthermore, the high-score gene profiles are suggested to measure up to 3 days after MI to distinguish the STEMI and NSTEMI.

Factors associated with complications in ST-elevation myocardial infarction: a single-center experience

BACKGROUND

ST-elevation myocardial infarction (STEMI) is a major public health problem. This study aimed to determine the prevalence and identify the determinants of STEMI-related complications in the Cardiology Intensive Care Unit of the Sud Francilien Hospital Center (SFHC).

METHODS

We retrospectively analyzed the data of 315 patients with STEMI aged ≥ 18 years. Logistic regression was used to identify factors independently associated with the occurrence of complications.

RESULTS

Overall, 315 patients aged 61.7 ± 13.4 years, of whom 261 were men, had STEMI during the study period. The hospital frequency of STEMI was 12.7%. Arrhythmias and acute heart failure were the main complications. Age ≥ 75 years (adjusted odds ratio [aOR], 5.18; 95% confidence interval [CI], 3.92-8.75), hypertension (aOR, 3.38; 95% CI, 1.68-5.82), and cigarette smoking (aOR, 3.52; 95% CI, 1.69-7.33) were independent determinants of acute heart failure. Meanwhile, diabetes mellitus (aOR, 1.74; 95% CI, 1.09-3.37), history of atrial fibrillation (aOR, 2.79; 95% CI, 1.66-4.76), history of stroke or transient ischemic attack (aOR, 1.99; 95% CI, 1.31-2.89), and low high-density lipoprotein-cholesterol (HDL-C) levels (aOR, 3.70; 95% CI, 1.08-6.64) were independent determinants of arrhythmias.

CONCLUSION

STEMI is a frequent condition at SFHC and is often complicated by acute heart failure and arrhythmias. Patients aged ≥ 75 years, those with hypertension or diabetes mellitus, smokers, those with a history of atrial fibrillation or stroke, and those with low HDL-C levels require careful monitoring for the early diagnosis and management of these complications.

Identification of the biological processes, immune cell landscape, and hub genes shared by acute anaphylaxis and ST-segment elevation myocardial infarction

Background: Patients with anaphylaxis are at risk for ST-segment elevation myocardial infarction (STEMI). However, the pathological links between anaphylaxis and STEMI remain unclear. Here, we aimed to explore shared biological processes, immune effector cells, and hub genes of anaphylaxis and STEMI. Methods: Gene expression data for anaphylactic (GSE69063) and STEMI (GSE60993) patients with corresponding healthy controls were pooled from the Gene Expression Omnibus database. Differential expression analysis, enrichment analysis, and CIBERSORT were used to reveal transcriptomic signatures and immune infiltration profiles of anaphylaxis and STEMI, respectively. Based on common differentially expressed genes (DEGs), Gene Ontology analysis, cytoHubba algorithms, and correlation analyses were performed to identify biological processes, hub genes, and hub gene-related immune cells shared by anaphylaxis and STEMI. The robustness of hub genes was assessed in external anaphylactic (GSE47655) and STEMI (GSE61144) datasets. Furthermore, a murine model of anaphylaxis complicated STEMI was established to verify hub gene expressions. The logistic regression analysis was used to evaluate the diagnostic efficiency of hub genes. Results: 265 anaphylaxis-related DEGs were identified, which were associated with immune-inflammatory responses. 237 STEMI-related DEGs were screened, which were involved in innate immune response and myeloid leukocyte activation. M0 macrophages and dendritic cells were markedly higher in both anaphylactic and STEMI samples compared with healthy controls, while CD4⁺ naïve T cells and CD8⁺ T cells were significantly lower. Enrichment analysis of 33 common DEGs illustrated shared biological processes of anaphylaxis and STEMI, including cytokine-mediated signaling pathway, response to reactive oxygen species, and positive regulation of defense response. Six hub genes were identified, and their expression levels were positively correlated with M0 macrophage abundance and negatively correlated with CD4⁺ naïve T cell abundance. In external anaphylactic and STEMI samples, five hub genes (IL1R2, FOS, MMP9, DUSP1, CLEC4D) were confirmed to be markedly upregulated. Moreover, experimentally induced anaphylactic mice developed impaired heart function featuring STEMI and significantly increased expression of the five hub genes. DUSP1 and CLEC4D were screened as blood diagnostic biomarkers of anaphylaxis and STEMI based on the logistic regression analysis. Conclusion: Anaphylaxis and STEMI share the biological processes of inflammation and defense responses. Macrophages, dendritic cells, CD8⁺ T cells, and CD4⁺ naïve T cells constitute an immune cell population that acts in both anaphylaxis and STEMI. Hub genes (DUSP1 and CLEC4D) identified here provide candidate genes for diagnosis, prognosis, and therapeutic targeting of STEMI in anaphylactic patients.

ITGAM rs1143679 Variant in Systemic Lupus Erythematosus Is Associated with Increased Serum Calcification Propensity

OBJECTIVES

CD11B/ITGAM (Integrin Subunit α M) mediates the adhesion of monocytes, macrophages, and granulocytes and promotes the phagocytosis of complement-coated particles. Variants of the ITGAM gene are candidates for genetic susceptibility to systemic lupus erythematosus (SLE). SNP rs1143679 (R77H) of CD11B particularly increases the risk of developing SLE. Deficiency of CD11B is linked to premature extra-osseous calcification, as seen in the cartilage of animals with osteoarthritis. Serum calcification propensity measured by the T50 test is a surrogate marker for systemic calcification and reflects increased cardiovascular (CV) risk. We aimed to assess whether the CD11B R77H gene variant is associated with a higher serum calcification propensity (i.e., a lower T50 value) in SLE patients compared to the wild-type allele (WT).

METHODS

Cross-sectional study incorporating adults with SLE genotyped for the CD11B variant R77H and assessed for serum calcification propensity with the T50 method. Participants were included in a multicenter trans-disciplinary cohort and fulfilled the 1997 revised American College of Rheumatology (ACR) criteria for SLE. We used descriptive statistics for comparing baseline characteristics and sequential T50 measurements in subjects with the R77H variant vs. WT CD11B.

RESULTS

Of the 167 patients, 108 (65%) were G/G (WT), 53 (32%) were G/A heterozygous, and 6 (3%) were A/A homozygous for the R77H variant. A/A patients cumulated more ACR criteria upon inclusion (7 ± 2 vs. 5 ± 1 in G/G and G/A; p = 0.02). There were no differences between the groups in terms of global disease activity, kidney involvement, and chronic renal failure. Complement C3 levels were lower in A/A individuals compared to others (0.6 ± 0.08 vs. 0.9 ± 0.25 g/L; p = 0.02). Baseline T50 did not differ between the groups (A/A 278 ± 42' vs. 297 ± 50' in G/G and G/A; p = 0.28). Considering all sequential T50 test results, serum calcification propensity was significantly increased in A/A individuals compared to others (253 ± 50 vs. 290 ± 54; p = 0.008).

CONCLUSIONS

SLE patients with homozygosity for the R77H variant and repeated T50 assessment displayed an increased serum calcification propensity (i.e., a lower T50) and lower C3 levels compared to heterozygous and WT CD11B, without differing with respect to global disease activity and kidney involvement. This suggests an increased CV risk in SLE patients homozygous for the R77H variant of CD11B.

Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies

OBJECTIVE

Our study aimed to identify early peripheral blood diagnostic biomarkers and elucidate the immune mechanisms of coronary artery disease (CAD) progression in patients with type 1 diabetes mellitus (T1DM).

METHODS

Three transcriptome datasets were retrieved from the Gene Expression Omnibus (GEO) database. Gene modules associated with T1DM were selected with weighted gene co-expression network analysis. Differentially expressed genes (DEGs) between CAD and acute myocardial infarction (AMI) peripheral blood tissues were identified using limma. Candidate biomarkers were selected with functional enrichment analysis, node gene selection from a constructed protein-protein interaction (PPI) network, and 3 machine learning algorithms. Candidate expression was compared, and the receiver operating characteristic curve (ROC) and nomogram were constructed. Immune cell infiltration was assessed with the CIBERSORT algorithm.

RESULTS

A total of 1283 genes comprising 2 modules were detected as the most associated with T1DM. In addition, 451 DEGs related to CAD progression were identified. Among them, 182 were common to both diseases and mainly enriched in immune and inflammatory response regulation. The PPI network yielded 30 top node genes, and 6 were selected using the 3 machine learning algorithms. Upon validation, 4 genes (TLR2, CLEC4D, IL1R2, and NLRC4) were recognized as diagnostic biomarkers with the area under the curve (AUC) > 0.7. All 4 genes were positively correlated with neutrophils in patients with AMI.

CONCLUSION

We identified 4 peripheral blood biomarkers and provided a nomogram for early diagnosing CAD progression to AMI in patients with T1DM. The biomarkers were positively associated with neutrophils, indicating potential therapeutic targets.

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network

Background

Acute myocardial infarction (AMI) is one of the most common causes of mortality around the world. Early diagnosis of AMI contributes to improving prognosis. In our study, we aimed to construct a novel predictive model for the diagnosis of AMI using an artificial neural network (ANN), and we verified its diagnostic value via constructing the receiver operating characteristic (ROC).

Methods

We downloaded three publicly available datasets (training sets GSE48060, GSE60993, and GSE66360) from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between 87 AMI and 78 control samples. We applied the random forest (RF) and ANN algorithms to further identify novel gene signatures and construct a model to predict the possibility of AMI. Besides, the diagnostic value of our model was further validated in the validation sets GSE61144 (7 AMI patients and 10 controls), GSE34198 (49 AMI patients and 48 controls), and GSE97320 (3 AMI patients and 3 controls).

Results

A total of 71 DEGs were identified, of which 68 were upregulated and 3 were downregulated. Firstly, 11 key genes in 71 DEGs were screened with RF classifier for the classification of AMI and control samples. Then, we calculated the weight of each key gene using ANN. Furthermore, the diagnostic model was constructed and named neuralAMI, with significant predictive power (area under the curve [AUC] = 0.980). Finally, our model was validated with the independent datasets GSE61144 (AUC = 0.900), GSE34198 (AUC = 0.882), and GSE97320 (AUC = 1.00).

Conclusion

Machine learning was used to develop a reliable predictive model for the diagnosis of AMI. The results of our study provide potential gene biomarkers for early disease screening.

Screening of Lipid Metabolism-Related Gene Diagnostic Signature for Patients With Dilated Cardiomyopathy

Background

Dilated cardiomyopathy (DCM) is characterized by enlarged ventricular dimensions and systolic dysfunction and poor prognosis. Myocardial lipid metabolism appears abnormal in DCM. However, the mechanism of lipid metabolism disorders in DCM remains unclear.

Methods

A gene set variation analysis (GSVA) were performed to estimate pathway activity related to DCM progression. Three datasets and clinical data downloaded from the Gene Expression Omnibus (GEO), including dilated cardiomyopathy and donor hearts, were integrated to obtain gene expression profiles and identify differentially expressed genes related to lipid metabolism. GO enrichment analyses of differentially expressed lipid metabolism-related genes (DELs) were performed. The clinical information used in this study were obtained from GSE21610 dataset. Data from the EGAS00001003263 were used for external validation and our hospital samples were also tested the expression levels of these genes through RT-PCR. Subsequently, logistic regression model with the LASSO method for DCM prediction was established basing on the 7 DELs.

Results

GSVA analysis showed that the fatty acid metabolism was closely related to DCM progression. The integrated dataset identified 19 DELs, including 8 up-regulated and 11 down-regulated genes. A total of 7 DELs were identified by further external validation of the data from the EGAS00001003263 and verified by RT-PCR. By using the LASSO model, 6 genes, including CYP2J2, FGF1, ETNPPL, PLIN2, LPCAT3, and DGKG, were identified to construct a logistic regression model. The area under curve (AUC) values over 0.8 suggested the good performance of the model.

Conclusion

Integrated bioinformatic analysis of gene expression in DCM and the effective logistic regression model construct in our study may contribute to the early diagnosis and prevention of DCM in people with high risk of the disease.