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

Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization study

OBJECTIVE

Acute myocardial infarction (AMI) is a severe condition with high morbidity and mortality rates. This study aimed to identify hub genes potentially associated with AMI and assess their clinical utility in predicting AMI occurrence.

METHODS

Gene microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted on samples from patients with AMI and control samples to identify modules significantly associated with AMI. GO and KEGG analyses were applied to investigate the potential functions of these hub genes. Lastly, the mendelian randomization (MR) method was applied to analyze the causal relationship between the hub gene TNF and AMI.

RESULTS

285 differentially expressed genes (DEGs) were identified through WCGNA and were clustered into 6 modules. The yellow module appeared most relevant to AMI. Further exploration through GO and KEGG pathway enrichment showed that key hub genes in the yellow module were linked to positive regulation of cytokine production, cytokine receptor binding, NF-kappa B signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. The top 10 genes identified through Cytoscape software analysis were IL1B, TNF, TLR4, TLR2, FCGR3B, MMP9, CXCL8, TLR8, ICAM1, and JUK. Utilizing inverse variance weighting (IVW) analysis, we discovered a significant association between TNF and AMI risk, with an OR of 0.946 (95% CI = 0.911-0.984, p = 0.005).

CONCLUSIONS

The result of this study indicated that TNF, TLR2, TLR4, IL1B and FCGR3B may be potential biodiagnostic markers for AMI. TNF can inhibit inflammatory and oxidative stress responses in AMI, exerting a protective role in the heart.

An integrated signature of clinical metrics and immune-related genes as a prognostic indicator for ST-segment elevation myocardial infarction patient survival

Background

The immune-inflammatory pathway plays a critical role in myocardial infarction development. However, few studies have systematically explored immune-related genes in relation to myocardial infarction prognosis using bioinformatic analysis. Our study aims to identify differentially expressed immune-related genes(DEIRGs) in ST-segment elevation myocardial infarction (STEMI) patients and investigate their association with clinical outcomes.

Materials and methods

We conducted a systematic review of Gene Expression Omnibus datasets, selecting GSE49925, GSE60993, and GSE61144 for analysis. DEIRGs were identified using GEO2R and overlapped across the chosen datasets. Functional enrichment analysis elucidated the DEIRGs' biological functions and pathways. We established an optimal prognostic prediction model using LASSO penalized Cox proportional hazards regression. The signature's clinical utility was evaluated through survival analysis, ROC curve assessment, and decision curve analysis. Additionally, we constructed a prognostic nomogram for survival rate prediction. External validation was performed using our own plasma samples.

Results

The resulting prognostic signature integrated two dysregulated DEIRGs (S100A12 and IL2RB) and two clinical variables (serum creatinine level and Gensini score). This signature effectively stratified patients into low- and high-risk groups. Survival analysis, ROC curve analysis, and decision curve analysis demonstrated its robust predictive performance and clinical utility within the first two years post-disease onset. External validation confirmed significant outcome differences between risk groups.

Conclusions

Our study establishes a prognostic signature that combines DEIRGs and clinical variables for STEMI patients. The signature exhibits promising predictive capabilities for patient stratification and survival risk assessment.

Favorable factors for the survival of ST-segment elevation myocardial infarction patients with medium- and high-risk thrombolysis in myocardial infarction scores

OBJECTIVE

ST-segment myocardial infarction (STEMI) is a time-sensitive emergency. This study screened the favorable factors for the survival of STEMI patients with medium- and high-risk thrombolysis in myocardial infarction (TIMI) scores.

METHODS

According to the TIMI scores at admission, 433 STEMI patients were retrospectively and consecutively selected and allocated into low-/medium-/high-risk groups, with their general information/blood routine/biochemical indicators/coagulation indicators documented. The factors influencing the in-hospital survival of STEMI patients were analyzed using univariate and multivariate logistic regression analyses. Moreover, the predictive value of favorable factors was analyzed by receiver operating characteristics (ROC) curve, and patients were assigned into high/low level groups based on the cut-off value of these factors, with their in-hospital survival rates compared.

RESULTS

The in-hospital survival rate of the medium-/high-risk groups was lower than that of the low-risk group. Emergency percutaneous coronary intervention (PCI), lymphocyte (LYM), total protein (TP), albumin (ALB), and sodium (Na) were independent favorable factors for in-hospital survival in the medium-/high-risk groups. Besides, LYM > 1.275 × 109/L, TP > 60.25 g/L, ALB > 34.55 g/L, and Na > 137.9 mmo1/L had auxiliary predictive value for the survival of STEMI patients with medium-/high-risk TIMI scores. Patients with high levels of LYM, TP, ALB, and Na exhibited higher in-hospital survival rates than patients with low levels.

CONCLUSION

For STEMI patients with medium- and high-risk TIMI scores, accepting emergency PCI and normal levels of LYM, TP, ALB, and Na were more conducive to in-hospital survival.