Identification of Oxidative Stress-Related Biomarkers in Acute Myocardial Infarction

An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods

Acute myocardial infarction (AMI) is a major contributor to cardiovascular-related mortality, and early diagnosis is crucial for effective treatment and better outcomes. While several biomarkers have been explored for AMI, there remains a need for reliable, non-invasive biomarkers that can accurately differentiate AMI patients from healthy individuals. This study aims to identify potential mRNA biomarkers in peripheral blood that could aid in the diagnosis and monitoring of AMI. We performed transcriptomic analysis of blood samples from 81 individuals, including 16 healthy controls, 58 AMI patients, and 7 post-treated AMI individuals. Through a combination of Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA), random forest (RF), Weighted Gene Co-expression Network Analysis (WGCNA), and LASSO regression, we identified mRNA markers that are significantly correlated with AMI. Specifically, the mRNA expressions of ANKRD52, ART1, NRP2, and PPP1R15A were elevated in AMI patients, whereas BAIAP2L1 and CCNE1 were downregulated. However, while these mRNA biomarkers show potential for distinguishing AMI patients from healthy individuals, further studies are needed to confirm their clinical applicability.

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.

Serum circRNA (Circ)_0051386 assists in the diagnosis of acute ST-segment elevation myocardial infarction and prediction of the occurrence of major adverse cardiovascular events after percutaneous coronary intervention

BACKGROUND

This study aimed to uncover the diagnostic value of circRNA (Circ)_0051386 in acute ST-segment elevation myocardial infarction (STEMI) and its predictive value for the occurrence of adverse major adverse cardiovascular events (MACEs).

METHODS

This study included 166 patients with STEMI and 83 health donors. The expression levels of serum Circ_0051386 in these participants were quantified using real-time quantitative polymerase chain reaction (RT-qPCR). Additionally, the incidence of MACEs during a 6-month follow-up period after percutaneous coronary intervention (PCI) was collected in the STEMI patient cohort.

RESULTS

Before and after propensity score matching (PSM), Circ_0051386 all had higher expression levels in the patients with STEMI than the normal subjects (all p < .001)and robust diagnosis values for the STEMI (AUC = 0.766, 0.779). Kaplan-Meier curves showed the high expression Circ_0051386 group had a higher occurrence rate of MACEs during a 6-month follow-up after PCI in patients with STEMI and this phenomenon was confirmed by internal validation (all p < .05). In addition, the multivariate COX regression showed gensini score (HR = 1.020, 95% CI = 1.002 - 1.038, p = .028) and Circ_0051386 (HR = 2.468, 95% CI =1.548-3.935, p < .001)were independent risk factors of the occurrence of MACEs in patients with STEMI after PCI. Pearson analysis presented that Circ_0051386 was positively correlated with gensini scores (r = 0.33), IL-1β (r = 0.55)and TNF-α(r = 0.41).

CONCLUSION

Our study indicated that Circ_0051386 is a biomarker of the diagnostic for STEMI and the predictor of the MACEs in STEMI patients after PCI. Its potential role in STEMI may be the regulation of inflammation in the vascular endothelial.