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

BackgroundImmune system dysregulation plays a critical role in aortic valve calcification (AVC) and metabolic syndrome (MS) pathogenesis. The study aimed to identify pivotal diagnostic candidate genes for AVC patients with MS.MethodsWe obtained three AVC and one MS dataset from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module gene via Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify candidate immune-associated hub genes for diagnosing AVC with MS. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC.ResultsThe merged AVC dataset included 587 DEGs, and 1,438 module genes were screened out in MS. MS DEGs were primarily enriched in immune regulation. The intersection of DEGs for AVC and module genes for MS was 50, which were mainly enriched in the immune system as well. Following the development of the PPI network, 26 node genes were filtered, and five candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all five candidate hub genes had high diagnostic values (area under the curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well.ConclusionFive immune-associated candidate hub genes (BEX2, SPRY2, CXCL16, ITGAL, and MORF4L2) were identified, and the nomogram was constructed for AVC with MS diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for AVC in MS patients.

Objective. This study is aimed at exploring the underlying molecular mechanisms of ST-segment elevation myocardial infarction (STEMI) and provides potential clinical prognostic biomarkers for STEMI. Methods. The GSE60993 dataset was downloaded from the GEO database, and the differentially expressed genes (DEGs) between STEMI and control groups were screened. Enrichment analysis of the DEGs was subsequently performed using the DAVID database. A protein–protein interaction network was constructed, and hub genes were identified. The hub genes in patients were then validated by quantitative reverse transcription-PCR. Furthermore, hub gene-miRNA interactions were evaluated using the miRTarBase database. Finally, patient data on classical cardiovascular risk factors were collected, and plasma microRNA-146a (miR-146a) levels were detected. An individualized nomogram was constructed based on multivariate Cox regression analysis. Results. A total of 239 DEGs were identified between the STEMI and control groups. Expression of S100A12 and miR-146a was significantly upregulated in STEMI samples compared with controls. STEMI patients with high levels of miR-146a had a higher risk of major adverse cardiovascular events (MACEs) than those with low levels of miR-146a (log-rank P = 0.034 ). Multivariate Cox regression analysis identified five statistically significant variables, including age, hypertension, diabetes mellitus, white blood cells, and miR-146a. A nomogram was constructed to estimate the likelihood of a MACE at one, two, and three years after STEMI. Conclusion. The incidence of MACEs in STEMI patients expressing high levels of miR-146a was significantly greater than in those expressing low levels. MicroRNA-146a can serve as a biomarker for adverse prognosis of STEMI and might function in its pathogenesis by targeting S100A12, which may exert its role via an inflammatory response. In addition, our study presents a valid and practical model to assess the probability of MACEs within three years of STEMI.

Objective Percutaneous coronary intervention (PCI) is one of the most effective treatments for acute coronary syndrome (ACS). However, the need for postoperative revascularization remains a major problem in PCI. This study was to develop and validate a nomogram for prediction of revascularization after PCI in patients with ACS. Methods A retrospective observational study was conducted using data from 1083 patients who underwent PCI (≥6 months) at a single center from June 2013 to December 2019. They were divided into training (70%; n = 758) and validation (30%; n = 325) sets. Multivariate logistic regression analysis was used to establish a predictive model represented by a nomogram. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot and decision curve analysis (DCA), respectively. Results The nomogram was comprised of ten variables: follow-up time (odds ratio (OR): 1.01; 95% confidence interval (CI): 1.00–1.03), history of diabetes mellitus (OR: 1.83; 95% CI: 1.25–2.69), serum creatinine level on admission (OR: 0.99; 95% CI: 0.98–1.00), serum uric acid level on admission (OR: 1.005; 95% CI: 1.002–1.007), lipoprotein-a level on admission (OR: 1.0021; 95% CI: 1.0013–1.0029), low density lipoprotein cholesterol level on re-admission (OR: 1.33; 95% CI: 0.10–0.47), the presence of chronic total occlusion (OR: 3.30; 95% CI: 1.93–5.80), the presence of multivessel disease (OR: 4.48; 95% CI: 2.85–7.28), the presence of calcified lesions (OR: 1.63; 95% CI: 1.11–2.39), and the presence of bifurcation lesions (OR: 1.82; 95% CI: 1.20–2.77). The area under the receiver operating characteristic curve values for the training and validation sets were 0.765 (95% CI: 0.732–0.799) and 0.791 (95% CI: 0.742–0.830), respectively. The calibration plots showed good agreement between prediction and observation in both the training and validation sets. DCA also demonstrated that the nomogram was clinically useful. Conclusion We developed an easy-to-use nomogram model to predict the risk of revascularization after PCI in patients with ACS. The nomogram may provide useful assessment of risk for subsequent treatment of ACS patients undergoing PCI.

Background Iron metabolism may be involved in the pathogenesis of the non-alcoholic fatty liver disease (NAFLD). The relationship between iron metabolism and NAFLD has not been clearly established. This study aimed to clarify the relationship between biomarkers of iron metabolism and NAFLD. Methods Based on the National Health and Nutrition Examination Survey (NHANES), restricted cubic spline models and multivariable logistic regression were used to examine the association between iron metabolism [serum iron (SI), serum ferritin (SF), transferrin saturation (TSAT), and soluble transferrin receptor (sTfR)] and the risk for NAFLD. In addition, stratified subgroup analysis was performed for the association between TSAT and NAFLD. Moreover, serum TSAT levels were determined in male mice with NAFLD. The expression of hepcidin and ferroportin, vital regulators of iron metabolism, were analyzed in the livers of mice by quantitative real-time PCR (qRT-PCR) and patients with NAFLD by microarray collected from the GEO data repository. Results Patients with NAFLD showed decreased SI, SF, and TSAT levels and increased STfR levels based on the NHANES. After adjusting for confounding factors, TSAT was significantly negatively correlated with NAFLD. Of note, the relationship between TSAT and NAFLD differed in the four subgroups of age, sex, race, and BMI (P for interaction < 0.05). Consistently, mice with NAFLD exhibited decreased serum TSAT levels. Decreased hepcidin and increased ferroportin gene expression were observed in the livers of patients and mice with NAFLD. Conclusion Serum TSAT levels and hepatic hepcidin expression were decreased in both patients and mice with NAFLD. Among multiple biomarkers of iron metabolism, lower TSAT levels were significantly associated with a higher risk of NAFLD in the U.S. general population. These findings might provide new ideas for the prediction, diagnosis, and mechanistic exploration of NAFLD.

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