Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Abstract

ObjectiveInflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.Materials and methodsThe microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).Results64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.ConclusionSERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.

Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis

“…KEGG enrichment analysis of down-regulated genes revealed that they were mainly concentrated in the ECM receptor interaction pathway. In ICM patients, the expression of genes controlling smooth muscle fibers, muscle contraction, and cardiac conduction was up-regulated, which is consistent with the pathophysiology of myocardial ischemia ( 23 - 25 ).…”

Integrated single-cell RNA-seq analysis revealed PTN secreted by fibroblasts acting on itself and macrophages via SDC4 ligand in myocardial hypertrophy

“…Studies had shown that PTN could enhance the survival of cardiomyocytes through inhibiting endogenous AKT/PKB activity [39] . PTN was also considered to be a potential biomarker for inflammatory ischemic cardiomyopathy with good ICM recognition [40] .…”

“…Studies had shown that PTN could enhance the survival of cardiomyocytes through inhibiting endogenous AKT/PKB activity [39] . PTN was also considered to be a potential biomarker for inflammatory ischemic cardiomyopathy with good ICM recognition [40] . In a proteomic analysis of pleural effusion from dead COVID-19 patients, 182 protein biomarkers including PTN were significantly elevate, indicating immune system overstimulation and cytokine storm [41] .…”

Identification and validation of potential hypoxia-related genes associated with coronary artery disease

“…Recently, machine learning algorithms have been used to identify disease biomarkers and therapeutic targets, investigate pathogenesis, and forecast clinical outcomes. Examples include the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) ( Wang et al, 2022 ). Therefore, this study aims to identify potential hypoxia-related genes (HRGs) in CAD.…”