Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis

Transcriptomic Analyses and Experimental Validation Identified Immune-Related lncRNA-mRNA Pair MIR210HG-BPIFC Regulating the Progression of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a disease in which the myocardium of the heart becomes asymmetrically thickened, malformed, disordered, and loses its normal structure and function. Recent studies have demonstrated the significant involvement of inflammatory responses in HCM. However, the precise role of immune-related long non-coding RNAs (lncRNAs) in the pathogenesis of HCM remains unclear. In this study, we performed a comprehensive analysis of immune-related lncRNAs in HCM. First, transcriptomic RNA-Seq data from both HCM patients and healthy individuals (GSE180313) were reanalyzed thoroughly. Key HCM-related modules were identified using weighted gene co-expression network analysis (WGCNA). A screening for immune-related lncRNAs was conducted within the key modules using immune-related mRNA co-expression analysis. Based on lncRNA-mRNA pairs that exhibit shared regulatory microRNAs (miRNAs), we constructed a competing endogenous RNA (ceRNA) network, comprising 9 lncRNAs and 17 mRNAs that were significantly correlated. Among the 26 lncRNA-mRNA pairs, only the MIR210HG-BPIFC pair was verified by another HCM dataset (GSE130036) and the isoprenaline (ISO)-induced HCM cell model. Furthermore, knockdown of MIR210HG increased the regulatory miRNAs and decreased the mRNA expression of BPIFC correspondingly in AC16 cells. Additionally, the analysis of immune cell infiltration indicated that the MIR210HG-BPIFC pair was potentially involved in the infiltration of naïve CD4+ T cells and CD8+ T cells. Together, our findings indicate that the decreased expression of the lncRNA-mRNA pair MIR210HG-BPIFC was significantly correlated with the pathogenesis of the disease and may be involved in the immune cell infiltration in the mechanism of HCM.

Identification of pyroptosis-associated genes with diagnostic value in calcific aortic valve disease

Background

Calcific aortic valve disease (CAVD) is one of the most prevalent valvular diseases and is the second most common cause for cardiac surgery. However, the mechanism of CAVD remains unclear. This study aimed to investigate the role of pyroptosis-related genes in CAVD by performing comprehensive bioinformatics analysis.

Methods

Three microarray datasets (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the Gene Expression Omnibus (GEO) database. Pyroptosis-related differentially expressed genes (DEGs) were identified between the calcified and the normal valve samples. LASSO regression and random forest (RF) machine learning analyses were performed to identify pyroptosis-related DEGs with diagnostic value. A diagnostic model was constructed with the diagnostic candidate pyroptosis-related DEGs. Receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic performances of the diagnostic model and the individual diagnostic candidate genes in the training and validation cohorts. CIBERSORT analysis was performed to estimate the differences in the infiltration of the immune cell types. Pearson correlation analysis was used to investigate associations between the diagnostic biomarkers and the immune cell types. Immunohistochemistry was used to validate protein concentration.

Results

We identified 805 DEGs, including 319 down-regulated genes and 486 up-regulated genes. These DEGs were mainly enriched in pathways related to the inflammatory responses. Subsequently, we identified 17 pyroptosis-related DEGs by comparing the 805 DEGs with the 223 pyroptosis-related genes. LASSO regression and RF algorithm analyses identified three CAVD diagnostic candidate genes (TREM1, TNFRSF11B, and PGF), which were significantly upregulated in the CAVD tissue samples. A diagnostic model was constructed with these 3 diagnostic candidate genes. The diagnostic model and the 3 diagnostic candidate genes showed good diagnostic performances with AUC values >0.75 in both the training and the validation cohorts based on the ROC curve analyses. CIBERSORT analyses demonstrated positive correlation between the proportion of M0 macrophages in the valve tissues and the expression levels of TREM1, TNFRSF11B, and PGF.

Conclusion

Three pyroptosis-related genes (TREM1, TNFRSF11B and PGF) were identified as diagnostic biomarkers for CAVD. These pyroptosis genes and the pro-inflammatory microenvironment in the calcified valve tissues are potential therapeutic targets for alleviating CAVD.

Preliminary study on molecular mechanism of COVID-19 intervention by Polygonum cuspidatum through computer bioinformatics

To explore the mechanism of action of Polygonum cuspidatum in intervening in coronavirus disease 2019 using a network pharmacology approach and to preliminarily elucidate its mechanism. The active ingredients and action targets of P cuspidatum were classified and summarized using computer virtual technology and molecular informatics methods. The active ingredients and relevant target information of P cuspidatum were identified using the TCM Systematic Pharmacology Database and Analysis Platform, the TCM Integrated Pharmacology Research Platform v2.0, and the SwissTarget database. The GENECARDS database was used to search for COVID-19 targets. The STRING database was analyzed and combined with Cytoscape 3.7.1 software to construct a protein interaction network map to screen the core targets. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was then performed. The core compound, polydatin, was selected and the core targets were analyzed by computer virtual docking using software such as discovery studio autodock tool. In vitro cell models were constructed to experimentally validate the activity of the core compound, polydatin. By computer screening, we identified 9 active ingredients and their corresponding 286 targets from P cuspidatum. A search of the GENECARDS database for COVID-19 yielded 303 core targets. By mapping the active ingredient targets to the disease targets, 27 overlapping targets could be extracted as potential targets for the treatment of COVID-19 with P cuspidatum. In addition, the enrichment analysis of Kyoto Encyclopedia of Genes and Genomes pathway on core targets showed that the coronavirus disease, MAPK signaling pathway, NF kappa B signaling pathway, and other signaling pathways were highly enriched. Combined with the degree-high target analysis in the protein interaction network, it was found to be mainly concentrated in the NF-kappaB (NF-κB) signaling pathway, indicating that the NF-κB signaling pathway may be an important pathway for P cuspidatum intervention. In vitro assays showed no effect of 0.1 to 10 μM polydatin on cell viability, but an inhibitory effect on the transcriptional activity of NF-κB-RE. Molecular docking showed stable covalent bonding of polydatin molecules with Il-1β protein at residue leu-26, TNF protein ser-60, residue gly-121, and residue ile-258 of ICAM-1 protein, indicating a stable docking result. The treatment of COVID-19 with P cuspidatum is characterized by multi-component, multi-target, and multi-pathway, which can exert a complex network of regulatory effects through the interaction between different targets, providing a new idea and basis for further exploration of the mechanism of action of P cuspidatum in the treatment of COVID-19.

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning

OBJECTIVES

Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.

METHODS

Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy.

RESULTS

Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM.

CONCLUSIONS

RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.