Susceptibility Modules and Genes in Hypertrophic Cardiomyopathy by WGCNA and ceRNA Network 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 6 cuproptosis-related genes for active ulcerative colitis with both diagnostic and therapeutic values

Cuproptosis has been reported to affect a variety of diseases. Therefore, we aimed to examine the role of cuproptosis-related genes in active ulcerative colitis (UC). We acquired 2 datasets of active UC from the Gene Expression Omnibus database and created immune cell infiltrations to research immune cell dysregulation. Based on the cuproptosis gene set and differentially expressed genes (DEGs), we identified the differentially expressed genes of cuproptosis (CuDEGs). We then used 2 machine learning methods to screen hub CuDEGs. Subsequently, we performed validation on additional datasets and investigated the relationship between hub CuDEGs and drug treatments. Thirty-five controls with inactive UC and 90 patients with active UC were obtained from the training sets. A total of 9157 DEGs and 27 CuDEGs were identified, respectively. Immune cell infiltration analysis revealed that patients with active UC exhibited higher levels of activated dendritic cells and neutrophils as well as lower levels of CD8+ T cells, regulatory T cells (Tregs), and macrophage M2. A six-gene cuproptosis signature was identified using machine learning algorithms. We further validated that the 6 hub CuDEGs showed a strong correlation with active UC and acted as cuproptosis-related biomarkers of active UC. Moreover, the expression of ATOX1 was downregulated, and SUMF1, MT1G, ATP7B, FDX1, and LIAS expression was upregulated in the colonic mucosa of active UC patients who responded to golimumab or vedolizumab therapy. With the exception of ATP7B, the expression patterns of hub CuDEGs before and after infliximab treatment of patients with active UC were similar to those of golimumab and vedolizumab. Cuproptosis and active UC have a complex relationship, as illustrated in our study. ATOX1, SUMF1, MT1G, ATP7B, FDX1, and LIAS are cuproptosis-related hub genes of active UC. Our study opens new avenues for investigating UC progression and developing novel therapeutic potential targets for the disease.

Characterization of the circulating transcriptome expression profile and identification of novel miRNA biomarkers in hypertrophic cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM), one of the most common genetic cardiovascular diseases, but cannot be explained by single genetic factors. Circulating microRNAs (miRNAs) are stable and highly conserved. Inflammation and immune response participate in HCM pathophysiology, but whether the miRNA profile changes correspondingly in human peripheral blood mononuclear cells (PBMCs) with HCM is unclear. Herein, we aimed to investigate the circulating non-coding RNA (ncRNA) expression profile in PBMCs and identify potential miRNAs for HCM biomarkers.

METHODS

A Custom CeRNA Human Gene Expression Microarray was used to identify differentially expressed (DE) mRNAs, miRNAs, and ncRNAs (including circRNA and lncRNA) in HCM PBMCs. Weighted correlation network analysis (WGCNA) was used to identify HCM-related miRNA and mRNA modules. The mRNAs and miRNAs from the key modules were used to construct a co-expression network. Three separate machine learning algorithms (random forest, support vector machine, and logistic regression) were applied to identify potential biomarkers based on miRNAs from the HCM co-expression network. Gene Expression Omnibus (GEO) database (GSE188324) and experimental samples were used for further verification. Gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network was used to determine the potential functions of the selected miRNAs in HCM.

RESULTS

We identified 1194 DE-mRNAs, 232 DE-miRNAs and 7696 DE-ncRNAs in HCM samples compared with normal controls from the microarray data sets. WGCNA identified key miRNA modules and mRNA modules evidently associated with HCM. We constructed a miRNA‒mRNA co-expression network based on these modules. A total of three hub miRNAs (miR-924, miR-98 and miR-1) were identified by random forest, and the areas under the receiver operator characteristic curves of miR-924, miR-98 and miR-1 were 0.829, 0.866, and 0.866, respectively.

CONCLUSIONS

We elucidated the transcriptome expression profile in PBMCs and identified three hub miRNAs (miR-924, miR-98 and miR-1) as potential biomarkers for HCM detection.

Dysfunctional network of hub genes in hypertrophic cardiomyopathy patients

BACKGROUND

Considering it is one of the major causes of sudden cardiac arrest, the proper management of hypertrophic cardiomyopathy (HCM) is essential. However, efficient treatment options for this disease are still lacking. The discovery of HCM-associated hub genes may help in diagnosis and offer a reliable tool for developing effective therapeutic strategies.

METHODS

We examined HCM-based gene expression datasets (GSE36961) from the Gene Expression Omnibus (GEO) database for the identification of differentially expressed genes (DEGs), PPI network development, module screening, and shortlisting of hub genes via GEOR2, STRING, and Cytoscape. Moreover, we also used another HCM-based gene expression dataset (GSE32453) for the expression validation of hub genes. Following this, we constructed the lncRNA-cricRNA-miRNA-mRNA regulatory network after retrieving information from the miRTarBase, miRDB, and MiRcode databases. Finally, we used DAVID to perform functional and pathway analysis of the hub genes.

RESULTS

From GSE36961, a total of the 262 most significant DEGs, including 162 down-regulated and 76 up-regulated, were identified between HCM patients and normal individuals. Among these DEGs, a total of 10 significantly down-regulated DEGs, including cluster of differentiation 14 (CD14), beta2 Integrin Gene (ITGB2), C1q subcomponent subunit B (C1QB), Cluster of Differentiation 163 (CD163), Hematopoietic Cell-Specific Lyn Substrate 1 (HCLS1), Arachidonate 5-Lipoxygenase Activating Protein (ALOX5AP), Pleckstrin (PLEK), Complement C1q C Chain (C1QC), Fc fragment Of IgE receptor Ig (FCER1G), and tyrosine kinase binding protein (TYROBP), were shortlisted as the hub genes. Pathway enrichment analysis showed that the identified hub genes were involved in the dysregulation of some diverse pathways in HCM patients. Such as, Pertussis, Complement and coagulation cascade, Legnionellosis, Asthma, Staphylococcus aureus infection, etc. Lastly, we also explored hub genes' regulatory 2 MicroRNAs (miRNAs, has-mir-7-5p and has-mir-27a-3p), one Long non-coding RNAs (lncRNA, OIP5-AS1-201), and one Circular RNA (cricRNA, CDR1as) via lncRNA-cricRNA-miRNA-mRNA regulatory network.

CONCLUSION

Our study revealed that ten hub genes (CD14, ITGB2, C1QB, CD163, HCLS1, ALOX5AP, PLEK, C1QC, FCER1G, and TYROBP) are involved in the development and progression of HCM. These genes can potentially be used as biomarkers and therapeutic targets for HCM patients.

Reconstruction and analysis of potential biomarkers for hypertrophic cardiomyopathy based on a competing endogenous RNA network

Hypertrophic cardiomyopathy (HCM) is a common heritable cardiomyopath. Although considerable effort has been made to understand the pathogenesis of HCM, the mechanism of how long noncoding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) network result in HCM remains unknown. In this study, we acquired a total of 520 different expression profiles of lncRNAs (DElncRNAs) and 371 messenger RNAs (mRNA, DEGs) by microarray and 33 microRNAs (DEmiRNAs) by sequencing in plasma of patients with HCM and healthy controls. Then lncRNA–miRNA pairs were predicted using miRcode and starBase and crossed with DEmiRNAs. MiRNA–mRNA pairs were retrieved from miRanda and TargetScan and crossed with DEGs. Combined with these pairs, the ceRNA network with eight lncRNAs, three miRNAs, and 22 mRNAs was constructed. lncRNA RP11-66N24.4 and LINC00310 were among the top 10% nodes. The hub nodes were analyzed to reconstruct a subnetwork. Furthermore, quantitative real-time polymerase chain reaction results showed that LINC00310 was significantly decreased in patients with HCM. For LINC00310, GO analysis revealed that biological processes were enriched in cardiovascular system development, sprouting angiogenesis, circulatory system development, and pathway analysis in the cGMP-PKG signaling pathway. These results indicate that the novel lncRNA-related ceRNA network in HCM and LINC00310 may play a role in the mechanism of HCM pathogenesis, which could provide insight into the pathogenesis of HCM.

Identification of Potential Diagnostic Biomarkers and Biological Pathways in Hypertrophic Cardiomyopathy Based on Bioinformatics Analysis

Hypertrophic cardiomyopathy (HCM) is a genetic heterogeneous disorder and the main cause of sudden cardiac death in adolescents and young adults. This study was aimed at identifying potential diagnostic biomarkers and biological pathways to help to diagnose and treat HCM through bioinformatics analysis. We selected the GSE36961 dataset from the Gene Expression Omnibus (GEO) database and identified 893 differentially expressed genes (DEGs). Subsequently, 12 modules were generated through weighted gene coexpression network analysis (WGCNA), and the turquoise module showed the highest negative correlation with HCM (cor = −0.9, p-value = 4 × 10−52). With the filtering standard gene significance (GS) < −0.7 and module membership (MM) > 0.9, 19 genes were then selected to establish the least absolute shrinkage and selection operator (LASSO) model, and LYVE1, MAFB, and MT1M were finally identified as key genes. The expression levels of these genes were additionally verified in the GSE130036 dataset. Gene Set Enrichment Analysis (GSEA) showed oxidative phosphorylation, tumor necrosis factor alpha-nuclear factor-κB (TNFα-NFκB), interferon-gamma (IFNγ) response, and inflammatory response were four pathways possibly related to HCM. In conclusion, LYVE1, MAFB, and MT1M were potential biomarkers of HCM, and oxidative stress, immune response as well as inflammatory response were likely to be associated with the pathogenesis of HCM.