Systematic analysis of molecular mechanisms of heart failure through the pathway and network-based approach

HAPLN1 knockdown inhibits heart failure development via activating the PKA signaling pathway

BACKGROUND

Heart failure (HF) is a heterogeneous syndrome that affects millions worldwide, resulting in substantial health and economic burdens. However, the molecular mechanism of HF pathogenesis remains unclear.

METHODS

HF-related key genes were screened by a bioinformatics approach.The impacts of HAPLN1 knockdown on Angiotensin II (Ang II)-induced AC16 cells were assessed through a series of cell function experiments. Enzyme-linked immunosorbent assay (ELISA) was used to measure levels of oxidative stress and apoptosis-related factors. The HF rat model was induced by subcutaneous injection isoprenaline and histopathologic changes in the cardiac tissue were assessed by hematoxylin and eosin (HE) staining and echocardiographic index. Downstream pathways regulated by HAPLN1 was predicted through bioinformatics and then confirmed in vivo and in vitro by western blot.

RESULTS

Six hub genes were screened, of which HAPLN1, FMOD, NPPB, NPPA, and COMP were overexpressed, whereas NPPC was downregulated in HF. Further research found that silencing HAPLN1 promoted cell viability and reduced apoptosis in Ang II-induced AC16 cells. HAPLN1 knockdown promoted left ventricular ejection fraction (LVEF) and left ventricular fraction shortening (LVFS), while decreasing left ventricular end-systolic volume (LVESV) in the HF rat model. HAPLN1 knockdown promoted the levels of GSH and suppressed the levels of MDA, LDH, TNF-α, and IL-6. Mechanistically, silencing HAPLN1 activated the PKA pathway, which were confirmed both in vivo and in vitro.

CONCLUSION

HAPLN1 knockdown inhibited the progression of HF by activating the PKA pathway, which may provide novel perspectives on the management of HF.

Exosomal miRNA Changes Associated with Restoration to Sinus Rhythm in Atrial Fibrillation Patients

We aimed to identify serum exosomal microRNAs (miRNAs) associated with the transition from atrial fibrillation (AF) to sinus rhythm (SR) and investigate their potential as biomarkers for the early recurrence of AF within three months post-treatment. We collected blood samples from eight AF patients at Chang Gung Memorial Hospital in Taiwan both immediately before and within 14 days following rhythm control treatment. Exosomes were isolated from these samples, and small RNA sequencing was performed. Using DESeq2 analysis, we identified nine miRNAs (16-2-3p, 22-3p, 23a-3p, 23b-3p, 125a-5p, 328-3p, 423-5p, 504-5p, and 582-3p) associated with restoration to SR. Further analysis using the DIABLO model revealed a correlation between the decreased expression of miR-125a-5p and miR-328-3p and the early recurrence of AF. Furthermore, early recurrence is associated with a longer duration of AF, presumably indicating a more extensive state of underlying cardiac remodeling. In addition, the reads were mapped to mRNA sequences, leading to the identification of 14 mRNAs (AC005041.1, ARHGEF12, AMT, ANO8, BCL11A, DIO3OS, EIF4ENIF1, G2E3-AS1, HERC3, LARS, NT5E, PITX1, SLC16A12, and ZBTB21) associated with restoration to SR. Monitoring these serum exosomal miRNA and mRNA expression patterns may be beneficial for optimizing treatment outcomes in AF patients.

Identification of crucial genes related to heart failure based on GEO database

BACKGROUND

The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.

METHODS

First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.

RESULTS

In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.

CONCLUSIONS

Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

Relationship between asporin and extracellular matrix behavior: A literature review

Asporin (ASPN), as a member of the small leucine-rich repeat proteoglycan family, is a type of protein that is found in the extracellular matrix. Collagen deposition or transformation is involved in a variety of pathological processes. ASPN is identified in cancerous tissue, pathological cardiac tissue, articular cartilage, keloid, and fibrotic lung tissue, and it has a role in the development of cancer, cardiovascular, bone and joint, keloid, and pulmonary fibrosis by interfering with collagen metabolism. This review article summarizes the data on ASPN expressions in mouse and human and highlights that overexpress of ASPN might play a role in a variety of diseases. Although our knowledge of ASPN is currently limited, these instances may help us better understand how it interacts with diseases.

Prognostic analysis of m6A-related lncRNAs as potential biomarkers in intrahepatic cholangiocarcinom

Intrahepatic cholangiocarcinoma (iCCA) patients had no obvious symptoms at early stage and poor postoperative survival. Therefore, the establishment of an iCCA prognostic prediction model to carry out refined management of iCCA patients is expected to improve the survival of the iCCA patient population. In this paper, we analyzed the expression profiling data of patients from 32 iCCA tissues and eight paracancerous tissues in The Cancer Genome Atlas (TCGA) database. Perl software was used to separate M6A-related genes and lncRNAs from expression matrix files obtained from the TCGA database. The differentially expressed lncRNAs in the iCCA samples and the normal samples were screened out by differential analysis using the R package limma, and the m6A-related lncRNAs were further screened by Pearson correlation analysis. WGCNA clustering analysis constructs a random network to extract the module genes most related to iCCA, and take the intersection of differentially expressed lncRNAs related to m6A. Univariate Cox screening was performed for the intersection lncRNAs that had significant influence on the prognosis of iCCA patients, and further screening was performed by LASSO method and multivariate Cox regression analysis. Risk model was constructed and prognostic ability was evaluated according to risk score. In conclusion, we identified four m6A-related lncRNAs with potential prognostic value in iCCA, and established a novel m6A-related lncRNA-based prognostic model, which can be used as an independent prognostic factor to predict the prognosis of clinical patients.

Identification of potential dilated cardiomyopathy-related targets by meta-analysis and co-expression analysis of human RNA-sequencing datasets

AIMS

Dilated cardiomyopathy (DCM) remains among the most refractory heart diseases because of its complicated pathogenesis, and the key molecules that cause it remain unclear.

MAIN METHODS

To elucidate the molecules and upstream pathways critical for DCM pathogenesis, we performed meta-analysis and co-expression analysis of RNA-sequencing (RNA-seq) datasets from publicly available databases. We analyzed three RNA-seq datasets containing comparisons of RNA expression in left ventricles between healthy controls and DCM patients. We extracted differentially expressed genes (DEGs) and clarified upstream regulators of cardiovascular disease-related DEGs by Ingenuity Pathway Analysis (IPA). Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) analysis were also used to identify the hub gene candidates strongly associated with DCM.

KEY FINDINGS

In total, 406 samples (184 healthy, 222 DCM) were used in this study. Overall, 391 DEGs [absolute fold change (FC) ≥ 1.5; P < 0.01], including 221 upregulated and 170 downregulated ones in DCM, were extracted. Seven common hub genes (LUM, COL1A2, CXCL10, FMOD, COL3A1, ADAMTS4, MRC1) were finally screened. IPA showed several upstream transcriptional regulators, including activating (NFKBIA, TP73, CALR, NFKB1, KLF4) and inhibiting (CEBPA, PPARGC1A) ones. We further validated increased expression of several common hub genes in the transverse aortic constriction-induced heart failure model.

SIGNIFICANCE

In conclusion, meta-analysis and WGCNA using RNA-seq databases of DCM patients identified seven hub genes and seven upstream transcriptional regulators.

Screening of key methylation-driven genes CDO1 in breast cancer based on WGCNA

BACKGROUND

With the rapid development of genomics and molecular biology, not only have biochemical indicators been used as tumour markers, but many new molecular markers have emerged. Epigenetic abnormalities are a new type of molecular marker, and DNA methylation is an important part of epigenetics.

OBJECTIVE

This study used weighted gene coexpression network analysis (WGCNA) to analyse key methylation-driven genes in breast cancer.

METHODS

The RNA-seq transcriptome data, DNA methylation data, and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, and the MethylMix R package was used to screen methylation-driven genes in breast cancer. The ClusterProfiler package and enrichplot package in R software were used to further analyse the function and signalling pathway of methylation-driven genes. Through univariate and multivariate Cox regression analyses, methylation-driver genes related to prognostic were obtained, a prognostic model was constructed and prognostic characteristics were analysed.

RESULTS

The 17 methylation-driven genes related to prognosis were obtained by the WGCNA method in breast cancer, and the prognostic significance of these methylation-driven genes was determined by transcriptome and methylation combined survival analysis. Analysis of functions and signalling pathways showed that these genes were mainly enriched in biological processes and signalling pathway. Through univariate and multivariate Cox regression analyses, a prognostic model of 5 methylation-driven genes was constructed.

CONCLUSIONS

The AUC of the receiver operating characteristic (ROC) curve of this model was 0.784, showing that the model had a good prediction effect. Based on WGCNA screening, it was found that only CDO1 was the key methylation-driven gene for prognosis in breast cancer, indicating that CDO1 may be an important indicator of the prognosis of breast cancer patients.

Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure

Purpose

The objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF).

Methods

Differentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map.

Results

Functional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF.

Conclusion

We identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis

Purpose

Heart failure (HF) is a clinical syndrome caused by ventricular insufficiency. In order to further explore the biomarkers related to HF, we apply the high-throughput database.

Materials and Methods

The GSE21610 was applied for the differentially expressed gene (DEG) analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The Gene Set Enrichment Analysis (GSEA) was used for gene expression profile GSE21610. The Protein-Protein Interaction (PPI) network and modules were also constructed for research. These hub gene function pathways were estimated in HF progression.

Result

We have identified 434 DEGs in total, including 304 downregulated DEGs and 130 upregulated DEGs. GO and KEGG illustrated that DEGs in HF were significantly enriched in G protein-coupled receptor binding, peroxisome, and cAMP signaling pathway. GSEA results showed gene set GSE21610 was gathered in lipid digestion, defense response to fungus, and intestinal lipid absorption. Finally, through analyzing the PPI network, we screened hub genes CDH1, TFRC, CCL2, BUB1B, and CD19 by the Cytoscape software.

Conclusion

This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to HF. These analysis results provide us with new ideas for finding biomarkers and treatment methods for HF.