Identification of time‑series differentially expressed genes and pathways associated with heart failure post‑myocardial infarction using integrated bioinformatics analysis

Li, Xuefei; Li, Bin; Jiang, Hong

doi:10.3892/mmr.2019.10190

Cited by 10 publications

(8 citation statements)

References 65 publications

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“…The recent high-throughput RNA sequencing data has been widely employed to screen the differentially expressed genes (DEGs) between normal samples and HF samples in human beings, which makes it accessible for us to further explore the entire molecular alterations in HF at multiple levels involving DNA, RNA, proteins, epigenetic alterations, and metabolism [ 14 ]. However, there still exist obstacles to put these RNA seq data in application in clinic for the reason that the number of DEGs found by expression profiling by high throughput sequencing were massive and the statistical analyses were also too sophisticated [ 15 – 19 ]…”

Section: Introductionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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Introduction Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. Methods The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein–protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene—miRNA regulatory network and target gene—TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. Results A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene—miRNA regulatory network and target gene—TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. Conclusion The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

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Section: Introductionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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show abstract

“…Recent studies have reported that some hub genes are enriched in neutrophil degranulation, immune pathway activation, and in ammatory responses through gene set enrichment analysis (GSEA) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Role of Oxidative Stress-Related Gene and Immune cell Infiltration in Chronic Heart Failure: Novel Insights from Bioinformatic Analyses

Yang

Wang

Zhang

et al. 2022

Preprint

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Background Oxidative stress and immune cells infiltration have been shown to play a role in chronic heart failure (CHF). In this study, we aimed to explore oxidative stress status and immune cells infiltration associated with CHF and they may identify new candidates for biomarker. Methods CHF samples were collected from Gene Expression Omnibus (GEO) database (GSE5406, GSE9128, and GSE76701). Differentially expressed genes of oxidative stress (OSDEGs) were identified based on differentially expressed genes (DEGs) and oxidative stress gene set. Multiple machine learning methods were adopted to screen signature OSDEGs. Consensus clustering was used to divide samples into categories with different OSDEGs levels. Functional enrichment analysis was conducted to evaluate the gene enrichment signaling pathways in OSDEGs. The correlation between OSDEGs and immune cells infiltration was performed by single sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Results Overall, 33 differentially expressed oxidative stress-related genes were identified. Among them, 10 were further regarded as independent predictors for CHF, and used to develop a nomogram that had shown good performance in predicting CHF with an area under the receiver operating characteristic curve of 0.93(95%CI: 0.85-1.00) in the training sets and 0.81 (95%CI: 0.43-1.00) in the validation set. Furthermore, hub genes were mainly enriched in the detoxification of reactive oxygen species pathway, cell redox homeostasis and negative regulation of oxidative stress-induced cell death. The CHF and control samples showed significantly different distributions (P < 0.05) of monocytes and M0 macrophages. In addition, both AKT1 and NOS3 held a positive relationship with monocytes, but HSP90AA1 was negatively correlated with natural killer cells and type 1 T helper cells, respectively. Conclusions These results indicate that oxidative stress status is closely linked to CHF risk prediction and immune cell infiltration. Thus, the oxidative stress-based molecular signature may be target for CHF intervention.

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“…High-throughput microarray and transcriptome sequencing (such as bulk RNA-or single-cell RNAsequencing) permit us to perform a quick and comprehensive detection over the gene expression profiling, which are unbiased methods for screening diseasespecific biomarkers. Based on the transcriptomics data, several studies have identified potential biomarkers for prediction of HF following AMI via differentially expressed genes (DEGs) analysis [16][17][18], whereas which may result in the omission of some key genes related to disease. Weighted gene co-expression network analysis (WGCNA), as a bioinformatics application, can provide rich information based on calculating the pair-wise correlations between gene expression profiles [19].…”

Section: Introductionmentioning

confidence: 99%

Identification of monocyte-associated genes as predictive biomarkers of heart failure after acute myocardial infarction

et al. 2021

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Background Acute myocardial infarction (AMI) is a major contributor of heart failure (HF). Peripheral blood mononuclear cells (PBMCs), mainly monocytes, are the essential initiators of AMI-induced HF. The powerful biomarkers for early identification of AMI patients at risk of HF remain elusive. We aimed to identify monocyte-related critical genes as predictive biomarkers for post-AMI HF. Methods We performed weighted gene co-expression network analysis (WGCNA) on transcriptomics of PBMCs from AMI patients who developed HF or did not. Functional enrichment analysis of genes in significant modules was performed via Metascape. Then we obtained the single-cell RNA-sequencing data of recruited monocytes/macrophages from AMI and control mice using the Scanpy and screened 381 differentially expressed genes (DEGs) between the two groups. We validated the expression changes of the 25 genes in cardiac macrophages from AMI mice based on bulk RNA-sequencing data and PBMCs data mentioned above. Results In our study, the results of WGCNA showed that two modules containing 827 hub genes were most significantly associated with post-AMI HF, which mainly participated in cell migration, inflammation, immunity, and apoptosis. There were 25 common genes between DEGs and hub genes, showing close relationship with inflammation and collagen metabolism. CUX1, CTSD and ADD3 exhibited consistent changes in three independent studies. Receiver operating characteristic curve analysis showed that each of the three genes had excellent performance in recognizing post-AMI HF patients. Conclusion Our findings provided a set of three monocyte-related biomarkers for the early prediction of HF development after AMI as well as potential therapeutic targets of post-AMI HF.

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Identification of time‑series differentially expressed genes and pathways associated with heart failure post‑myocardial infarction using integrated bioinformatics analysis

Cited by 10 publications

References 65 publications

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Role of Oxidative Stress-Related Gene and Immune cell Infiltration in Chronic Heart Failure: Novel Insights from Bioinformatic Analyses

Identification of monocyte-associated genes as predictive biomarkers of heart failure after acute myocardial infarction

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