Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood

Yang, Xiangyu; Li, Jie; Hu, Xinyao; Zhang, Yinzhuang; Kuang, Yuanyuan; Liu, Yubo; Liu, Chenxi; Gao, Hongbo; Ma, Li; Tang, Jia; Ma, Qin

doi:10.3389/fcvm.2022.993579

Front. Cardiovasc. Med.

2022

DOI: 10.3389/fcvm.2022.993579

|View full text |Cite

Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood

Xiangyu Yang

Jie Li

Xinyao Hu

et al.

Abstract: ObjectiveThis study aims to analyze the gene expression profile of peripheral blood in different stages of myocardial infarction (MI) by transcriptome sequencing, and to study the gene expression characteristics of peripheral blood after MI.MethodsDifferentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify genes and modules associated with old myocardial infarction (OMI). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genome… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Preprint2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

View full text Add to dashboard Cite

Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

show abstract

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

View full text Add to dashboard Cite

show abstract

Bioinformatics and next generation data analysis reveals the potential role of inflammation in sepsis and its associated complications

Vastrad¹,

Vastrad²

2023

Preprint

View full text Add to dashboard Cite

Sepsis is the leading systemic inflammatory response syndrome in worldwide, yet relatively little is known about the genes and signaling pathways involved in sepsis progression. The current investigation aimed to elucidate potential key candidate genes and pathways in sepsis and its associated complications. Next generation sequencing (NGS) dataset (GSE185263) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 348 sepsis samples and 44 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. Next, we made use of the g:Profiler to analyze gene ontology (GO) and REACTOME pathway. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Furthermore, we constructed miRNA-hub gene regulatory network and TF-hub gene regulatory network among hub genes utilizing miRNet and NetworkAnalyst online databases tool and Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis of hub genes through the pROC package in R statistical software. In total, 958 DEGs were identified, of which 479 were up regulated and 479 were down regulated. GO and REACTOME results showed that DEGs mainly enriched in regulation of cellular process, response to stimulus, extracellular matrix organization and immune system. The hub genes of PRKN, KIT, FGFR2, GATA3, ERBB3, CDK1, PPARG, H2BC5, H4C4 and CDC20 might be associated with sepsis and its associated complications. Predicted miRNAs (e.g., hsa-mir-548ad-5p and hsa-mir-2113) and TFs (e.g., YAP1 and TBX5) were found to be significantly correlated with sepsis and its associated complications. In conclusion, the DEGs, relative pathways, hub genes, miRNA and TFs identified in the current investigation might help in understanding of the molecular mechanisms underlying sepsis and its associated complications progression and provide potential molecular targets and biomarkers for sepsis and its associated complications.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood

Cited by 2 publications

References 32 publications

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Bioinformatics and next generation data analysis reveals the potential role of inflammation in sepsis and its associated complications

Contact Info

Product

Resources

About