scholarly journals Identification of circRNA-miRNA-mRNA Regulatory Network and Autophagy Interaction Network in Atrial Fibrillation Based on Bioinformatics Analysis

2021 ◽  
Vol Volume 14 ◽  
pp. 8527-8540
Author(s):  
Xiaoying Chao ◽  
Weiran Dai ◽  
Shuo Li ◽  
Chenyang Jiang ◽  
Zhiyuan Jiang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Interaction Network

scholarly journals Identification of Pivotal MicroRNAs and Target Genes Associated with Persistent Atrial Fibrillation Based on Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shengjue Xiao ◽  
Yufei Zhou ◽  
Qiaozhi Liu ◽  
TianTian Zhang ◽  
Defeng Pan
Keyword(s):  
Atrial Fibrillation ◽  
Regulatory Network ◽  
Gene Network ◽  
Target Genes ◽  
Bioinformatics Analysis ◽  
Akt Signaling ◽  
Akt Signaling Pathway ◽  
Ppi Network ◽  
Expression Array ◽  
Transcription Factor Target

Atrial fibrillation (AF) is one of the most common supraventricular arrhythmias worldwide. However, the specific molecular mechanism underlying AF remains unclear. Our study is aimed at identifying pivotal microRNAs (miRNAs) and targeting genes associated with persistent AF (pAF) using bioinformatics analysis. Three gene expression array datasets (GSE31821, GSE41177, and GSE79768) and an miRNA expression array dataset (GSE68475) associated with pAF were downloaded. Differentially expressed genes (DEGs) were identified using the LIMMA package, and differentially expressed miRNAs (DEMs) were screened from GSE68475. Target genes for DEMs were predicted using the miRTarBase database, and intersections between these target genes and DEGs were selected for further analysis, including the generation of protein–protein interaction (PPI) network, miRNA–transcription factor–target regulatory network, and drug–gene network. A total of 264 DEGs and 40 DEMs were identified between the pAF and control groups. Functional and pathway enrichment analyses of up- and downregulated DEGs were performed. The common genes (CGs) were primarily enriched in the phosphoinositide 3-kinase- (PI3K-) protein kinase B (Akt) signaling pathway, negative regulation of cell division, and response to hypoxia. The PPI network, miRNA–transcription factor–target regulatory network, and drug–gene network were constructed using Cytoscape. The present study revealed several novel miRNAs and genes involved in pAF. We speculated that miR-4298, miR-3125, miR-4306, and miR-671-5p could represent significant miRNAs that act on the target gene superoxide dismutase 2 (SOD2) during the development of pAF and may serve as essential biomarkers for pAF diagnosis and treatment. Moreover, MYC might function in pAF pathogenesis through the PI3K–Akt signaling pathway.

Bioinformatics Analysis of a TF-miRNA-lncRNA Regulatory Network in Major Depressive Disorder

2021 ◽  
pp. 113842
Author(s):  
Qinglai Bian ◽  
Jianbei Chen ◽  
Jiajia Wu ◽  
Fengmin Ding ◽  
Xiaojuan Li ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Major Depressive

scholarly journals Retraction Note: Integrative analysis of the circRNA–miRNA regulatory network in atrial fibrillation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhong‑bao Ruan ◽  
Fei Wang ◽  
Qiu‑ping Yu ◽  
Ge‑cai Chen ◽  
Li Zhu
Keyword(s):  
Atrial Fibrillation ◽  
Regulatory Network ◽  
Integrative Analysis

scholarly journals Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Huang ◽  
Zhandong Sun ◽  
Wenying Yan ◽  
Yujie Zhu ◽  
Yuxin Lin ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Expression Profiles ◽  
Delayed Diagnosis ◽  
Interaction Network ◽  
High Sensitivity ◽  
Regulate Gene Expression ◽  
Protein Protein Interaction ◽  
Small Noncoding Rnas ◽  
Mirna Expression Profiles ◽  
Pathways Analysis

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.

Identification of potential key genes and key pathways related to clear cell renal cell carcinoma through bioinformatics analysis

2020 ◽  
Vol 52 (8) ◽  
pp. 853-863
Author(s):  
Wenxin Zhai ◽  
Haijiao Lu ◽  
Shenghua Dong ◽  
Jing Fang ◽  
Zhuang Yu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Phosphoenolpyruvate Carboxykinase ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Interaction Network ◽  
Polymerase Chain Reaction Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Key Genes

Abstract Clear cell renal cell carcinoma (ccRCC) is a common malignancy of the genitourinary system and is associated with high mortality rates. However, the molecular mechanism of ccRCC pathogenesis is still unclear, which translates to few effective diagnostic and prognostic biomarkers. In this study, we conducted a bioinformatics analysis on three Gene Expression Omnibus datasets and identified 437 differentially expressed genes (DEGs) related to ccRCC development and prognosis, of which 311 and 126 genes are respectively down-regulated and up-regulated. The protein–protein interaction network of these DEGs consists of 395 nodes and 1872 interactions and 2 prominent modules. The Staphylococcus aureus infection and complement and coagulation cascades are significantly enriched in module 1 and are likely involved in ccRCC progression. Forty-two hub genes were screened, of which von Willebrand factor, TIMP metallopeptidase inhibitor 1, plasminogen, formimidoyltransferase cyclodeaminase, solute carrier family 34 member 1, hydroxyacid oxidase 2, alanine-glyoxylate aminotransferase 2, phosphoenolpyruvate carboxykinase 1, and 3-hydroxy-3-methylglutaryl-CoA synthase 2 are possibly related to the prognosis of ccRCC. The differential expression of all nine genes was confirmed by quantitative real-time polymerase chain reaction analysis of the ccRCC and normal renal tissues. These key genes are potential biomarkers for the diagnosis and prognosis of ccRCC and warrant further investigation.

scholarly journals Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis

2021 ◽  
Vol 7 ◽  
Author(s):  
Tao Yan ◽  
Shijie Zhu ◽  
Miao Zhu ◽  
Chunsheng Wang ◽  
Changfa Guo
Keyword(s):  
Atrial Fibrillation ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Gene Correlation

Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF.Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR).Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis.Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

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