scholarly journals Comprehensive Identification of Key Genes Involved in Development of Diabetes Mellitus-Related Atherogenesis Using Weighted Gene Correlation Network Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Qi Huang ◽  
Guoxiong Deng ◽  
Rongguo Wei ◽  
Qiaoye Wang ◽  
Donghua Zou ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Network Analysis ◽  
Correlation Network ◽  
Gene Correlation ◽  
Key Genes

scholarly journals Identification of key genes in ruptured atherosclerotic plaques by weighted gene correlation network analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bao-Feng Xu ◽  
Rui Liu ◽  
Chun-Xia Huang ◽  
Bin-Sheng He ◽  
Guang-Yi Li ◽  
...  
Keyword(s):  
Network Analysis ◽  
Correlation Network ◽  
Atherosclerotic Plaques ◽  
Gene Correlation ◽  
Key Genes

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.

scholarly journals Weighted gene correlation network analysis identifies microenvironment-related genes signature as prognostic candidate for Grade II/III glioma

Aging ◽  
2020 ◽  
Vol 12 (21) ◽  
pp. 22122-22138
Author(s):  
Yong Li ◽  
Gang Deng ◽  
Huikai Zhang ◽  
Yangzhi Qi ◽  
Lun Gao ◽  
...  
Keyword(s):  
Network Analysis ◽  
Correlation Network ◽  
Gene Correlation ◽  
Grade Ii

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer

2019 ◽  
Vol 235 (1) ◽  
pp. 394-407 ◽  
Author(s):  
Jianing Tang ◽  
Qian Yang ◽  
Qiuxia Cui ◽  
Dan Zhang ◽  
Deguang Kong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Correlation Network ◽  
Gene Correlation
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