scholarly journals Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome Analysis

2022 ◽  
Vol 2022 ◽  
pp. 1-23
Author(s):  
Qiaoqiao Li ◽  
Xueping Gao ◽  
Xueshan Luo ◽  
Qingrui Wu ◽  
Jintao He ◽  
...  
Keyword(s):  
Network Analysis ◽  
Molecular Mechanism ◽  
Clinical Significance ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Cardioembolic Stroke ◽  
High Morbidity ◽  
Pathophysiological Mechanism ◽  
Hub Genes

Cardioembolic stroke (CS) is the most common type of ischemic stroke in the clinic, leading to high morbidity and mortality worldwide. Although many studies have been conducted, the molecular mechanism underlying CS has not been fully grasped. This study was aimed at exploring the molecular mechanism of CS using comprehensive bioinformatics analysis and providing new insights into the pathophysiology of CS. We downloaded the public datasets GSE58294 and GSE16561. Differentially expressed genes (DEGs) were screened via the limma package using R software. CIBERSORT was used to estimate the proportions of 22 immune cells based on the gene expression profiling of CS patients. Using weighted gene correlation network analysis (WGCNA) to cluster the genes into different modules and detect relationships between modules and immune cell types, hub genes were identified based on the intersection of the protein-protein interaction (PPI) network analysis and WGCNA, and their clinical significance was then verified using another independent dataset GSE16561. Totally, 319 genes were identified as DEGs and 5413 genes were clustered into nine modules using WGCNA. The blue module, with the highest correlation coefficient, was identified as the key module associated with stroke, neutrophils, and B cells naïve. Based on the PPI analysis and WGCNA, five genes (MCEMP1, CLEC4D, GPR97, TSPAN14, and FPR2) were identified as hub genes. Correlation analysis indicated that hub genes had general association with infiltration-related immune cells. ROC analysis also showed they had potential clinical significance. The results were verified using another dataset, which were consistent with our analysis. Five crucial genes determined using integrative bioinformatics analysis might play significant roles in the pathophysiological mechanism in CS and be potential targets for pharmaceutic therapies.

scholarly journals Identification of Hub Genes Associated with Immune Cells in Dilated Cardiomyopathy via Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Jielin Deng ◽  
Yunqiu Jiang ◽  
Changjin Deng ◽  
hong jiang
Keyword(s):  
Network Analysis ◽  
Dilated Cardiomyopathy ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Gene Profiling ◽  
Functional Enrichment ◽  
Pathophysiological Mechanism ◽  
Hub Genes ◽  
Key Genes

Abstract Background: Dilated cardiomyopathy (DCM) is the most common cardiomyopathy which account for a majority of heart failure. Although massive clinic experiments and gene profiling analyses on DCM have been conducted, the molecular mechanism of DCM associated with immune cells has not been fully elucidated. This study was designed to discover the immune mechanism of DCM using integrative bioinformatics analysis and provide new insights into the pathophysiology of DCM. Methods: The GSE29819 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells based on 14 samples of 7 DCM patients. Weighted gene co-expression network analysis (WGCNA) was performed to cluster the 2500 genes with the highest average expression into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on key genes in significant modules identified by WGCNA and Cibersort. Key genes were then applied to Cytoscape to construct protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) were identified based on DCM and normal controls in GSE29819 through R language. Hub genes were selected based on the DEGs and the genes identified by PPI and then verified via public GEO databases. Results: The yellow and tan modules with 163 genes were identified as the key modules based on top 2500 DCM microarrays, significantly correlated with M1 and M2 macrophages. The intersection of newly screened 17 genes based on 163 key genes through Cytoscape and 2682 DEGs were defined as hub genes including CCT2, CCL2, and TXN. The results were finally verified via GSE116250 datasets.Conclusions: The three hub genes associated with two immune cells identified by comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism of DCM, which provided potential immunological therapeutic targets and new insights into the treatment of DCM.

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 The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Clinical Significance ◽  
Immune Cells ◽  
Complement System ◽  
Aortic Wall ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
The Complement System ◽  
Coexpression Network Analysis

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

scholarly journals Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

2021 ◽  
Vol 27 ◽  
Author(s):  
Wanbang Zhou ◽  
Yiyang Chen ◽  
Ruixing Luo ◽  
Zifan Li ◽  
Guanwei Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Tumor Progression ◽  
Protein Interactions ◽  
Immune Cells ◽  
Immune Cell ◽  
Immune Microenvironment ◽  
Hub Genes ◽  
Expression Levels ◽  
Immune Infiltration

Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.

scholarly journals Identification of Disease-Specific Hub Biomarkers and Immune Infiltration in Osteoarthritis and Rheumatoid Arthritis Synovial Tissues by Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Fei Sun ◽  
Jian lin Zhou ◽  
Pu ji Peng ◽  
Chen Qiu ◽  
Jia rui Cao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Relative Proportion ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Synovial Tissues ◽  
Disease Specific

Background. Osteoarthritis (OA) and rheumatoid arthritis (RA) are well-known cause of joint disability. Although they have shown the analogous clinical features involving chronic synovitis that progresses to cartilage and bone destruction, the pathogenesis that initiates and perpetuates synovial lesions between RA and OA remains elusive. Objective. This study is aimed at identifying disease-specific hub genes, exploring immune cell infiltration, and elucidating the underlying mechanisms associated with RA and OA synovial lesion. Methods. Gene expression profiles (GSE55235, GSE55457, GSE55584, and GSE12021) were selected from Gene Expression Omnibus for analysis. Differentially expressed genes (DEGs) were identified by the “LIMMA” package in Bioconductor. The DEGs were identified by Gene Ontology (GO) and KEGG pathway analysis. A protein-protein interaction network was constructed to identify candidate hub genes by using STRING and Cytoscape. Hub genes were identified by validating from GSE12021. Furthermore, we employed the CIBERSORT website to assess immune cell infiltration between OA and RA. Finally, we explored the correlation between the levels of hub genes and relative proportion of immune cells in OA and RA. Results. We identified 68 DEGs which were mainly enriched in immune response and chemokine signaling pathway. Six hub genes with a cutoff of AUC > 0.80 by ROC analysis and relative expression of P < 0.05 were identified successfully. Compared with OA, the RA synovial tissues consisted of a higher proportion of 7 immune cells, whereas 4 immune cells were found in relatively lower proportion ( P < 0.05 ). In addition, the levels of 6 hub genes were closely associated with relative proportion of 11 immune cells in OA and RA. Conclusions. We used bioinformatics analysis to identify hub genes and explored immune cell infiltration of immune microenvironment in synovial tissues. Our results should offer insights into the underlying molecular mechanisms of synovial lesion and provide potential target for immune-based therapies of OA and RA.

scholarly journals Weighted Gene Correlation Network Analysis Applied to Identify the Immune Cell-related Hub Genes in ANCA Nephritis

2022 ◽  
Author(s):  
Juan Jin ◽  
Di Zhang ◽  
Mingzhu Liang ◽  
Wenfang He ◽  
Jinshi Zhang
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Critical Role ◽  
Rapidly Progressive Glomerulonephritis ◽  
Antineutrophil Cytoplasmic Antibody ◽  
Hub Genes ◽  
Anca Associated Vasculitis ◽  
Gene Correlation

Abstract Background: Antineutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is the most common reason caused rapidly progressive glomerulonephritis worldwide. But the molecular mechanisms of ANCA - associated nephritis (AAN) have not been thoroughly expounded. So that，we aim to seek the potential molecular pathogenesis of AAN by bioinformatic.Result: Finally, four hub genes, PBK, CEP55, CCNB1 and BUB1B, were identified. These four hub geneswas verified higher in AAN than normal.Conclusion: Those four genes identified by integrated bioinformatics analysis may play a critical role in AAN. May offering a new insights and potential therapeutic to the AAN

scholarly journals Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Colon Cancer Patients and Assist Immunotherapy

2021 ◽  
Author(s):  
Boyang Xu ◽  
Ziqi Peng ◽  
Guanyu Yan ◽  
Ningning Wang ◽  
Moye Chen ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Microsatellite Instability ◽  
Cancer Patients ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Macrophage Infiltration ◽  
M2 Macrophage ◽  
High Morbidity ◽  
M2 Macrophages ◽  
Hub Genes

Abstract Background: Colon cancer is a kind of malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divide it into different subtypes in order to achieve individualized treatment. With the rise of immunotherapy, its value in the field of tumor has initially emerged. Based on the above background, from the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored it.Methods: Cibersort was used to analyze the level of immune cell infiltration in colon cancer patients in the TCGA database. WGCNA, Consensus Clustering analysis, Lasso analysis, and univariate KM analysis were used to screen and verify the hub genes associated with M2 macrophages. PCA was used to establish the M2 macrophage-related score—M2I Score. The correlation between M2I Score and somatic cell variation and microsatellite instability were analysed. Furthermore the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored. Results: M2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were selected as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in COAD was constructed based on four hub genes. M2I Score was positively correlated with tumor mutation load (TMB). The M2I Score of MSI-H group was higher than that of MSI-L group and MSS group. Combine with the TCIA database, we concluded that patients with a high M2I Score were more sensitive to PD-1 inhibitors and PD-1 inhibitors combined with CTLA-4 inhibitors. The low rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone.Conclusion: Four prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score and divided the patients into two subgroups: high M2I Score group and low M2I Score group. TMB, microsatellite instability and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

scholarly journals Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Gene Ontology ◽  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Integrin Subunit ◽  
Ppi Network ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

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