scholarly journals Identification of Key Exosome Gene Signature in Mediating Coronary Heart Disease by Weighted Gene Correlation Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yanbin Fu ◽  
Yanzhi Ge ◽  
Jianfeng Cao ◽  
Zedazhong Su ◽  
Danqing Yu
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Mast Cells ◽  
Network Analysis ◽  
B Cell ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Neutrophil Degranulation ◽  
Gene Correlation

Background. Coronary heart disease (CHD) is the most prevalent disease with an unelucidated pathogenetic mechanism and is mediated by complex molecular interactions of exosomes. Here, we aimed to identify differentially expressed exosome genes for the disease development and prognosis of CHD. Method. Six CHD samples and 32 normal samples were downloaded from the exoRbase database to identify the candidate genes in the CHD. The differentially expressed genes (DEGs) were identified. And then, weighted gene correlation network analysis (WGCNA) was used to investigate the modules in coexpressed genes between CHD samples and normal samples. DEGs and the module of the WGCNA were intersected to obtain the most relevant exosome genes. After that, the function enrichment analyses and protein-protein interaction network (PPI) were performed for the particular module using STRING and Cytoscape software. Finally, the CIBERSORT algorithm was used to analyze the immune infiltration of exosome genes between CHD samples and normal samples. Result. We obtain a total of 715 overlapping exosome genes located at the intersection of the DEGs and key modules. The Gene Ontology enrichment of DEGs in the blue module included inflammatory response, neutrophil degranulation, and activation of CHD. In addition, protein-protein networks were constructed, and hub genes were identified, such as LYZ, CAMP, HP, ORM1, and LTF. The immune infiltration profiles varied significantly between normal controls and CHD. Finally, we found that mast cells activated and eosinophils had a positive correlation. B cell memory had a significant negative correlation with B cell naive. Besides, neutrophils and mast cells were significantly increased in CHD patients. Conclusion. The underlying mechanism may be related to neutrophil degranulation and the immune response. The hub genes and the difference in immune infiltration identified in the present study may provide new insights into the diagnostic and provide candidate targets for CHD.

scholarly journals Elucidating the Key Biomarkers and Immune Infiltration in Acne by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Lu Yang ◽  
Yan-hong Shou ◽  
Yong-sheng Yang ◽  
Jin-hua Xu
Keyword(s):  
Mast Cells ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Acne Vulgaris ◽  
Interaction Network ◽  
Microarray Dataset ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration

Abstract Background Acne vulgaris is a common inflammatory condition of skin. However, the landscape of immune infiltration in acne has not been entirely described. Objectives This study used a bioinformatics approach to investigate the inflammatory acne-related key biomarkers and signaling pathways, and immune infiltration in the acne lesion. Methods Two microarray datasets (GSE108110 and GSE53795) were downloaded from Gene Expression Omnibus. We used “limma” package from R software to identify the differentially expressed genes (DEGs) and perform the functional enrichment analyses. Then we built a protein-protein interaction network (PPI), performed the hub genes’ identification through STRING and Cytoscape. We applied the CIBERSORT algorithm to describe the immune infiltration in acne, and explored the correlation between biomarkers and immune infiltration. In the end, our findings in the study were verified by analyzing microarray dataset GSE6475. Results The differentially expressed genes (DEGs) including 292 upregulated genes and 150 downregulated genes in acne compared with non-lesional skin. The hub genes FPR1, C3AR1, CXCL1, CXCL8, FPR2, C3, CCR7, ITGB2 and pivotal pathways JAK-STAT signaling pathway, Toll-like receptor and NOD-like receptor signaling pathway were the most significantly associated with raising neutrophils, monocytes, activated mast cells, as well as reducing resting mast cells and Tregs. Conclusions Our study provides new insights into the pathogenesis and the targets which might be immunomodulatory potential for acne.

scholarly journals Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8390 ◽  
Author(s):  
Weisong Cai ◽  
Haohuan Li ◽  
Yubiao Zhang ◽  
Guangtao Han
Keyword(s):  
T Cells ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Normal Controls

Background Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. Objective This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. Materials and Methods The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. Results A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. Conclusion The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.

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 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 Transcriptome Analyses Revealed the Ultraviolet B Irradiation and Phytohormone Gibberellins Coordinately Promoted the Accumulation of Artemisinin in Artemisia Annua L.

2020 ◽  
Author(s):  
Tingyu Ma ◽  
Han Gao ◽  
Dong Zhang ◽  
Yuhua Shi ◽  
Tianyuan Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Artemisia Annua ◽  
Enrichment Analysis ◽  
Ultraviolet B ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Uv Treatment ◽  
Artemisia Annua L ◽  
Ultraviolet B Irradiation ◽  
Artemisinin Biosynthesis

Abstract Background: Artemisinin-based combination therapy has become the preferred approach for treating malaria and has successfully reduced malaria-related mortality. Currently, the main source of artemisinin is Artemisia annua L., and thus, it is of strategic importance to enhance artemisinin contents in A. annua plants. Phytohormones and illumination are known to be important external environmental factor that can have notable effects on the production of secondary metabolite. The activities of different hormones can be influenced to varying degrees by light, and thus light and hormones may jointly regulate various processes in plants. Here, we performed transcriptome and metabolome analyses revealed that ultraviolet B irradiation and phytohormone gibberellins coordinately promoted the accumulation of artemisinin in Artemisia annua.Methods: Artemisinin analysis was performed by ultra-high performance liquid chromatography-tandem quadrupole mass spectrometry (UPLC-ESI-QqQ-MS/MS). RNA sequencing, GO and KEGG enrichment analysis were applied to analyzing the differentially expressed genes (DEGs) under ultraviolet B irradiation and gibberellins treatments. Weighted gene co-expression network (WGCNA) analyzed the genes in artemisinin‑related modules and identified candidate hub genes in these modules.Results: In this study, we found that cross-talk between UV-B and GA induced processes leading to modifications in artemisinin accumulation. A total of 14,762 genes differentially expressed (DEGs) among different treatments were identified by transcriptome analysis. UV-B and GA treatments enhanced the accumulation of artemisinin by up-regulating the expression of the key artemisinin biosynthesis genes ADS and CYP71AV1. According to the high degree value and high expression level, a total of 84 co-expressed transcription factors were identified. Among them, MYB and NAC TFs mainly involved in regulating the biosynthesis of artemisinin. Weighted gene co-expression network analysis revealed that GA+UV in blue modules was positively correlated with artemisinin synthesis, suggesting that the candidate hub genes in these modules should be up-regulated to enhance artemisinin synthesis in response to GA+UV treatment.Conclusion: Our study demonstrated the co-regulation of artemisinin biosynthetic pathway genes under ultraviolet B irradiation and phytohormone gibberellins treatment. The co-expression was analysis revealed that the selected MYB and NAC TFs might have regulated the artemisinin biosynthesis gene expression with ADS and CYP71AV1 genes. Weighted gene co-expression network analysis revealed that GA+UV treatment in blue modules was positively correlated with artemisinin synthesis. We established the network to distinguish candidate hub genes in blue modules might be up-regulated to enhance artemisinin synthesis in response to GA+UV treatment.

scholarly journals Identification of Key Immune-Related Genes, Molecular Pathways and Immune Infiltration as Diagnostic and Therapeutic Candidate Targets for RA: an integrated bioinformatics-based analysis

2021 ◽  
Author(s):  
Sheng Fang ◽  
Xiao Fang ◽  
Xin Xu ◽  
Lin Zhong ◽  
An-quan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Mast Cells ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Systemic Autoimmune Disease ◽  
Molecular Pathways ◽  
Hub Genes ◽  
Immune Infiltration

Abstract Relevance Rheumatoid arthritis (RA) is a systemic autoimmune disease with an aggressive, chronic synovial inflammation as the main pathological change. However, the specific etiology, pathogenesis, and related biomarkers in diagnosis and treatment are still not fully elucidated. This study attempts to provide new perspectives and insights into RA at the genetic, molecular, and cellular levels through the tenet of personalized medicine. Methods Gene expression profiles of four individual knee synovial tissues were downloaded from a comprehensive gene expression database, R language was used to screen for significantly differentially expressed genes (DEGs), Gene Ontology Enrichment Analysis, Kyoto Gene Encyclopedia, and Gene Set Enrichment Analysis were performed to analyze the biological functions and signaling pathways of these DEGs, STRING online database was used to establish protein-protein interaction networks, Cytoscape software to obtain ten hub genes, Goplot to get six inflammatory immune-related hub genes, and CIBERSORT algorithm to impute immune infiltration. Results Molecular pathways that play important roles in RA were obtained: Toll-like receptors, AMPK, MAPK, TNF, FoxO, TGF-beta, PI3K and NF-κB pathways, Ten hub genes: Ccr1, Ccr2, Ccr5, Ccr7, Cxcl5, Cxcl6, Cxcl13, Ccl13, Adcy2, and Pnoc. among which Adcy2 and Pnoc have not been reported in RA studies, suggesting that they may be worthy targets for further study. It was also found that among the synoviocytes in RA, the proportions of plasma cells, CD8 T cells, follicular helper T cells, monocytes, γ delta T cells, and M0 macrophages were higher, while the proportions of CD4 memory resting T cells, regulatory T cells (Tregs), activated NK cells, resting dendritic cells, M1 macrophages, eosinophils, activated mast cells, resting mast cells were lower in proportion, and each cell played an important role in RA. Conclusions This study may help understand the key genes, molecular pathways, the role of inflammatory immune infiltrating cells in RA’s pathogenesis and provide new targets and ideas for the diagnosis and personalized treatment of RA.

scholarly journals Comprehensive Analysis of RNA Expression Profile Identifies Hub miRNA-circRNA Interaction Networks in the Hypoxic Ischemic Encephalopathy

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Lin Wei ◽  
Xia Li ◽  
Lijuan Wang ◽  
Yanyan Song ◽  
Hongmei Dong
Keyword(s):  
Network Analysis ◽  
Expression Profile ◽  
Bioinformatics Analysis ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Hypoxic Ischemic Encephalopathy ◽  
Circular Rnas ◽  
Neutrophil Degranulation ◽  
Ischemic Encephalopathy

Hypoxic ischemic encephalopathy (HIE) is classified as a sort of serious nervous system syndrome that occurs in the early life period. Noncoding RNAs had been confirmed to have crucial roles in human diseases. So far, there were few systematical and comprehensive studies towards the expression profile of RNAs in the brain after hypoxia ischemia. In this study, 31 differentially expressed microRNAs (miRNAs) with upregulation were identified. In addition, 5512 differentially expressed mRNAs, long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) were identified in HIE groups. Bioinformatics analysis showed these circRNAs and mRNAs were significantly enriched in regulation of leukocyte activation, response to virus, and neutrophil degranulation. Pathway and its related gene network analysis indicated that HLA − DPA1, HLA − DQA2, HLA − DQB1, and HLA − DRB4 have a more crucial role in HIE. Finally, miRNA-circRNA-mRNA interaction network analysis was also performed to identify hub miRNAs and circRNAs. We found that miR-592 potentially targeting 5 circRNAs, thus affecting 15 mRNA expressions in HIR. hsa_circ_0068397 and hsa_circ_0045698 were identified as hub circRNAs in HIE. Collectively, using RNA-seq, bioinformatics analysis, and circRNA/miRNA interaction prediction, we systematically investigated the differentially expressed RNAs in HIE, which could give a new hint of understanding the pathogenesis of HIE.

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