scholarly journals Identification of Prognostic Biomarkers of Potential Hub Genes in Urothelial Carcinoma and Function in Microenvironment

2021 ◽  
Author(s):  
Wei Chu ◽  
Bing Zhang ◽  
Haifeng Gong ◽  
Qianqian Zhao ◽  
Jun Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Urothelial Carcinoma ◽  
Checkpoint Inhibitors ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Database Analysis ◽  
Protein Levels ◽  
Normal Tissues

Abstract Background Urothelial carcinoma (UC) is the most common histological type of urinary system. In the past decades, despite the advances in UC diagnosis and therapy, there are still challenges to improve the overall survival (OS) of UC patients. PD-L1 inhibitor and PD-1 inhibitor have been approved for treating invasive UC, however, only about 20% of patients with metastatic UC show clinical benefits from immune checkpoint inhibitors. Therefor, bioinformatics tools were utilized to screen prognostic-related biomarkers, and analyze their relationship with immunocyte in UC, hoping to provide new ideas for the clinical treatment of UC patients.Methods Three gene expression profiles (i.e. GSE32548, GSE32894 and GSE48075) were selected from GEO, and divide them into invasive and superficial UC group for study. NetworkAnalyst tool was used to construct gene regulatory network of DEGs, while DAVID and Metascape were utilized to perform gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEGs. The hub genes were screened by STRING and cytoscape, and the ONCOMINE, GEPIA, UALCAN, cBioPortal and HPA databases were used to analyze the expression differences and survival curves of UC at the DNA, RNA, protein levels and protein levels. TIMER was used to analyze the relationship between hub genes and immunocyte infiltration.Results In total, 63 DEGs were identified from the GEO database of UC, of which 31 and 32 were up-and down-regulated. GO/KEGG pathway analysis identified DEGs were mainly enriched in the collagen catabolic process, extracellular matrix (ECM) organization, ECM structural constituent and ECM-receptor interaction. Nine hub genes (i.e. COL1A1, COL1A2, COL3A1, COL5A2, MMP9, POSTN, SPP1, VCAN and THBS2) upregulated in invasive UC compared with superficial UC were identified. cBioportal database analysis showed that 35% of UC patients presented genetic variants in the hub genes, of which amplification and deletion mutations were the most common. ONCOMINE and UALCAN database analysis showed that the mRNA expression of all hub genes in invasive UC was significantly higher than that in superficial UC and normal tissues. HPA database analysis showed that there was up-regulation of COL3A1, SPP1, POSTN and VCAN protein in UC tissues than in normal tissues. GEPIA showed that COL1A2, COL3A1, THBS2, and VCAN were positively correlated with the OS rate among patients with UC (P < 0.05). UALCAN showed that UC patients with high expression of COL1A1, COL1A2, COL5A2 and POSTN had a poorer prognosis (P < 0.05). TRRUST database analysis indicated that there was a significant correlation between the expression of the hub genes and the infiltration of CD4 + T cells, CD8 + T cells, macrophages, neutrophils and dendritic cells.Conclusion Hub genes played important roles in pathogenesis and treatment prognosis of UC. Hub genes analysis provides new predictive biomolecules for UC immunotherapy and prognosis judgment.

scholarly journals Identification of Prognostic Biomarkers of Potential Hub Genes in Urothelial Carcinoma and Function in Microenvironment

2021 ◽  
Author(s):  
Wei Chu ◽  
Bing Zhang ◽  
Haifeng Gong ◽  
Qianqian Zhao ◽  
Jun Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Urothelial Carcinoma ◽  
Checkpoint Inhibitors ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Database Analysis ◽  
Normal Tissues

Abstract Background: Urothelial carcinoma (UC) is the most common histological type of urinary system. In the past decades, despite the advances in UC diagnosis and therapy, there are still challenges to improve the overall survival (OS) of UC patients. PD-L1 inhibitor and PD-1 inhibitor have been approved for treating invasive UC, however, only about 20% of patients with metastatic UC show clinical benefits from immune checkpoint inhibitors. Therefor, bioinformatics tools were utilized to screen prognostic-related biomarkers, and analyze their relationship with immunocyte in UC, hoping to provide new ideas for the clinical treatment of UC patients.Methods: Three gene expression profiles (i.e. GSE32548, GSE32894 and GSE48075) were selected from GEO, and divide them into invasive and superficial UC group for study. NetworkAnalyst tool was used to construct gene regulatory network of DEGs, while DAVID and Metascape were utilized to perform GO/KEGG enrichment analysis of DEGs. The hub genes were screened by STRING and cytoscape, and the ONCOMINE, GEPIA, UALCAN, cBioPortal and HPA databases were used to analyze the expression differences at the DNA, RNA, protein levels and prognostic of UC. TIMER was used to analyze the relationship between hub genes and immunocyte infiltration.Results: In total, 63 DEGs were identified from the GEO database of UC, of which 31 and 32 were up-and down-regulated. GO/KEGG pathway analysis identified DEGs were mainly enriched in the collagen catabolic process, extracellular matrix (ECM) organization, ECM structural constituent and ECM-receptor interaction. Nine hub genes (i.e. COL1A1, COL1A2, COL3A1, COL5A2, MMP9, POSTN, SPP1, VCAN and THBS2) upregulated in invasive UC compared with superficial UC were identified. cBioportal database analysis showed that 35% of UC patients presented genetic variants in the hub genes, of which amplification and deletion mutations were the most common. ONCOMINE and UALCAN database analysis showed that the mRNA expression of all hub genes in invasive UC was significantly higher than that in superficial UC and normal tissues. HPA database analysis showed that there was up-regulation of COL3A1, SPP1, POSTN and VCAN protein in UC tissues than in normal tissues. GEPIA showed that COL1A2, COL3A1, THBS2, and VCAN were positively correlated with the OS rate among patients with UC (P < 0.05). UALCAN showed that UC patients with high expression of COL1A1, COL1A2, COL5A2 and POSTN had a poorer prognosis (P < 0.05). TRRUST database analysis indicated that there was a significant correlation between the expression of the hub genes and the infiltration of CD4+T cells, CD8+T cells, macrophages, neutrophils and dendritic cells. Conclusion: Hub genes played important roles in pathogenesis and treatment prognosis of UC and they can provides new biomolecular predictions for immunotherapy and prognosis judgment of UC.

scholarly journals Identification of Prognostic Biomarkers of Potential Hub Genes in Urothelial Carcinoma and Function in Microenvironment

2021 ◽  
Author(s):  
Wei Chu ◽  
Bing Zhang ◽  
Haifeng Gong ◽  
Qianqian Zhao ◽  
Jun Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Urothelial Carcinoma ◽  
Checkpoint Inhibitors ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Database Analysis ◽  
Normal Tissues ◽  
Clinical Benefits ◽  
New Ideas ◽  
And Function

Abstract Background: Urothelial carcinoma (UC) is the most common histological type of urinary system. In the past decades, despite the advances in UC diagnosis and therapy, there are still challenges to improve the overall survival (OS) of UC patients. PD-L1 inhibitor and PD-1 inhibitor have been approved for treating invasive UC, however, only about 20% of patients with metastatic UC show clinical benefits from immune checkpoint inhibitors. Therefor, bioinformatics tools were utilized to screen prognostic-related biomarkers, and analyze their relationship with immunocyte in UC, hoping to provide new ideas for the clinical treatment of UC patients.Results: In total, 63 DEGs were identified from the GEO database of UC, of which 31 and 32 were up-and down-regulated. GO/KEGG pathway analysis identified DEGs were mainly enriched in the collagen catabolic process, extracellular matrix (ECM) organization, ECM structural constituent and ECM-receptor interaction. Nine hub genes (i.e. COL1A1, COL1A2, COL3A1, COL5A2, MMP9, POSTN, SPP1, VCAN and THBS2) upregulated in invasive UC compared with superficial UC were identified. cBioportal database analysis showed that 35% of UC patients presented genetic variants in the hub genes, of which amplification and deletion mutations were the most common. ONCOMINE and UALCAN database analysis showed that the mRNA expression of all hub genes in invasive UC was significantly higher than that in superficial UC and normal tissues. HPA database analysis showed that there was up-regulation of COL3A1, SPP1, POSTN and VCAN protein in UC tissues than in normal tissues. GEPIA showed that COL1A2, COL3A1, THBS2, and VCAN were positively correlated with the OS rate among patients with UC (P < 0.05). UALCAN showed that UC patients with high expression of COL1A1, COL1A2, COL5A2 and POSTN had a poorer prognosis (P < 0.05). TRRUST database analysis indicated that there was a significant correlation between the expression of the hub genes and the infiltration of CD4+T cells, CD8+T cells, macrophages, neutrophils and dendritic cells. Conclusion: Hub genes played important roles in pathogenesis and treatment prognosis of UC and they can provides new biomolecular predictions for immunotherapy and prognosis judgment of UC.

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 Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

2021 ◽  
Vol 24 (5-6) ◽  
pp. 267-279
Author(s):  
Xianyang Zhu ◽  
Wen Guo
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Wnt Signaling Pathway ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes

<b><i>Background:</i></b> This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. <b><i>Methods:</i></b> Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). <b><i>Results:</i></b> A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including <i>AKT1</i>, <i>CDC42</i>, <i>HLA-DQA2</i>, <i>TUBB</i>, <i>TWISTNB</i>, <i>GSK3B</i>, <i>FZD2</i>, <i>KLC1</i>, <i>GUSB</i>, and <i>RHOG</i>. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including <i>GSK3B, CDC42, AKT1, FZD2</i>, and <i>GUSB</i>) were identified. <b><i>Conclusion:</i></b> In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that <i>GSK3B, CDC42, AKT1, FZD2</i>, <i>and GUSB</i> may be key genes related to the progress of OA and may become promising therapeutic targets.

scholarly journals Identification of Key Genes and Pathways in SARS-CoV-2 Infection using Bioinformatics Analysis

2020 ◽  
Author(s):  
Zhe Wang ◽  
Chenhao Jiang ◽  
Xuxuan Zhang ◽  
Yingna Zhang ◽  
Yan Ren ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Expression Profiles ◽  
Severe Pneumonia ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Airway Epithelial ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Human Airway

Abstract Background: Coronavirus disease 2019 (COVID-19) is a disease that causes fatal disorders including severe pneumonia. Our study aimed to utilize bioinformatics method to analyze the expression profiling by high throughput sequencing in human bronchial organoids/primary human airway epithelial infected with SARS-CoV-2 to identify the potentially crucial genes and pathways associated with COVID-19.Methods: We analyzed microarray datasets GSE153970 and GSE150819 derived from the GEO database. Firstly, the Differentially expressed genes （DEGs） in human bronchial organoids/primary human airway epithelial infected with SARS-CoV-2. Next, the DEGs were used for GO and KEGG pathway enrichment analysis. Then, the PPI network was constructed and Cytoscape was used to find the key genes.Results: Gene expression profiles of GSE153970 and GSE150819, in all 12 samples were analyzed. A total of 145 DEGs and 5 hub genes were identified in SARS-CoV-2. Meanwhile, we found that the 145 genes are associated with immune responses and the top 5 hub genes including CXCL8, CXCL1, CXCL2, CCL20, and CSF2 were mainly related to leukocyte migration, endoplasmic reticulum lumen, receptor ligand activity. In addition, the results also showed that the hub genes were associated with Cytokine−cytokine receptor interaction, IL−17 signaling pathway, and Rheumatoid arthritis in SARS-CoV-2 infection.Conclusion: The five crucial genes consisting of CXCL8, CXCL1, CXCL2, CCL20, and CSF2 were considered as hub genes of SARS-CoV-2, which may be used as diagnostic biomarkers or molecular targets for the treatment of SARS-CoV-2. It is evidenced that bioinformatics analyses in SARS-CoV-2 can be useful for understanding the underlying molecular mechanism and exploring effective therapeutic targets.

scholarly journals Identification of Hub Genes Associated with Melanoma Development by Comprehensive Bioinformatics Analysis

2020 ◽  
Author(s):  
Jie Jiang ◽  
Guoyong Xu ◽  
Tuo Liang ◽  
Chaojie Yu ◽  
Shian Liao ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
High Expression ◽  
Hub Genes ◽  
Gene Therapies ◽  
Protein Levels ◽  
Normal Tissues ◽  
Melanoma Diagnosis ◽  
Melanoma Patients ◽  
Further Development ◽  
Core Genes

Abstract Introduction: The aim of this study was to identify important genes associated with melanoma for further development of new target gene therapies and to analyze their significance in relation to prognosis.Materials and methods: Gene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO and KEGG enrichment analysis as well as construction of the PPI, visualized with Cytoscape, and screened for the top 10 Hub genes using CytoHubba. We verified the protein levels of the Hub gene using the HPA database.Results: A total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1, EXO1, KIF20A, TPX2 and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). The HPA database results showed that FOXM1, KIF20A, TPX2 and CDC20 showed upregulated expression in melanoma compared to normal tissues.Conclusion: FOXM1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with low prognosis of melanoma patients, and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.

scholarly journals Identification of Significant Genes And Pathways In ARDS Via Bioinformatical Analysis

2021 ◽  
Author(s):  
Weina Lu ◽  
Ran Ji
Keyword(s):  
Cellular Response ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Signal Pathways ◽  
Ppi Network ◽  
Hub Genes

Abstract Background and Aims: Acute respiratory distress syndrome (ARDS) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS.Methods: Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL 339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis, protein-protein interaction (PPI) network construction, Transcription Factors (TFs) forecasting, and their expression in varied organs.Results: A total of 39 differential expressed genes were screened from the datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, cellular response to interferon-beta and also involved in some signal pathways, including cytokine-cytokine receptor interaction, salmonella infection, legionellosis, chemokine, and Toll-like receptor signal pathway. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions: This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

scholarly journals Comprehensive Analysis of a circRNA-miRNA-mRNA Network to Reveal Potential Inflammation-Related Targets for Gastric Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
YunXia Liu ◽  
YeFeng Xu ◽  
Feng Xiao ◽  
JianFeng Zhang ◽  
YiQing Wang ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Gene Networks ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Genetic Alterations ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes

Gastric cancer (GC) is the most common malignancy of the stomach. This study was aimed at elucidating the regulatory network of circRNA-miRNA-mRNA and identifying the precise inflammation-related targets in GC. The expression profiles of GSE83521, GSE78091, and GSE33651 were obtained from the GEO database. Interactions between miRNAs and circRNAs were investigated by the Circular RNA Interactome, and targets of miRNAs were predicted with miRTarBase. Then, a circRNA/miRNA/mRNA regulatory network was constructed. Also, functional enrichment analysis of selected differentially expressed genes (DEGs) was performed. The inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. And a protein-protein interaction (PPI) network of DE mRNAs was constructed with STRING and Cytoscape to identify hub genes. The genetic alterations, neighboring gene networks, expression levels, and the poor prognosis of hub genes were investigated in cBioPortal, Oncomine, and Human Protein Atlas databases and Kaplan-Meier plotter, respectively. A total of 10 DE miRNAs and 33 DEGs were identified. The regulatory network contained 26 circRNAs, 10 miRNAs, and 1459 mRNAs. Functional enrichment analysis revealed that the selected 33 DEGs were involved in negative regulation of fat cell differentiation, response to wounding, extracellular matrix- (ECM-) receptor interaction, and regulation of cell growth pathways. THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were selected as inflammation-related hub genes of GC in the PPI network. The genetic alterations in these hub genes were related to amplification and missense mutations. Furthermore, the genes RYR2, ERBB2, PI3KCA, and HELZ2 were connected to hub genes in this study. The hub gene levels in clinical specimens were markedly upregulated in GC tissues and correlated with poor overall survival (OS). Our results suggest that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were associated with the pathogenesis of gastric carcinogenesis and may serve as biomarkers and inflammation-related targets for GC.

scholarly journals Identification of significant alteration genes, pathways and TFs induced by LPS in ARDS via bioinformatical analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weina Lu ◽  
Ran Ji
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Signal Pathways ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background and aims Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS. Methods Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis in DAVID database, protein–protein interaction (PPI) network construction evaluated by the online database STRING, Transcription Factors (TFs) forecasting based on the Cytoscape plugin iRegulon, and their expression in varied organs in The Human Protein Atlas. Results A total of 39 differential expressed genes were screened from the two datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, and also involved in some signal pathways, including cytokine–cytokine receptor interaction, Salmonella infection, Legionellosis, Chemokine, and Toll-like receptor signal pathway with an integrated analysis. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

scholarly journals Identification of key genes in calcific aortic valve disease via weighted gene co-expression network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jin-Yu Sun ◽  
Yang Hua ◽  
Hui Shen ◽  
Qiang Qu ◽  
Jun-Yan Kan ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Network Analysis ◽  
Aortic Valve ◽  
Aortic Valve Disease ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Valve Disease ◽  
Hub Genes ◽  
Calcific Aortic Valve Disease ◽  
Underlying Mechanisms

Abstract Background Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear. Methods The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by ‘limma’ and ‘weighted gene co-expression network analysis (WGCNA)’ package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein–protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation. Results We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively. Conclusions This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.

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