scholarly journals Bioinformatic Identification of Hub Genes and Biological Pathways for Sepsis-Associated Acute Lung Injury

2021 ◽  
Author(s):  
Chao Zhang ◽  
Feng Xu ◽  
Fang Fang
Keyword(s):  
Gene Expression ◽  
Acute Lung Injury ◽  
Lung Injury ◽  
Function Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Biological Pathways ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Sepsis-associated acute lung injury (ALI) is a potentially lethal complication associated with a poor prognosis and high mortality worldwide, especially in the outbreak of COVID-19. However, the fundamental mechanisms of this complication were still not fully elucidated. Thus, we conducted this study to identify hub genes and biological pathways of sepsis-associated ALI, mainly focus on two pathways of LPS and HMGB1. Methods: Gene expression profile GSE3037 were downloaded from Gene Expression Omnibus (GEO) database, including 8 patients with sepsis-induced acute lung injury, with 8 unstimulated blood neutrophils, 8 LPS- induced neutrophils and 8 HMGB1-induced neutrophils. Differentially expressed genes (DEGs) identifications, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, Gene Set Enrichment Analysis (GSEA) and protein-protein interaction (PPI) network constructions were performed to obtain hub genes and relevant biological pathways.Results: We identified 534 and 317 DEGs for LPS- and HMGB1-induced ALI, respectively. The biological pathways involved in LPS- and HMGB1-induced ALI were also identified accordingly. By PPI network analysis, we found that ten hub genes for LPS-induced ALI (CXCL8, TNF, IL6, IL1B, ICAM1, CXCL1, CXCL2, IL1A, IL1RN and CXCL3) and another ten hub genes for HMGB1-induced ALI (CCL20, CXCL2, CXCL1, CCL4, CXCL3, CXCL9, CCL21, CXCR6, KNG1 and SST). Furthermore, by combining analysis, the results revealed that genes of TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3 were potential biomarkers for sepsis-associated ALI. Conclusions: Our study revealed that ten hub genes associated with sepsis-induced ALI were TNF, CCL20, IL1B, NFKBIA, CCL4, PTGS2, TNFAIP3, CXCL2, CXCL1 and CXCL3, which may serve as genetic biomarkers and be further verified in prospective experimental trials.

scholarly journals Transcriptome-Wide Gene Expression in a Murine Model of Ventilator-Induced Lung Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Zhao Li ◽  
Guoshao Zhu ◽  
Chen Zhou ◽  
Hui Wang ◽  
Le Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Injury ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Ventilator Induced Lung Injury ◽  
Significant Difference ◽  
Tf Gene

Background. Mechanical ventilation could lead to ventilator-induced lung injury (VILI), but its underlying pathogenesis remains largely unknown. In this study, we aimed to determine the genes which were highly correlated with VILI as well as their expressions and interactions by analyzing the differentially expressed genes (DEGs) between the VILI samples and controls. Methods. GSE11434 was downloaded from the gene expression omnibus (GEO) database, and DEGs were identified with GEO2R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using DAVID. Next, we used the STRING tool to construct protein-protein interaction (PPI) network of the DEGs. Then, the hub genes and related modules were identified with the Cytoscape plugins: cytoHubba and MCODE. qRT-PCR was further used to validate the results in the GSE11434 dataset. We also applied gene set enrichment analysis (GSEA) to discern the gene sets that had a significant difference between the VILI group and the control. Hub genes were also subjected to analyses by CyTargetLinker and NetworkAnalyst to predict associated miRNAs and transcription factors (TFs). Besides, we used CIBERSORT to detect the contributions of different types of immune cells in lung tissues of mice in the VILI group. By using DrugBank, small molecular compounds that could potentially interact with hub genes were identified. Results. A total of 141 DEGs between the VILI group and the control were identified in GSE11434. Then, seven hub genes were identified and were validated by using qRT-PCR. Those seven hub genes were largely enriched in TLR and JAK-STAT signaling pathways. GSEA showed that VILI-associated genes were also enriched in NOD, antigen presentation, and chemokine pathways. We predicted the miRNAs and TFs associated with hub genes and constructed miRNA-TF-gene regulatory network. An analysis with CIBERSORT showed that the proportion of M0 macrophages and activated mast cells was higher in the VILI group than in the control. Small molecules, like nadroparin and siltuximab, could act as potential drugs for VILI. Conclusion. In sum, a number of hub genes associated with VILI were identified and could provide novel insights into the pathogenesis of VILI and potential targets for its treatment.

scholarly journals Identification of Candidate Biomarkers Related to Diagnosis and Prognosis of Thyroid Cancer via Transcriptomic Profile Analysis

2020 ◽  
Author(s):  
Qiang Li ◽  
Sheng Jiang ◽  
Tienan Feng ◽  
Tengteng Zhu ◽  
Biyun Qian
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Profile Analysis ◽  
Function Analysis ◽  
Disease Free Survival ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Transcriptomic Profile ◽  
Diagnosis And Prognosis

Abstract BackgroundThe detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Compared with other cancers, the gene profile plays a more prominent role in the diagnosis and treatment of TC. MethodsFour datasets from Gene Expression Omnibus (GEO) was used to perform transcriptomic profile analysis. The overlapping differentially expressed genes (DEGs) were analyzed by R package “limma” and “RobustRankAggreg”. Then the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from Gene Expression Profiling Interactive Analysis (GEPIA) database, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes.ResultsA total of 80 DEGs (34 upregulated and 46 downregulated) were identified. Then FN1, TIMP1, ITGA2, and KIT were considered hub genes, which had a high degree of connectivity in the PPI network. GEPIA identified that FN1, TIMP1, and ITGA2 were upregulated, and KIT was downregulated. Upregulations of FN1 expression (P=0.024) and ITGA2 expression (P=0.029) and downregulation of KIT expression (P=0.012) increased risks of decreased DFS for patients. IHC showed that the expression of FN1, TIMP1, and ITGA2 protein were upregulated, while the expression of KIT protein was downregulated in the TC clinical specimens. Besides, five hub genes were enriched in the PI3K-Akt signaling pathway and ECM-receptor interaction.ConclusionsIn summary, these hub genes were potential biomarkers of diagnosis and prognosis of TC.

scholarly journals Identification of Candidate Biomarkers Correlated Poorer Prognosis of Breast Cancer via Bioinformatics Method

2021 ◽  
Author(s):  
Gang Chen ◽  
Mingwei Yu ◽  
Jianqiao Cao ◽  
Huishan Zhao ◽  
Yuanping Dai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Molecular Therapy ◽  
Hub Genes

Abstract Background: Breast cancer (BC) is a malignancy with a high incidence among women in the world, and it is very urgent to identify significant biomarkers and molecular therapy methods.Methods: Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed. Additionally, hub genes were screened by constructing a protein-protein interaction (PPI) network. Then, we explored the prognostic values and molecular mechanism of these hub genes Kaplan-Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). Results: 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. GO and KEGG pathway analysis revealed that DEGs were mainly related to cell cycles and cell proliferation. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree of genes were selected, among which, 11 hub gene were significantly correlated with the prognosis of patients with BC. From GSEA reviewed correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. Conclusion: this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

scholarly journals Screening and Identification of Potential Key Biomarkers in Psoriasis : Evidence From Bioinformatic Analysis

2021 ◽  
Author(s):  
Yang Yang ◽  
Shaoqiong Xie ◽  
Jiajing Lu ◽  
Suwei Tang
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Lesional Skin ◽  
Signaling Axis ◽  
Skin Samples

Abstract Background: Psoriasis is a chronic and prevalent skin condition brought on by various genetic and external factors. Till date, there is no cure for this disease. It is, therefore, crucial to examine the underlying mechanisms leading up to psoriasis. The goal of our study was to explore the mechanistic pathways involved in the molecular pathogenesis of psoriasis.Methods: Using Gene Expression Omnibus (GEO), we performed an extensive analysis of the transcript expression profile in psoriasis patients. The datasets GSE13355, GSE30999, and GSE106992, containing 239 pairs of normal and psoriatic skin samples were arbitrarily assigned to two non-overlapping cohorts for cross-validated differential gene expression analysis. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and gene set enrichment analysis (GSEA) were employed for interpretation, visualization, and unified recognition. The STRING database was used to construct the protein–protein interaction (PPI) network and the hub genes were established using Cytoscape. Additionally, both differentially regulated genes and functional likeness of hub genes were further examined in terms of gene activation and functional outcome. The correlation between normal tissue and infiltrating immune cells was analyzed by CIBERSORT. Moreover, ROC analysis was performed to distinguish between skin lesion samples and skin non-lesion samples. In addition, a signaling axis involving lnRNA, miRNA, mRNA, and ceRNA was generated with information from the DEmiRNA, DElncRNA, and DEmiRNA-DEmRNA relationship. Lastly, immunohistochemical evaluations were used to analyze the highest expression of single gene in the whole body within the Human Protein Atlas (HPA) database.Results: The genetic profiles of 239 pairs of normal and lesional skin samples were downloaded from three datasets in the GEO database. PPI network revealed a tight interaction among 197 differentially expressed genes (DEGs). Moreover, gene ontology analyses indicated that psoriasis-related DEGs mostly included viral defense genes, type I interferon axis, and its corresponding cellular responses. The Kyoto encyclopedia of genes and the DEGs enrichment analysis showed involvement of the NOD-like receptor signaling network, cytokine-cytokine receptor binding, and IL-17 signaling axis in psoriasis verses non-psoriatic tissues. GSEA analysis demonstrated that CXCL8 was only enriched in the "complement characteristic" pathway. ROC curves indicated that CXCL8 expression was highly effective in classifying both lesional and non-lesional skin samples (with AUC 0.941, 0,935, and 0.794 for GSE13355, GSE30999, and GSE 106992). Furthermore, CIBERSORT database indicated that CXCL8 was correlated to 22 types of infiltrating immune cells. In addition, 6 miRNAs were predicted to be related to CXCL8, including hsa-miR-1294, hsa-miR-140-3p, hsa-miR-185-5p, hsa-miR-4306, hsa-miR-4644, and hsa-miR-493-5p. Lastly, immunohistochemical analysis showed that CXCL8 was most widely distributed in lymphoid tissues.Conclusions: Based on our analysis, CXCL8 plays a key role in psoriatic development. Our comprehensive bioinformatics analysis of the GEO data provided new insights into the exploration of molecular mechanisms while searching for highly efficient therapeutic targets for treating psoriasis.

scholarly journals Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yanzhe Wang ◽  
Wenjuan Cai ◽  
Liya Gu ◽  
Xuefeng Ji ◽  
Qiusheng Shen
Keyword(s):  
Atrial Fibrillation ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Cell Counting ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Protein Protein Interaction ◽  
Endothelial Growth Factor

Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.

scholarly journals Prognostic values and prospective pathway signaling of MicroRNA-182 in ovarian cancer: a study based on gene expression omnibus (GEO) and bioinformatics analysis

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yaowei Li ◽  
Li Li
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated. Methods We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed. Results A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC. Conclusions Our study suggests that miR-182 is essential for the biological progression of OC.

scholarly journals Diagnostic biomarker candidates for pulpitis revealed by bioinformatics analysis of merged microarray gene expression datasets

2020 ◽  
Author(s):  
Ming Chen ◽  
Junkai Zeng ◽  
Yeqing Yang ◽  
Buling Wu
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Dental Pulp ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Gene Set Enrichment Analysis ◽  
Diagnostic Biomarker ◽  
Hub Genes ◽  
Geo Database

Abstract Background Pulpitis is known as an inflammatory disease classified by the level of inflammation. The existed traditional methods of evaluating status of dental pulp tissue in clinical practice still have some shortages and limitations. Immediate and accurate diagnosis of pulpitis is essential to the choice of treatment. Through integrating different datasets from Gene Expression Omnibus (GEO) database, we analyzed the merged expression matrix of pulpitis, aiming to identified biological pathways and diagnostic biomarker of pulpitis.Methods By integrating two datasets (GSE77459 and GSE92681) in GEO database using sva and limma packages, differentially expressed genes (DEGs) of pulpitis were identified. Then DEGs were used to analyze biological pathways of dental pulp inflammation with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the Gene Set Enrichment Analysis (GSEA). Protein–protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape.Results A total of 472 DEGs consisting of 396 upregulated and 76 downregulated genes were found in pulpitis tissue. DEGs in GO analysis were enriched in biological processes about inflammation and in KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signaling pathway and NF-κB signaling pathway. GSEA results provided further functional annotations including complement system, IL6/JAK/STAT3 signaling pathway and inflammatory response pathways. According to the degrees of nodes in PPI network, 10 hub genes were obtained and 8 diagnostic biomarker candidates were screened, including PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1.

scholarly journals Identification of Hub Genes in Tuberculosis via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tiancheng Zhang ◽  
Guihua Rao ◽  
Xiwen Gao
Keyword(s):  
Gene Expression ◽  
Pulmonary Tuberculosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Set Enrichment ◽  
Data Set ◽  
Hub Gene ◽  
The World

Background. Tuberculosis (TB) is a serious chronic bacterial infection caused by Mycobacterium tuberculosis (MTB). It is one of the deadliest diseases in the world and a heavy burden for people all over the world. However, the hub genes involved in the host response remain largely unclear. Methods. The data set GSE11199 was studied to clarify the potential gene network and signal transduction pathway in TB. The subjects were divided into latent tuberculosis and pulmonary tuberculosis, and the distribution of differentially expressed genes (DEGs) was analyzed between them using GEO2R. We verified the enriched process and pathway of DEGs by making use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The construction of protein-protein interaction (PPI) network of DEGs was achieved through making use of the Search Tool for the Retrieval of Interacting Genes (STRING), aiming at identifying hub genes. Then, the hub gene expression level in latent and pulmonary tuberculosis was verified by a boxplot. Finally, through making use of Gene Set Enrichment Analysis (GSEA), we further analyzed the pathways related to DEGs in the data set GSE11199 to show the changing pattern between latent and pulmonary tuberculosis. Results. We identified 98 DEGs in total in the data set GSE11199, 91 genes upregulated and 7 genes downregulated included. The enrichment of GO and KEGG pathways demonstrated that upregulated DEGs were mainly abundant in cytokine-mediated signaling pathway, response to interferon-gamma, endoplasmic reticulum lumen, beta-galactosidase activity, measles, JAK-STAT signaling pathway, cytokine-cytokine receptor interaction, etc. Based on the PPI network, we obtained 4 hub genes with a higher degree, namely, CTLA4, GZMB, GZMA, and PRF1. The box plot showed that these 4 hub gene expression levels in the pulmonary tuberculosis group were higher than those in the latent group. Finally, through Gene Set Enrichment Analysis (GSEA), it was concluded that DEGs were largely associated with proteasome and primary immunodeficiency. Conclusions. This study reveals the coordination of pathogenic genes during TB infection and offers the diagnosis of TB a promising genome. These hub genes also provide new directions for the development of latent molecular targets for TB treatment.

scholarly journals Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Hanxi Wan ◽  
Xinwei Huang ◽  
Peilin Cong ◽  
Mengfan He ◽  
Aiwen Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Expression Levels ◽  
Protein Protein Interaction

Idiopathic pulmonary fibrosis (IPF) is a progressive disease whose etiology remains unknown. The purpose of this study was to explore hub genes and pathways related to IPF development and prognosis. Multiple gene expression datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis (WGCNA) was performed and differentially expressed genes (DEGs) identified to investigate Hub modules and genes correlated with IPF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were performed on selected key genes. In the PPI network and cytoHubba plugin, 11 hub genes were identified, including ASPN, CDH2, COL1A1, COL1A2, COL3A1, COL14A1, CTSK, MMP1, MMP7, POSTN, and SPP1. Correlation between hub genes was displayed and validated. Expression levels of hub genes were verified using quantitative real-time PCR (qRT-PCR). Dysregulated expression of these genes and their crosstalk might impact the development of IPF through modulating IPF-related biological processes and signaling pathways. Among these genes, expression levels of COL1A1, COL3A1, CTSK, MMP1, MMP7, POSTN, and SPP1 were positively correlated with IPF prognosis. The present study provides further insights into individualized treatment and prognosis for IPF.

scholarly journals Study on potential differentially expressed genes in stroke by bioinformatics analysis

2021 ◽  
Author(s):  
Xitong Yang ◽  
Pengyu Wang ◽  
Shanquan Yan ◽  
Guangming Wang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Normal Control ◽  
Enrichment Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

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