scholarly journals Identification of Key Genes and Immune Infiltrate in Nonalcoholic Steatohepatitis: A Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhen-yu Jiang ◽  
Yi Zhou ◽  
Lu Zhou ◽  
Shao-wei Li ◽  
Bang-mao Wang
Keyword(s):  
T Cells ◽  
Nonalcoholic Steatohepatitis ◽  
Γδ T Cells ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Normal Tissues ◽  
Key Genes

Background. Nonalcoholic steatohepatitis (NASH) can progress to cirrhosis and hepatic carcinoma and is closely associated with changes in the neurological environment. The discovery of new biomarkers would aid in the treatment of NASH. Methods. Data GSE89632 were downloaded from the Gene Expression Omnibus (GEO) database, and R package “limma” was used to identify differentially expressed genes (DEGs) for NASH vs. normal tissues. The STRING database was used to construct a protein-protein interaction (PPI) network, and the Cytoscape software program (Version 3.80) was used to visualize the PPI network and identify key genes. The immune infiltration of NASH was determined using the R package “CIBERSORT”. Results. We screened 41 DEGs. GO and KEGG enrichment analyses of the DEGs revealed the enrichment of pathways related to NAFLD steatosis and inflammation. A PPI network analysis was also performed on the DEGs, and seven genes (MYC, CXCL8, FOS, SOCS1, SOCS3, IL6, and PTGS2) were identified as hub genes. An immune infiltration assessment revealed that macrophages M2, memory resting CD4+ T cells, and γΔ T cells play important roles in the immune microenvironment of NASH, which may be mediated by the seven identified hub genes.

scholarly journals Identification of Hub Genes and Immune Infiltration in Psoriasis by Bioinformatics Method

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenxing Su ◽  
Yuqian Wei ◽  
Biao Huang ◽  
Jiang Ji
Keyword(s):  
T Cells ◽  
B Cells ◽  
Nk Cells ◽  
Signaling Pathway ◽  
Control Sample ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Chemokine Signaling

BackgroundPsoriasis is a chronic, prolonged, and recurrent skin inflammatory disease. However, the pathogenesis of psoriasis is not completely clear, thus we aimed to explore potential molecular basis of it.MethodsTwo datasets were downloaded from the Gene Expression Omnibus database. After identifying the differentially expressed genes of psoriasis skin lesion samples and healthy controls, three kinds of analyses, namely functional annotation, protein-protein interaction (PPI) network, and immune infiltration analyses, were performed.ResultsA total of 152 up-regulated genes and 38 down-regulated genes were selected for subsequent analyses. Evaluation of the PPI network identified the most important module containing 13 hub genes. Gene ontology analysis showed that the hub genes have a significant enrichment effect on positive regulation of cell migration, defense response to the other organism and epithelial cell differentiation. KEGG signaling pathway analysis showed that the hub genes were significantly enriched in chemokine signaling, Toll-like receptor signaling pathway, and IL-17 signaling pathway. Compared with the normal control sample, naive B cells, CD8+ T cells, activated memory CD4+ T cells, follicular helper T cells, gamma delta T cells, resting NK cells, monocytes, M0 macrophages, M1 macrophages, activated dendritic cells and neutrophils infiltrated more, while memory B cells, naive CD4+ T cells, regulatory T cells (Tregs), activated NK cells, resting mast cells, and eosinophils infiltrated less.ConclusionTo conclude, the hub genes and pathways identified from psoriasis lesions and normal controls along with the immune infiltration profile may provide new insights into the study of psoriasis.

scholarly journals Identification of key genes and immune infiltration modulated by CPAP in obstructive sleep apnea by integrated bioinformatics analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0255708
Author(s):  
Cheng Fan ◽  
Shiyuan Huang ◽  
Chunhua Xiang ◽  
Tianhui An ◽  
Yi Song
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Mononuclear Cells ◽  
Upper Airway ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Peripheral Blood Mononuclear ◽  
Immune Infiltration ◽  
Obstructive Sleep ◽  
Key Genes

Patients with obstructive sleep apnea (OSA) experience partial or complete upper airway collapses during sleep resulting in nocturnal hypoxia-normoxia cycling, and continuous positive airway pressure (CPAP) is the golden treatment for OSA. Nevertheless, the exact mechanisms of action, especially the transcriptome effect of CPAP on OSA patients, remain elusive. The goal of this study was to evaluate the longitudinal alterations in peripheral blood mononuclear cells transcriptome profiles of OSA patients in order to identify the hub gene and immune response. GSE133601 was downloaded from Gene Expression Omnibus (GEO). We identified black module via weighted gene co-expression network analysis (WGCNA), the genes in which were correlated significantly with the clinical trait of CPAP treatment. Finally, eleven hub genes (TRAV10, SNORA36A, RPL10, OBP2B, IGLV1-40, H2BC8, ESAM, DNASE1L3, CD22, ANK3, ACP3) were traced and used to construct a random forest model to predict therapeutic efficacy of CPAP in OSA with a good performance with AUC of 0.92. We further studied the immune cells infiltration in OSA patients with CIBERSORT, and monocytes were found to be related with the remission of OSA and partially correlated with the hub genes identified. In conclusion, these key genes and immune infiltration may be of great importance in the remission of OSA and related research of these genes may provide a new therapeutic target for OSA in the future.

scholarly journals Identification of key genes and immune infiltration modulated by CPAP in Obstructive sleep apnea by integrated bioinformatics analysis

2021 ◽  
Author(s):  
yi Song ◽  
Cheng Fan ◽  
Shiyuan Huang ◽  
Chunhua Xiang ◽  
Tianhui An
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Mononuclear Cells ◽  
Upper Airway ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Peripheral Blood Mononuclear ◽  
Immune Infiltration ◽  
Obstructive Sleep ◽  
Key Genes

Patients with obstructive sleep apnea (OSA) experience partial or complete upper airway collapses during sleep resulting in nocturnal hypoxia-normoxia cycling. And continuous positive airway pressure (CPAP) is the golden treatment for OSA. Nevertheless, the exact mechanisms of action, especially the transcriptome effect of CPAP on OSA patients, remains elusive. The goal of this study was to evaluate the longitudinal alterations in peripheral blood mononuclear cells transcriptome profiles of OSA patients in order to identify the hub gene and immune response. GSE133601 were downloaded from Gene Expression Omnibus (GEO). We identified black module via weighted gene co-expression network analysis (WGCNA), the genes in which were correlated significantly with the clinical trait of CPAP treatment. Finally, eleven hub genes (TRAV10, SNORA36A, RPL10, OBP2B, IGLV1-40, H2BC8, ESAM, DNASE1L3, CD22, ANK3, ACP3) were traced and used to construct a random forest model to predict therapeutic efficacy of CPAP in OSA with a good performance with AUC of 0.92. We further studied the immune cells infiltration in OSA patients with CIBERSORT, and monocytes were found to be related with the remission of OSA and partially correlated with the hub genes identified. In conclusion, these key genes and immune infiltration may be of great importance in the remission of OSA and related research of these genes may provide a new therapeutic target for OSA in the future.

scholarly journals Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

2020 ◽  
Author(s):  
Ming Cao ◽  
Chen Shen ◽  
Jie Zhu ◽  
YuHai Wang
Keyword(s):  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Benign Meningioma ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

scholarly journals Identification of Key Genes and Pathways in Breast Cancer Based on Bioinformatic Analysis

2020 ◽  
Author(s):  
Chi Pan ◽  
Qingtao Ni
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Human Tumor ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Cancer Type ◽  
Hub Genes ◽  
Ampk Signaling ◽  
Key Genes

Abstract Breast cancer(BC) is the most frequent cancer type in women. However, the pathogenesis of BC is still not well understood. Thus, we aim to explore key genes and pathways in relation to BC. We used the Gene Expression Omnibus (GEO) database to identify the differently expression of genes in the carcinogenesis and progression of BC. Then, bioinformatics analysis was performed to determine the key genes targets and pathways associated with BC. The gene expression profile of the hub genes in human tumor was displayed using GEPIA. Finally, the expression of hub genes, correlation between genes and miRNA were screened via the miRDB, MirTarBase and DIANA Tools. We screened 159 downregulated genes and 55 upregulated genes in BC patients among the 4 datasets. The enriched functions of the DEGs involved in cell proliferation, positive regulation of Akt signaling and extracellular exosome, PPAR signaling pathway and AMPK signaling pathway. 3 hub genes were screened out by construction of PPI network. MELK were found to be upregulated, and CLU and NTRK2 were downregulated. Further verification showed that MELK displayed higher levels in almost all tumors. We found correlation between these hube genes and the miRNAs. All in all, three key genes closely related to the incidence of BC were identified, and the results could provide new potential molecular targets for the diagnosis and treatment of BC. In particular, MELK is regulated by multiple miRNAs and participate in the development of BC.

scholarly journals Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9633
Author(s):  
Jie Meng ◽  
Rui Su ◽  
Yun Liao ◽  
Yanyan Li ◽  
Ling Li
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

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.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

Identification of Potential Genes Associated with Vemurafenib Efficacy and Melanoma Prognosis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Qi ◽  
GuiE Ma
Keyword(s):  
Signaling Pathway ◽  
Estrogen Signaling ◽  
Akt Signaling Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Ampk Signaling ◽  
Melanoma Patients ◽  
Key Genes ◽  
Neurotrophin Signaling ◽  
Upregulated Genes

Objective: This work aimed to investigate the molecular mechanisms underlying the efficacy of vemurafenib as a treatment for melanoma. Methods: The GSE52882 dataset, which includes A375 and A2058 melanoma cell lines treated with vemurafenib and dimethyl sulfoxide (DMSO), and clinical information associated with melanoma patients, were acquired from the Gene Expression Omnibus (GEO) database and University of California Santa Cruz (UCSC), respectively. Functional enrichment analysis, protein-protein interaction (PPI) network construction, sub-module analysis, and transcriptional regulation analysis were performed on overlapping differentially expressed genes (DEGs) identified in both cell lines. Finally, we performed a survival analysis based on the genes identified. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Conclusion: MMP2, CXCL8, PIK3R3, ITGB3, and LEF1 may play roles in the efficacy of vemurafenib treatment in melanoma; for example, MMP2 and PIK3R3 are likely associated with vemurafenib resistance. These findings will contribute to the development of novel therapies for melanoma.

Sign in / Sign up

Export Citation Format

Share Document