Identification of Hub Genes Associated with Development of Lung Adenocarcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Jinglei Li ◽  
Wei Hou
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Normal Control ◽  
Gene Expression Analysis ◽  
Normal Control Group ◽  
Control Group ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

Abstract Purpose: Lung adenocarcinoma (LUAD) has high heterogeneity and poor prognosis, posing a major challenge to human health worldwide. Therefore, it is necessary to improve our understanding of the molecular mechanism of LUAD in order to be able to better predict its prognosis and develop new therapeutic strategies for target genes.Methods: The Cancer Genome Atlas and Gene Expression Omnibus, were selected to comprehensively analyze and explore the differences between LUAD tumors and adjacent normal tissues. Critical gene information was obtained through weighted gene co-expression network analysis (WGCNA), differential gene expression analysis, and survival analysis.Results: Using WGCNA and differential gene expression analysis, 29 differentially expressed genes were screened. The functional annotation analysis showed these genes to be mainly concentrated in heart trabecula formation, regulation of inflammatory response, collagen-containing extracellular matrix, and metalloendopeptidase inhibitor activity. Also, in the protein–protein interaction network analysis, 10 central genes were identified using Cytoscape's CytoHubba plug-in. The expression of CDH5, TEK, TIMP3, EDNRB, EPAS1, MYL9, SPARCL1, KLF4, and TGFBR3 in LUAD tissue was found to be lower than that in the normal control group, while the expression of MMP1 in LUAD tissue was higher than that in the normal control group. According to survival analysis, the low expression of MYL9 and SPARCL1 was correlated with poor overall survival in patients with LUAD. Finally, through the verification of the Oncomine database, it was found that the expression levels of MYL9 and SPARCL1 were consistent with the mRNA levels in LUAD samples, and both were downregulated.Conclusion: Two survival-related genes, MYL9 and SPARCL1, were determined to be highly correlated with the development of LUAD. Both may play an essential role in the development LUAD and may be potential biomarkers for its diagnosis and treatment in the future.

scholarly journals Identification of Hub Genes Associated With Development and Microenvironment of Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis and Differential Gene Expression Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Qingquan Bai ◽  
Haoling Liu ◽  
Hongyu Guo ◽  
Han Lin ◽  
Xuan Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Clinical Stage ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

A further understanding of the molecular mechanism of hepatocellular carcinoma (HCC) is necessary to predict a patient’s prognosis and develop new targeted gene drugs. This study aims to identify essential genes related to HCC. We used the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis to analyze the gene expression profile of GSE45114 in the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas database (TCGA). A total of 37 overlapping genes were extracted from four groups of results. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed on the 37 overlapping genes. Then, we used the STRING database to map the protein interaction (PPI) network of 37 overlapping genes. Ten hub genes were screened according to the Maximal Clique Centrality (MCC) score using the Cytohubba plugin of Cytoscape (including FOS, EGR1, EPHA2, DUSP1, IGFBP3, SOCS2, ID1, DUSP6, MT1G, and MT1H). Most hub genes show a significant association with immune infiltration types and tumor stemness of microenvironment in HCC. According to Univariate Cox regression analysis and Kaplan-Meier survival estimation, SOCS2 was positively correlated with overall survival (OS), and IGFBP3 was negatively correlated with OS. Moreover, the expression of IGFBP3 increased with the increase of the clinical stage, while the expression of SOCS2 decreased with the increase of the clinical stage. In conclusion, our findings suggest that SOCS2 and IGFBP3 may play an essential role in the development of HCC and may serve as a potential biomarker for future diagnosis and treatment.

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.

Differential gene expression analysis in Enchytraeus albidus exposed to natural and chemical stressors at different exposure periods

Ecotoxicology ◽  
2011 ◽  
Vol 21 (1) ◽  
pp. 213-224 ◽  
Author(s):  
Sara C. Novais ◽  
Clara F. Howcroft ◽  
Laura Carreto ◽  
Patrícia M. Pereira ◽  
Manuel A. S. Santos ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Differential Gene Expression Analysis ◽  
Differential Gene
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