Identification of Hub genes associated with infection of three lung cell lines by SARS-CoV-2 with integrated bioinformatics analysis.
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ABSTRACT:
SUBMITTER: Xie TA
PROVIDER: S-EPMC7579704 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
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Identification of Hub genes associated with infection of three lung cell lines by SARS-CoV-2 with integrated bioinformatics analysis.
Xie Tian-Ao TA Han Meng-Yi MY Su Xiao-Rui XR Li Hou-He HH Chen Ji-Chun JC Guo Xu-Guang XG
Journal of cellular and molecular medicine 20200914 20
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Project description:BackgroundAt the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity.MethodsThe gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein-protein interaction (PPI) network of DEGs and, in turn, to identify hub genes.ResultsA total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10.ConclusionThe results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.
| S-EPMC8677925 | biostudies-literature
Project description:An improved understanding of the molecular mechanism of colorectal adenocarcinoma is necessary to predict the prognosis and develop new target gene therapy strategies. This study aims to identify hub genes associated with colorectal adenocarcinoma and further analyze their prognostic significance. In this study, The Cancer Genome Atlas (TCGA) COAD-READ database and the gene expression profiles of GSE25070 from the Gene Expression Omnibus were collected to explore the differentially expressed genes between colorectal adenocarcinoma and normal tissues. The weighted gene co-expression network analysis (WGCNA) and differential expression analysis identified 82 differentially co-expressed genes in the collected datasets. Enrichment analysis was applied to explore the regulated signaling pathway in colorectal adenocarcinoma. In addition, 10 hub genes were identified in the protein-protein interaction (PPI) network by using the cytoHubba plug-in of Cytoscape, where five genes were further proven to be significantly related to the survival rate. Compared with normal tissues, the expressions of the five genes were both downregulated in the GSE110224 dataset. Subsequently, the expression of the five hub genes was confirmed by the Human Protein Atlas database. Finally, we used Cox regression analysis to identify genes associated with prognosis, and a 3-gene signature (CLCA1-CLCA4-GUCA2A) was constructed to predict the prognosis of patients with colorectal cancer. In conclusion, our study revealed that the five hub genes and CLCA1-CLCA4-GUCA2A signature are highly correlated with the development of colorectal adenocarcinoma and can serve as promising prognosis factors to predict the overall survival rate of patients.
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Project description:BackgroundLung cancer is an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. This study conducted a bioinformatics analysis to identify critical genes associated with poor prognosis in non-small cell lung cancer (NSCLC).MethodsWe downloaded three datasets (GSE33532, GSE27262, and GSE18842) from the gene expression omnibus (GEO), and used the GEO2R online tools to identify the differentially expressed genes (DEGs). We then used the Search Tool for Retrieval of Interacting Genes (STRING) database to establish a protein-protein interaction (PPI) network and used the Cytoscape software to perform a module analysis of the PPI network. A Kaplan-Meier plotter was used to perform the overall survival (OS) analysis, and the Gene Expression Profiling Interactive Analysis (GEPIA) database was used for expression level analysis of hub genes. Further, the UALCAN database was used to validate the relationship between the gene expression level of each hub gene and clinical characteristics.ResultsWe identified 254 DEGs, which were composed of 66 up-regulated genes and 188 down-regulated genes. Out of these, five DEGs were identified as hub genes (CDC20, BUB1, CCNB2, CCNB1, UBE2C) by constructing a PPI network. The use of a Kaplan-Meier plotter to generate patient survival curves suggested a strong relationship between the five hub genes with worse OS. Validation of the above results using the GEPIA database showed that all the hub genes were highly expressed in NSCLC tissues. Using the UALACN data mining platform, we found that the five hub genes are correlated with tumor stage and the status of node metastasis in NSCLC patients.ConclusionsWe identified five hub DEGs that might provide perspectives in the explorations of pathogenesis and treatments for NSCLC.
| S-EPMC8798611 | biostudies-literature
Project description:PurposeSmoking is recognized as a risk factor for Chronic Obstructive Pulmonary Disease (COPD), yet only 20-25% of smokers eventually develop COPD. Since its molecular pathogenesis remains unclear, there is an important need to further understand genetic differences between smokers with COPD and healthy smokers, screen out high-risk and susceptible groups among smokers, and find effective therapeutic targets.MethodsBioinformatics tools were used to screen biomarkers that were significantly associated with COPD smokers and healthy smokers. qRT-PCR and Western blotting analysis were used to detect hub gene expression in CSE-treated BEAS-2B cells and lung tissue of COPD mouse models.ResultsOur study identified 132 DEGs. The GO and KEGG analyses suggested that the ECM-receptor interaction, MAPK signaling pathway, Chemokine signaling pathway, PI3K-Akt signaling pathway, extracellular matrix organization and collagen fibril organization were associated with the occurrence and development of COPD. In addition, WGCNA analysis of GSE1650 showed that the brown module was most correlated with COPD. The intersection between the brown module and DEGs was used to identify 9 HUB genes (COL14A1, SULF1, MOXD1, CXCL12, CHRNA1, COMP, POU2AF1, MMP11, THBS2) that showed consistent expression and upregulation. Both the mRNA and protein expression levels of the Hub genes (except that of MMP11) were significantly upregulated in tobacco smoke exposed mouse emphysema models and CSE treated BEAS-2B cells.ConclusionOur results suggest that COL14A1, SULF1, MOXD1, CXCL12, CHRNA1, COMP, POU2AF1, and THBS2 may be potentially useful biomarkers for identifying smokers with a risk of developing COPD. The GO and KEGG functional enrichment analyses further confirmed the significant role played by ECM in the pathogenesis of COPD. The results of this study may provide further insights into the pathogenetic mechanisms involved in COPD.
| S-EPMC8901430 | biostudies-literature
Project description:The aim of the present study was to identify the hub genes and provide insight into the tumorigenesis and development of breast cancer. To examine the hub genes in breast cancer, integrated bioinformatics analysis was performed. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the 'limma' package in R. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis was used to determine the functional annotations and potential pathways of the DEGs. Subsequently, a protein-protein interaction network analysis and weighted correlation network analysis (WGCNA) were conducted to identify hub genes. To confirm the reliability of the identified hub genes, RNA gene expression profiles were obtained from The Cancer Genome Atlas (TCGA)-breast cancer database, and WGCNA was used to screen for genes that were markedly correlated with breast cancer. By combining the results from the GEO and TCGA datasets, 15 hub genes were identified to be associated with breast cancer pathophysiology. Overall survival analysis was performed to examine the association between the expression of hub genes and the overall survival time of patients with breast cancer. Higher expression of all hub genes was associated with significantly shorter overall survival in patients with breast cancer compared with patients with lower levels of expression of the respective gene.
| S-EPMC6607081 | biostudies-literature
Project description:IntroductionCell entry of SARS-CoV-2 causes genome-wide disruption of the transcriptional profiles of genes and biological pathways involved in the pathogenesis of COVID-19. Expression allelic imbalance is characterized by a deviation from the Mendelian expected 1:1 expression ratio and is an important source of allele-specific heterogeneity. Expression allelic imbalance can be measured by allele-specific expression analysis (ASE) across heterozygous informative expressed single nucleotide variants (eSNVs). ASE reflects many regulatory biological phenomena that can be assessed by combining genome and transcriptome information. ASE contributes to the interindividual variability associated with the disease. We aim to estimate the transcriptome-wide impact of SARS-CoV-2 infection by analyzing eSNVs.MethodsWe compared ASE profiles in the human lung cell lines Calu-3, A459, and H522 before and after infection with SARS-CoV-2 using RNA-Seq experiments.ResultsWe identified 34 differential ASE (DASE) sites in 13 genes (HLA-A, HLA-B, HLA-C, BRD2, EHD2, GFM2, GSPT1, HAVCR1, MAT2A, NQO2, SUPT6H, TNFRSF11A, UMPS), all of which are enriched in protein binding functions and play a role in COVID-19. Most DASE sites were assigned to the MHC class I locus and were predominantly upregulated upon infection. DASE sites in the MHC class I locus also occur in iPSC-derived airway epithelium basal cells infected with SARS-CoV-2. Using an RNA-Seq haplotype reconstruction approach, we found DASE sites and adjacent eSNVs in phase (i.e., predicted on the same DNA strand), demonstrating differential haplotype expression upon infection. We found a bias towards the expression of the HLA alleles with a higher binding affinity to SARS-CoV-2 epitopes.DiscussionIndependent of gene expression compensation, SARS-CoV-2 infection of human lung cell lines induces transcriptional allelic switching at the MHC loci. This suggests a response mechanism to SARS-CoV-2 infection that swaps HLA alleles with poor epitope binding affinity, an expectation supported by publicly available proteome data.
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Project description: Not available
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Project description:BackgroundWith the increasing incidence of papillary thyroid carcinoma (PTC), PTC continues to garner attention worldwide; however its pathogenesis remains to be elucidated. The purpose of this study was to explore key biomarkers and potential new therapeutic targets for, PTC.MethodsGEO2R and Venn online software were used for screening of differentially expressed genes. Hub genes were screened via STRING and Cytoscape, followed by Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed using the UALCAN online software and immunohistochemistry.ResultsWe identified 334 consistently differentially expressed genes (DEGs) comprising 136 upregulated and 198 downregulated genes. Gene Ontology enrichment analysis results suggested that the DEGs were mainly enriched in cancer-related pathways and functions. PPI network visualization was performed and 17 upregulated and 13 downregulated DEGs were selected. Finally, the expression verification and overall survival analysis conducted using the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI, and ENTPD1 were associated with the development of PTC and the prognosis of PTC patients, and the expression of LPAR5, TFPI and ENTPD1 was verified using a tissue chip.ConclusionsIn summary, the hub genes and pathways identified in the present study not only provide information for the development of new biomarkers for PTC but will also be useful for elucidation of the pathogenesis of PTC.
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Project description:Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein-protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p < 0.01), FOSB (p < 0.01), and NR4A2 (p < 0.05). Additionally, T follicular helper cells showed significant associations with ATF3 (p < 0.05), FOSB (p < 0.05), and JUN (p < 0.05). Mendelian randomization analysis provided strong evidence linking JUN (IVW: OR = 0.910, p = 0.005) and IL6 (IVW: OR = 1.024, p = 0.026) with KOA. Through the utilization of various bioinformatics analysis methods, we have pinpointed key hub genes relevant to knee osteoarthritis. These findings hold promise for advancing pre-symptomatic diagnostic strategies and enhancing our understanding of the biological underpinnings behind knee osteoarthritis susceptibility genes.
| S-EPMC11439059 | biostudies-literature
Project description:Anaplastic thyroid carcinoma (ATC) is a very rare malignancy; the pathogenesis of which is still not fully understood. The aim of the present study was to identify hub genes and pathways in ATC by microarray expression profiling. Two independent datasets (GSE27155 and GSE53072) were downloaded from GEO database. The differentially expressed genes (DEGs) between ATC tissues and normal thyroid tissues were screened out by the limma package and then enriched by gene ontology (GO) and KEGG pathway analysis. The hub genes were selected by protein-protein interaction (PPI) analysis. A total of 141 common upregulated and 87 common downregulated genes were screened out. These DEGs were significantly enriched in the phagosome and NF-kappa B signaling pathway. Through PPI analysis, TOP2A, TYMS, CCNB1, RACGAP1, FEN1, PRC1, and UBE2C were selected as hub genes, which were highly expressed in ATC tissues. TCGA data suggested that the expression levels of TOP2A, TYMS, FEN1, and PRC1 genes were also upregulated in other histological subtypes of thyroid carcinoma. High expression of TOP2A, TYMS, FEN1, PRC1, or UBE2C gene significantly decreased disease-free survival of patients with other thyroid carcinomas. In conclusion, the present study identified several hub genes and pathways, which will contribute to elucidating the pathogenesis of ATC and providing therapeutic targets for ATC.
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