Dataset Information

Identification of hub genes related to the recovery phase of irradiation injury by microarray and integrated gene network analysis.

ABSTRACT: BACKGROUND: Irradiation commonly causes long-term bone marrow injury charactertized by defective HSC self-renewal and a decrease in HSC reserve. However, the effect of high-dose IR on global gene expression during bone marrow recovery remains unknown. METHODOLOGY: Microarray analysis was used to identify differentially expressed genes that are likely to be critical for bone marrow recovery. Multiple bioinformatics analyses were conducted to identify key hub genes, pathways and biological processes. PRINCIPAL FINDINGS: 1) We identified 1302 differentially expressed genes in murine bone marrow at 3, 7, 11 and 21 days after irradiation. Eleven of these genes are known to be HSC self-renewal associated genes, including Adipoq, Ccl3, Ccnd1, Ccnd2, Cdkn1a, Cxcl12, Junb, Pten, Tal1, Thy1 and Tnf; 2) These 1302 differentially expressed genes function in multiple biological processes of immunity, including hematopoiesis and response to stimuli, and cellular processes including cell proliferation, differentiation, adhesion and signaling; 3) Dynamic Gene Network analysis identified a subgroup of 25 core genes that participate in immune response, regulation of transcription and nucleosome assembly; 4) A comparison of our data with known irradiation-related genes extracted from literature showed 42 genes that matched the results of our microarray analysis, thus demonstrated consistency between studies; 5) Protein-protein interaction network and pathway analyses indicated several essential protein-protein interactions and signaling pathways, including focal adhesion and several immune-related signaling pathways. CONCLUSIONS: Comparisons to other gene array datasets indicate that global gene expression profiles of irradiation damaged bone marrow show significant differences between injury and recovery phases. Our data suggest that immune response (including hematopoiesis) can be considered as a critical biological process in bone marrow recovery. Several critical hub genes that are key members of significant pathways or gene networks were identified by our comprehensive analysis.

SUBMITTER: Zhang J

PROVIDER: S-EPMC3172286 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundPulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.MethodsSingle-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.ResultsNineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.ConclusionWDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

Project description:BackgroundDiabetic kidney disease (DKD) is a leading cause of end-stage renal disease; however, the underlying molecular mechanisms remain unclear. Recently, bioinformatics analysis has provided a comprehensive insight toward the molecular mechanisms of DKD. Here, we re-analyzed three mRNA microarray datasets including a single-cell RNA sequencing (scRNA-seq) dataset, with the aim of identifying crucial genes correlated with DKD and contribute to a better understanding of DKD pathogenesis.MethodsThree datasets including GSE131882, GSE30122, and GSE30529 were utilized to find differentially expressed genes (DEGs). The potential functions of DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed, and hub genes were selected with the top three molecular complex detection (MCODE) score. A correlation analysis between hub genes and clinical indicators was also performed.ResultsIn total, 84 upregulated DEGs and 49 downregulated DEGs were identified. Enriched pathways of the upregulated DEGs included extracellular matrix (ECM) receptor interaction, focal adhesion, human papillomavirus infection, malaria, and cell adhesion molecules. The downregulated DEGs were mainly enriched in ascorbate and aldarate metabolism, arginine and proline metabolism, endocrine- and other factor-regulated calcium reabsorption, mineral absorption and longevity regulating pathway, and multiple species signaling pathway. Seventeen hub genes were identified, and correlation analysis between unexplored hub genes and clinical features of DKD suggested that EGF, KNG1, GADD45B, and CDH2 might have reno-protective roles in DKD. Meanwhile, ATF3, B2M, VCAM1, CLDN4, SPP1, SOX9, JAG1, C3, and CD24 might promote the progression of DKD. Finally, most hub genes were found present in the immune cells of diabetic kidneys, which suggest the important role of inflammation infiltration in DKD pathogenesis.ConclusionsIn this study, we found seventeen hub genes using a scRNA-seq contained multiple-microarray analysis, which enriched the present understanding of molecular mechanisms underlying the pathogenesis of DKD in cells' level and provided candidate targets for diagnosis and treatment of DKD.

Project description:Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM-receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

Dataset Information

Identification of hub genes related to the recovery phase of irradiation injury by microarray and integrated gene network analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets