Dataset Information

Identification of pivotal genes and pathways for spinal cord injury via bioinformatics analysis.

ABSTRACT: The present study aimed to identify key genes and pathways associated with spinal cord injury (SCI) and subsequently investigate possible therapeutic targets for the condition. The array data of GSE20907 was downloaded from the Gene Expression Omnibus database and 24 gene chips, including 3?day, 4?day, 1?week, 2?week and 1?month post?SCI together with control propriospinal neurons, were used for the analysis. The raw data was normalized and then the differentially expressed genes (DEGs) in the (A) 2?week post?SCI group vs. control group, (B) 1?month post?SCI group vs. control group, (C) 1?month and 2?week post?SCI group vs. control group, and (D) all post?SCI groups vs. all control groups, were analyzed with a limma package. Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for DEGs were performed. Cluster analysis was performed using ClusterOne plugins. All the DEGs identified were associated with immune and inflammatory responses. Signal transducer and activator of transcription 3 (STAT3), erb?B2 receptor tyrosine kinase 4 (ERBB4) and cytochrome B?245, ? polypeptide (CYBA) were in the network diagrams of (A), (C) and (D), respectively. The enrichment analysis of DEGs identified in all samples demonstrated that the DEGs were also enriched in the chemokine signaling pathway (enriched in STAT3) and the high?affinity immunoglobulin E receptor (Fc?RI) signaling pathway [enriched in proto?oncogene, src family tyrosine kinase (LYN)]. Immune and inflammatory responses serve significant roles in SCI. STAT3, ERBB4 and CYBA may be key genes associated with SCI at certain stages. Furthermore, STAT3 and LYN may be involved in the development of SCI via the chemokine and Fc?RI signaling pathways, respectively.

SUBMITTER: Zhu Z

PROVIDER: S-EPMC5646972 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of pivotal genes and pathways for spinal cord injury via bioinformatics analysis.

Zhu Zonghao Z Shen Qiang Q Zhu Liang L Wei Xiaokang X

Molecular medicine reports 20170721 4

The present study aimed to identify key genes and pathways associated with spinal cord injury (SCI) and subsequently investigate possible therapeutic targets for the condition. The array data of GSE20907 was downloaded from the Gene Expression Omnibus database and 24 gene chips, including 3‑day, 4‑day, 1‑week, 2‑week and 1‑month post‑SCI together with control propriospinal neurons, were used for the analysis. The raw data was normalized and then the differentially expressed genes (DEGs) in the ( ...[more]

PMID: 28731189

Similar Datasets

Project description:Background: Spinal cord injury (SCI) is a severe neurological deficit affecting both young and older people worldwide. The potential role of key enhancer RNAs (eRNAs) in SCI remains elusive, which is a prominent challenge in the trauma repair process. This study aims to investigate the roles of key eRNAs, transcription factors (TFs), signaling pathways, and small-molecule inhibitors in SCI using multi-omics bioinformatics analysis. Methods: Microarray data of peripheral blood mononuclear cell (PBMC) samples from 27 healthy volunteers and 25 chronic-phase SCI patients were retrieved from the Gene Expression Omnibus database. Differentially expressed transcription factors (DETFs), differentially expressed enhancer RNAs (DEeRNAs), and differentially expressed target genes (DETGs) were identified using the Linear Models for Microarray Data (limma) package. Fraction of immune cells was estimated using CIBERSORT algorithm. Gene Set Variation Analysis (GSVA) was applied to identify the downstream signaling pathways. The eRNA regulatory network was constructed based on the correlation results. Connectivity Map (CMap) database was used to find potential drugs for SCI patients. The cellular communication analysis was performed to explore the molecular regulation mechanism of SCI based on single-cell RNA sequencing (scRNA-seq) data. Chromatin immunoprecipitation sequencing (ChIP-seq) and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data were used to validate the key regulatory mechanisms. scRNA-seq dataset was used to validate the cell subtype localization of the key eRNAs. Results: In total, 21 DETFs, 24 DEeRNAs, and 829 DETGs were identified. A regulatory network of 13 DETFs, six DEeRNAs, seven DETGs, two hallmark pathways, two immune cells, and six immune pathways was constructed. The link of Splicing factor proline and glutamine rich (SFPQ) (TF) and vesicular overexpressed in cancer prosurvival protein 1 (VOPP1) (eRNA) (R = 0.990, p < 0.001, positive), VOPP1 (eRNA) and epidermal growth factor receptor (EGFR) (target gene) (R = 0.974, p < 0.001, positive), VOPP1, and T helper (Th) cells (R = -0.987, p < 0.001, negative), and VOPP1 and hallmark coagulation (R = 0.937, p < 0.001, positive) was selected. Trichostatin A was considered the best compound target to SCI-related eRNAs (specificity = 0.471, p < 0.001). Conclusion: VOPP1, upregulated by SFPQ, strengthened the transient expression of EGFR. Th cells and coagulation were the potential downstream pathways of VOPP1. This regulatory network and potential inhibitors provide novel diagnostic biomarkers and therapeutic targets for SCI.

Project description:Complex pathological changes occur during the development of spinal cord injury (SCI), and determining the underlying molecular events that occur during SCI is necessary for the development of promising molecular targets and therapeutic strategies. This study was designed to explore differentially expressed genes (DEGs) associated with the acute and chronic stages of SCI using bioinformatics analysis. Gene expression profiles (GSE45006, GSE93249, and GSE45550) were downloaded from the Gene Expression Omnibus database. SCI-associated DEGs from rat samples were identified, and Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed. In addition, a protein-protein interaction network was constructed. Approximately 66 DEGs were identified in GSE45550 between 3-14 days after SCI, whereas 2418 DEGs were identified in GSE45006 1-56 days after SCI. Moreover, 1263, 195, and 75 overlapping DEGs were identified between these two expression profiles, 3, 7/8, and 14 days after SCI, respectively. Additionally, 16 overlapping DEGs were obtained in GSE45006 1-14 days after SCI, including Pank1, Hn1, Tmem150c, Rgd1309676, Lpl, Mdh1, Nnt, Loc100912219, Large1, Baiap2, Slc24a2, Fundc2, Mrps14, Slc16a7, Obfc1, and Alpk3. Importantly, 3882 overlapping DEGs were identified in GSE93249 1-6 months after SCI, including 3316 protein-coding genes and 567 long non-coding RNA genes. A comparative analysis between GSE93249 and GSE45006 resulted in the enrichment of 1135 overlapping DEGs. The significant functions of these 1135 genes were correlated with the response to the immune effector process, the innate immune response, and cytokine production. Moreover, the biological processes and KEGG pathways of the overlapping DEGs were significantly enriched in immune system-related pathways, osteoclast differentiation, the nuclear factor-κB signaling pathway, and the chemokine signaling pathway. Finally, an analysis of the overlapping DEGs associated with both acute and chronic SCI, assessed using the expression profiles GSE93249 and GSE45006, identified four overlapping DEGs: Slc16a7, Alpk3, Lpl and Nnt. These findings may be useful for revealing the biological processes associated with SCI and the development of targeted intervention strategies.

Project description:Metastasis is the leading cause of melanoma-related mortality. Current therapies are rarely curative for metastatic melanoma, revealing the urgent need to identify more effective preventive and therapeutic targets. This study aimed to screen the core genes and molecular mechanisms related to melanoma metastasis. A gene expression profile, GSE8401, including 31 primary melanoma and 52 metastatic melanoma clinical samples, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between melanoma metastases and primary melanoma were screened using GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of DEGs were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with Molecular Complex Detection (MCODE) plug-in tools were utilized to detect the protein-protein interaction (PPI) network among DEGs. The top 10 genes with the highest degrees of the PPI network were defined as hub genes. In the results, 425 DEGs, including 60 upregulated genes and 365 downregulated genes, were identified. The upregulated genes were enriched in ECM-receptor interactions and the regulation of actin cytoskeleton, while 365 downregulated genes were enriched in amoebiasis, melanogenesis, and ECM-receptor interactions. The defined hub genes included CDK1, COL17A1, EGFR, DSG1, KRT14, FLG, CDH1, DSP, IVL, and KRT5. In addition, the mRNA and protein levels of the hub genes during melanoma metastasis were verified in the TCGA database and paired post- and premetastatic melanoma cells, respectively. Finally, KRT5-specific siRNAs were utilized to reduce the KRT5 expression in melanoma A375 cells. An MTT assay and a colony formation assay showed that KRT5 knockdown significantly promoted the proliferation of A375 cells. A Transwell assay further suggested that KRT5 knockdown significantly increased the cell migration and cell invasion of A375 cells. This bioinformatics study provided a deeper understanding of the molecular mechanisms of melanoma metastasis. The in vitro experiments showed that KRT5 played the inhibitory effects on melanoma metastasis. Therefore, KRT5 may serve important roles in melanoma metastasis.

Dataset Information

Identification of pivotal genes and pathways for spinal cord injury via bioinformatics analysis.

Publications

Identification of pivotal genes and pathways for spinal cord injury via bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets