Dataset Information

Analysis of temporal expression profiles after sciatic nerve injury by bioinformatic method.

ABSTRACT: After Peripheral nerve injuries (PNI), many complicated pathophysiologic processes will happen. A global view of functional changes following PNI is essential for the looking for the adequate therapeutic approaches. In this study, we performed an in-depth analysis on the temporal expression profiles after sciatic nerve injury by bioinformatic methods, including (1) cluster analysis of the samples; (2) identification of gene co-expression modules(CEMs) correlated with the time points; (3) analysis of differentially expressed genes at each time point (DEGs-ET); (4) analysis of differentially expressed genes varying over time (DEGs-OT); (5) creating Pairwise Correlation Plot for the samples; (6) Time Series Regression Analysis; (7) Determining the pathway, GO (gene ontology) and drug by enrichment analysis. We found that at a 3?h "window period" some specific gene expression may exist after PNI, and responses to lipopolysaccharide (LPS) and TNF signaling pathway may play important roles, suggesting that the inflammatory microenvironment exists after PNI. We also found that troglitazone was closely associated with the change of gene expression after PNI. Therefore, the further evaluation of the precise mechanism of troglitazone on PNI is needed and it may contribute to the development of new drugs for patients with PNI.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5575162 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Analysis of temporal expression profiles after sciatic nerve injury by bioinformatic method.

Zhang Yichong Y Zhan Yuanbo Y Han Na N Kou Yuhui Y Yin Xiaofeng X Zhang Peixun P

Scientific reports 20170829 1

After Peripheral nerve injuries (PNI), many complicated pathophysiologic processes will happen. A global view of functional changes following PNI is essential for the looking for the adequate therapeutic approaches. In this study, we performed an in-depth analysis on the temporal expression profiles after sciatic nerve injury by bioinformatic methods, including (1) cluster analysis of the samples; (2) identification of gene co-expression modules(CEMs) correlated with the time points; (3) analysi ...[more]

PMID: 28852045

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Project description:BackgroundSciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.MethodsSciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents.Results14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment.ConclusionsThe gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

Dataset Information

Analysis of temporal expression profiles after sciatic nerve injury by bioinformatic method.

Publications

Analysis of temporal expression profiles after sciatic nerve injury by bioinformatic method.

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