ArticleSpinal cord2017
Identification of temporal genes involved in the mechanisms of spinal cord injury.
Article in Spinal cord, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Development and validation of a machine learning-based nomogram for predicting HLA-B27 expression.BMC immunology · 2023Article
- Synaptic Cell Adhesion Molecule 3 (SynCAM3) Deletion Promotes Recovery from Spinal Cord Injury by Limiting Glial Scar Formation.International journal of molecular sciences · 2022Article
- Inhibition of IL1R1 or CASP4 attenuates spinal cord injury through ameliorating NLRP3 inflammasome-induced pyroptosis.Frontiers in immunology · 2022Article
- Identification of four differentially expressed genes associated with acute and chronic spinal cord injury based on bioinformatics data.Neural regeneration research · 2021Article
- Article
- Chronic spinal cord changes in a high-fat diet-fed male rat model of thoracic spinal contusion.Physiological genomics · 2017Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAs a high-cost neurological disability, spinal cord injury (SCI) can result in permanent paralysis and loss of sensation. To identify the temporal genes involved in the pathogenesis of SCI, we analysed the expression profile of GSE45006.
methodsGSE45006 was downloaded from Gene Expression Omnibus, including 20 SCI samples (samples at 1 day, 3 days, 1 week, 2 weeks and 8 weeks after injury, four repetitions for each time point) and 4 normal samples. The Bayesian Estimation of Temporal Regulation (BETR) and randomForest packages were used to screen the temporal genes and the top 100 temporal genes, respectively. Then, the gplots package and Pearson correlation analysis separately were used to perform hot map analysis and expression pattern clustering for the top 100 temporal genes. Using the clusterProfiler package and TargetMine tool, their potential functions were analysed by enrichment analyses. Moreover, interaction relationships between these temporal genes and pathways were investigated by pathway-gene crosslinking networks.
resultsIn total, 1907 temporal genes were identified. The top 100 temporal genes were obtained and divided into six clusters. Most of the gene functions were enriched in biological process categories. ARG1 and NOS3 in cluster 4 were enriched in biological process of arginine catabolic process. TGFβ2, TGFβ3, ALDH2 and ALDH3A2 were correlated with numerous pathways in the pathway-gene crosslinking network. Pathways related to TGFβ2 and TGFβ3 were connected to pathways related to ARG1 and NOS3 via ARG1.
conclusionSeveral temporal genes, including TGFβ2, TGFβ3, ALDH2, ALDH3A2, ARG1 and NOS3, might be involved in SCI.
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