Identification of gene coexpression modules, hub genes, and pathways related to spinal cord injury using integrated bioinformatics methods

Wang, Tienan; Wu, Baolin; Zhang, Xiuzhi; Zhang, Meng; Zhang, Shuo; Huang, Wei; Liu, Tao; Yu, Weiting; Li, Junlei; Yu, Xiaobing

doi:10.1002/jcb.27908

Cited by 8 publications

(7 citation statements)

References 46 publications

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“…It has been used to study diseases which affect the neurological system, such as neuropathic pain (15), Parkinson's disease (16), and Alzheimer's disease (17). This method has also been successfully utilized in previous studies to identify pathway-related modules in SCI patients (18). Hence, the co-expression network for models and hubs of SCI genes is accessible; however, the number of studies addressing this issue remains insufficient.…”

Section: Introductionmentioning

confidence: 99%

Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis

Chen¹,

Zhao²,

Yin³

et al. 2021

Ann Transl Med

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Background: Spinal cord injury (SCI) has an immediate and devastating impact on the control over various movements and sensations. However, no effective therapies for SCI currently exist.Methods: To identify and analyze SCI subtypes, we obtained the expression profile data of the 1,057 genes (889 intersection genes) in GSE45550 using weighted gene co-expression network analysis (WGCNA), and 14 co-expression gene modules were identified. Next, we filtered out the network degree top 10 (degree >80) genes, considered the final key SCI genes. A multifactor regulatory network (105 interaction pairs), consisting of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and transcription factors (TFs) was constructed. This network was involved in the co-expression of key genes. We selected the top 10 regulatory factors (degree >4) as core regulators in the multifactor regulatory network. Results:The results of functional enrichment analysis of the target gene expressing the core regulatory factor [1,059] showed that these target genes were enriched in pathways for human cytomegalovirus infection, chronic myeloid leukemia, and pancreatic cancer. Further, we used the key genes in the coexpression network to categorize the SCI samples in GSE45550. The expression levels of the top 6 genes (CCNB2, CCNB1, CKS2, COL5A1, KIF20A, and RACGAP1) may act as potential marker genes for different SCI subtypes. On the basis of these different subtypes, 8 SCI core gene CDK1-associated drugs were also found to provide potential therapeutic options for SCI.Conclusions: These results may provide a novel therapeutic strategy for the treatment of SCI.

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Section: Introductionmentioning

confidence: 99%

Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis

Chen¹,

Zhao²,

Yin³

et al. 2021

Ann Transl Med

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“…A small number of studies on SCI applied WGCNA. For example, Wang et al used WGCNA in 2 open access array data and identified 3 hub genes including Rac2, Itgb2 and Tyrobp , which may play crucial roles in SCI via natural killer cell-mediated cytotoxicity [ 37 ]. Another study using WGCNA indicated that the effects of NT3-chitosan on neurite regeneration were mediated by the enhancement in neurogenesis and angiogenesis and reduced inflammatory responses [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Integrated Bioinformatics Analysis of Hub Genes and Pathways Associated with a Compression Model of Spinal Cord Injury in Rats

Chang

Zheng

et al. 2020

Med Sci Monit

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Background Spinal cord injury (SCI) is a serious nervous system condition that can cause lifelong disability. The aim of this study was to identify potential molecular mechanisms and therapeutic targets for SCI. Material/Methods We constructed a weighted gene coexpression network and predicted which hub genes are involved in SCI. A compression model of SCI was established in 45 Sprague-Dawley rats, which were divided into 5 groups (n=9 per group): a sham operation group, and 1, 3, 5, and 7 days post-SCI groups. The spinal cord tissue on the injured site was harvested on 1, 3, 5, and 7 days after SCI and 3 days after surgery in the sham operation group. High-throughput sequencing was applied to investigate the expression profile of the mRNA in all samples. Differentially expressed genes were screened and included in weighted gene coexpression network analysis (WGCNA). Co-expressed modules and hub genes were identified by WGCNA. The biological functions of each module were investigated using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Results According to the RNA-seq data, a total of 1965 differentially expressed genes were screened, and WGCNA identified 10 coexpression modules and 5 hub genes. Module function analysis revealed that SCI was associated with immune response, cell division, neuron projection development, and collagen fibril organization. Conclusions Our study revealed dynamic changes in a variety of biological processes following SCI and identified 5 hub genes via WGCNA. These results provide insights into the molecular mechanisms and therapeutic targets of SCI.

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“…A cut height of 0.85 and effect size of ≥10 were used to identify the modules. Pearson's correlation matrices were calculated for the modules (20).…”

Section: Methodsmentioning

confidence: 99%

Identification of RAD54 homolog B as a promising therapeutic target for breast cancer

Feng

Xia

2019

Oncol Lett

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Breast cancer is a recognized threat to the health of women globally. Due to the lack of the knowledge about the molecular pathogenesis of breast cancer, therapeutic strategies remain inadequate, especially for aggressive breast cancer. In the present study, sequential bioinformatics analysis was performed using data from the GSE20711 dataset, and the results demonstrated that three genes may impact the survival of patients with breast cancer. One of these genes, RAD54 homolog B (RAD54B), may be a potential prognostic factor for breast cancer. A signature was established that could evaluate the overall survival for patients with breast cancer based on the risk score calculated from RAD54B expression and the Tumor-Node-Metastasis (TNM) stage [risk score=expRAD54B x 0.236 + TNM stage (I/II=0 or III/IV=1) x1.025]. In addition, based on the GSE85871 dataset and inhibitory assay, the study identified a natural compound, Japonicone A, which may reduce the proliferation of breast cancer cells by inhibiting the expression of RAD54B. Overall, the present study identified a novel candidate gene and a candidate compound as promising therapeutic targets for the treatment of breast cancer.

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Identification of gene coexpression modules, hub genes, and pathways related to spinal cord injury using integrated bioinformatics methods

Cited by 8 publications

References 46 publications

Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis

Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis

Integrated Bioinformatics Analysis of Hub Genes and Pathways Associated with a Compression Model of Spinal Cord Injury in Rats

Identification of RAD54 homolog B as a promising therapeutic target for breast cancer

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