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

Chen, Qi; Zhao, Ziru; Yin, Guoyong; Yang, Cao; Wang, Danfeng; Zhi, Feng; Ta, Na

doi:10.21037/atm-21-340

Cited by 10 publications

(7 citation statements)

References 40 publications

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“…GO and KEGG pathway analysis showed that most of these genes are related to cell cycle and DNA replication, and participate in cell proliferation. The hub genes obtained by cytohub screening are Cdk1, Ccna2, Ccnb2, Ccnb1, Kif20a, Top2a, Aurka, Ns5atp9, Ube2c, Birc5, which are partially consistent with previous studies [52][53][54] . Cyclin-dependent kinase 1(Cdk1) is a member of serine/threonine kinase expressed in splinter cell, which is involved in the transition between G2 phase and mitosis.…”

Section: Discussionsupporting

confidence: 91%

Bioinformatics Analysis of Programmed Cell Death in Spinal Cord Injury

Deng

et al. 2023

World Neurosurgery

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Section: Discussionsupporting

confidence: 91%

Bioinformatics Analysis of Programmed Cell Death in Spinal Cord Injury

Deng

et al. 2023

World Neurosurgery

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“…In line with our study, Q. Chen et al. ( 2021 ) also used WGCNA to detect gene expression in SCI and found that six genes may serve as a potential prognostic target for the diagnosis and treatment of SCI.…”

Section: Discussionsupporting

confidence: 89%

“…Chen et al (2021) also used WGCNA to detect gene expression in SCI and found that six genes may serve as a potential prognostic target for the diagnosis and treatment of SCI.However, the limitation of our study was that the limited data published in the open dataset and the incomprehensive clinicopathological features analyzed may lead to potential statistical bias. Therefore, a more comprehensive study should be conducted in the future to identify more biomarkers for SCI treatment.In conclusion, we identified DEGs, enriched signaling pathways, and hub genes in the young and aged SCI groups.…”

mentioning

confidence: 96%

Identification of age‐specific biomarkers of spinal cord injury: A bioinformatics analysis of young and aged mice models

Miao,

Su,

Cheng

2023

Brain and Behavior

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BackgroundSpinal cord injury (SCI) is a debilitating event that often results in long‐term physical damage, disability, and a significant impact on a patient's quality of life. Past research has noted an age‐dependent decline in regeneration post‐SCI. This study seeks to identify potential biomarkers and enriched pathways in young and aged SCI mouse models.MethodsWe retrieved the microarray data of spinal cord samples from SCI mice and control mice from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R software and the Linear Models for Microarray Data (limma) package. The Gene Set Enrichment Analysis (GSEA) determined enrichment differences among gene sets. The Weighted Gene Co‐expression Network Analysis (WGCNA) pinpointed clinically significant modules and hub genes in SCI. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was employed to uncover significantly related pathways of crucial genes in SCI.ResultsWe found 2560 DEGs in the young SCI group, comprised of 1733 upregulated RNAs and 827 downregulated RNAs. In the aged SCI group, 3048 DEGs were revealed including 1856 upregulated and 1192 downregulated genes. The GSEA revealed 12 enriched signaling pathways in the young SCI group, such as IL6/JAK/STAT3 signaling, interferon alpha response, and interferon gamma response, and 21 signaling pathways in the aged SCI group such as IL6/JAK/STAT3 signaling, E2F targets, and angiogenesis‐related pathways. The WGCNA identified the turquoise module as significantly associated with the clinical traits of both young and aged SCI, and revealed 3181 hub genes. Ultimately, 1858 significant genes in SCI were found, with associated signaling pathways including epithelial‐mesenchymal transition (EMT), interferon gamma response, and KARS signaling.ConclusionOur study explored the RNA expression patterns and enriched signaling pathways in young and aged SCI mice. These findings may provide potential biomarkers for targeted SCI therapy.

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“…In the interictal PBMC samples, 31 miRNAs were DE compared to healthy controls, including hsa-miR-5189-3p, hsa-miR-96-5p, hsa-miR-3613-5p, hsa-miR-99a-3p, and hsa-miR-542-3p. hsa-miR-5189-3p closely correlates with spinal cord injury and is part of a set of transcripts proposed to have a role in classifying the disease [85]. The miR-96 and its miR-183 family have been observed to be regulated in diverse regions of the somatosensory nociceptive pathway in different chronic pain conditions in rodents [86].…”

Section: Discussionmentioning

confidence: 99%

Disease- and headache-specific microRNA signatures and their predicted mRNA targets in peripheral blood mononuclear cells in migraineurs: role of inflammatory signalling and oxidative stress

et al. 2022

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Background Migraine is a primary headache with genetic susceptibility, but the pathophysiological mechanisms are poorly understood, and it remains an unmet medical need. Earlier we demonstrated significant differences in the transcriptome of migraineurs' PBMCs (peripheral blood mononuclear cells), suggesting the role of neuroinflammation and mitochondrial dysfunctions. Post-transcriptional gene expression is regulated by miRNA (microRNA), a group of short non-coding RNAs that are emerging biomarkers, drug targets, or drugs. MiRNAs are emerging biomarkers and therapeutics; however, little is known about the miRNA transcriptome in migraine, and a systematic comparative analysis has not been performed so far in migraine patients. Methods We determined miRNA expression of migraineurs’ PBMC during (ictal) and between (interictal) headaches compared to age- and sex-matched healthy volunteers. Small RNA sequencing was performed from the PBMC, and mRNA targets of miRNAs were predicted using a network theoretical approach by miRNAtarget.com™. Predicted miRNA targets were investigated by Gene Ontology enrichment analysis and validated by comparing network metrics to differentially expressed mRNA data. Results In the interictal PBMC samples 31 miRNAs were differentially expressed (DE) in comparison to healthy controls, including hsa-miR-5189-3p, hsa-miR-96-5p, hsa-miR-3613-5p, hsa-miR-99a-3p, hsa-miR-542-3p. During headache attacks, the top DE miRNAs as compared to the self-control samples in the interictal phase were hsa-miR-3202, hsa-miR-7855-5p, hsa-miR-6770-3p, hsa-miR-1538, and hsa-miR-409-5p. MiRNA-mRNA target prediction and pathway analysis indicated several mRNAs related to immune and inflammatory responses (toll-like receptor and cytokine receptor signalling), neuroinflammation and oxidative stress, also confirmed by mRNA transcriptomics. Conclusions We provide here the first evidence for disease- and headache-specific miRNA signatures in the PBMC of migraineurs, which might help to identify novel targets for both prophylaxis and attack therapy.

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

Cited by 10 publications

References 40 publications

Bioinformatics Analysis of Programmed Cell Death in Spinal Cord Injury

Bioinformatics Analysis of Programmed Cell Death in Spinal Cord Injury

Identification of age‐specific biomarkers of spinal cord injury: A bioinformatics analysis of young and aged mice models

Disease- and headache-specific microRNA signatures and their predicted mRNA targets in peripheral blood mononuclear cells in migraineurs: role of inflammatory signalling and oxidative stress

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