scholarly journals Identification of Ferroptotic Genes in Spinal Cord Injury at Different Time Points: Bioinformatics and Experimental Validation

2021 ◽  
Author(s):  
Yu Kang ◽  
Qiangwei Li ◽  
Rui Zhu ◽  
Shuang Li ◽  
Xin Xu ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Interaction Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Cord Injury ◽  
Key Genes

Abstract Programmed cell death (PCD) is an important pathologic process after spinal cord injury (SCI), and as a newly type of PCD, ferroptosis is also involved in the secondary SCI, however, the underlying molecular mechanisms remain unclear. Integrating animal experiment and bioinformatics, we validated the ferroptotic phenotype in SCI first, and then bioinformatic analyses, including Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, gene set enrichment analysis and protein-protein interaction analysis were performed to investigate the ferroptotic genes at 1 day, 3 days, 7 days, 14 days and 56 days post-SCI, finally, the ferroptotic genes in SCI were identified and expression of 5 key genes were validated by western blot. The ferroptotic symbols including iron overload, lipid peroxidation, shrunken mitochondria and ROS accumulation were detected in the acute and sub-acute phase of SCI. The outcomes of bioinformatics suggested that mTOR signaling pathway, HIF-1 signaling pathway, VEGF signaling pathway, Protein processing in endoplasmic reticulum were involved in ferroptotic regulation and ATF-3, XBP-1, HO-1, DDIT-3 and CHAC-1 were selected as the ferroptotic key genes in SCI. Besides, response to oxidative stress, amide metabolic process, cation transport and cytokine production were showed as the essential biological process in ferroptosis after SCI. The ferroptotic phenotype following SCI was validated and the ferroptotic genes and signaling pathways were identified. The results contribute to exploring the ferroptotic mechanism underlying secondary SCI and to providing potential target for clinical treatment.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

scholarly journals Identification  of acute spinal cord injury and autophagy-related potential key genes，pathways, and targeting drugs through bioinformatics analysis

2021 ◽  
Author(s):  
Wei-long Xu ◽  
Yan Zhao
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Text Mining ◽  
Enrichment Analysis ◽  
Acute Spinal Cord Injury ◽  
Molecular Pathways ◽  
Cord Injury ◽  
Significant Gene ◽  
Gene Modules ◽  
Key Genes

Abstract Background: Acute spinal cord injury (ASCI) is considered a form of severe central nervous system damage. At present, research in the fields of spinal surgery and neurology has highlighted the complex mechanisms underlying ASCI, among which autophagy is considered to play a crucial role.Objectives: We aimed to identify the genes and molecular pathways associated with ASCI and autophagy using computational tools and publicly available data, and to identify drugs targeting the relevant genes associated with ASCI and autophagy.Materials and Methods: We used text mining to detect the ASCI and autophagy-associated genes, and the intersection of the two gene sets was selected for gene ontology analysis using the DAVID program. We then constructed protein–protein interaction networks, followed by a functional enrichment analysis, from which we obtained two significant gene modules. Finally, the final list of genes was queried against the Drug Gene Interaction database to find drug candidates targeting the relevant ASCI and autophagy genes.Results: Our analysis identified 156 genes common to both the “ASCI” and “Autophagy” text mining concepts. Gene enrichment analysis yielded two significant gene modules (20 genes), which represent six significant signal pathways and could be targeted by 28 Food and Drug Administration (FDA)-approved drug molecules, and identified the drug–gene interactions. Conclusion: In conclusion, we presented a method to explore the potential key genes, molecular pathways and candidate drugs associated with ASCI and autophagy. As a result, in this method, we identified a total of 20 potential genes, six significant pathways and 28 candidate drugs, which could provides a basis for new trials and the development of novel targeted therapies as potential treatments for ASCI.

scholarly journals Bioinformatics analysis of key genes and pathways in Hashimoto thyroiditis tissues

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Long Zheng ◽  
Xiaojie Dou ◽  
Huijia Song ◽  
Pengwei Wang ◽  
Wei Qu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Interaction Analysis ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Hashimoto Thyroiditis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Abstract Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, only a few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, protein–protein interaction analysis, and gene set enrichment analysis were performed. In total, 85 DEGs, containing 76 up-regulated and 9 down-regulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3, and inflammatory pathways may promote the origin and progression of HT. The present study indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

scholarly journals Identification of biological pathways and genes associated with neurogenic heterotopic ossification by text mining

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8276 ◽  
Author(s):  
Yichong Zhang ◽  
Yuanbo Zhan ◽  
Yuhui Kou ◽  
Xiaofeng Yin ◽  
Yanhua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Heterotopic Ossification ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Egf Receptor ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Specific Gene ◽  
Cord Injury

Background Neurogenic heterotopic ossification is a disorder of aberrant bone formation affecting one in five patients sustaining a spinal cord injury or traumatic brain injury (SCI-TBI-HO). However, the underlying mechanisms of SCI-TBI-HO have proven difficult to elucidate. The aim of the present study is to identify the most promising candidate genes and biological pathways for SCI-TBI-HO. Methods In this study, we used text mining to generate potential explanations for SCI-TBI-HO. Moreover, we employed several additional datasets, including gene expression profile data, drug data and tissue-specific gene expression data, to explore promising genes that associated with SCI-TBI-HO. Results We identified four SCI-TBI-HO-associated genes, including GDF15, LDLR, CCL2, and CLU. Finally, using enrichment analysis, we identified several pathways, including integrin signaling, insulin pathway, internalization of ErbB1, urokinase-type plasminogen activator and uPAR-mediated signaling, PDGFR-beta signaling pathway, EGF receptor (ErbB1) signaling pathway, and class I PI3K signaling events, which may be associated with SCI-TBI-HO. Conclusions These results enhance our understanding of the molecular mechanisms of SCI-TBI-HO and offer new leads for researchers and innovative therapeutic strategies.

scholarly journals Retraction Note to: Analyzing time-series microarray data reveals key genes in spinal cord injury

2015 ◽  
Vol 42 (10) ◽  
pp. 1485-1485
Author(s):  
Xun Xia ◽  
Bo Qu ◽  
Yuan Ma ◽  
Li-bin Yang ◽  
Hai-dong Huang ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Time Series ◽  
Microarray Data ◽  
Cord Injury ◽  
Key Genes ◽  
Time Series Microarray
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