Deciphering Expression Profiling and Functional Signatures of Individualized Obesity-Associated Genes in Ossification of Ligamentum Flavum Through RNA-Sequence Data Mining

Abstract Background: Obese individuals predispose to ossification of ligamentum flavum (OLF), whereas the underlying connections between obesity phenotype and OLF pathomechanism are not fully understood, especially during early life. This study aimed to explore obesity-associated genes and their functional signatures in OLF. Methods: Gene microarray expression data related to OLF were downloaded from the GSE106253 dataset in the Gene Expression Omnibus (GEO) database. The potential obesity-related differentially expressed genes (ORDEGs) in OLF were screened. Then, gene-ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied for these genes. Furthermore, protein-protein interactions (PPI) were used to identify hub ORDEGs, and Metascape was used to further verify the key signaling pathways and immune-related function signatures of hub ORDEGs. Finally, correlation analysis of hub ORDEGs and identified OLF-related infiltrating immune cells (OIICs) was constructed to understand the possible mechanical link among obesity, immune response and OLF. Results: OLF-related differentially expressed genes and 2051 obesity-related genes from four databases were intersected to obtain 99 ORDEGs, including 54 upregulated and 55 downregulated genes. GO and KEGG analysis revealed that these genes were mainly involved in metabolism, inflammation and immune-related biological functions and pathways. A PPI network was established to determine 14 hub genes (AKT1, CCL2, CCL5, CXCL2, ICAM1, IL10, MYC, PTGS2, SAA1, SOCS1, SOCS3, STAT3, TNFRSF1B and VEGFA). The co-expression network demonstrated that this module was associated with cellular response to biotic stimulus, regulation of inflammatory response, regulation of tyrosine phosphorylation of STAT protein. Furthermore, Metascape functional annotations showed that hub genes were mainly involved in receptor signaling pathway via JAK-STAT, response to TNF and regulation of defense response, and their representative enriched pathways were TNF, adipocytokine and JAK-STAT signaling pathways. Subgroup analysis indicated that T cell activation might be potential immune function processes involved, and correlation analysis revealed that cDCs, memory B-cells and preadipocytes were highly correlated infiltrating immune cells. Conclusions: Our study deciphered individualized obesity-associated gene signature for the first time, which may facilitate exploring the underlying cellular and molecular pathogenesis and novel therapeutic targets of obesity-related early-onset OLF.

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AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1773 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1037.2-1038

Author(s):

X. Sun ◽

S. X. Zhang ◽

S. Song ◽

T. Kong ◽

C. Zheng ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Shanxi Province ◽

Receptor Interaction ◽

Term Therapy ◽

Pathway Enrichment Analysis ◽

Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

10.21203/rs.3.rs-1023446/v1 ◽

2021 ◽

Author(s):

Li Guoquan ◽

Du Junwei ◽

He Qi ◽

Fu Xinghao ◽

Ji Feihong ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Inflammatory Responses ◽

Expression Profiles ◽

Treatment Options ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Chronic Lymphocytic Thyroiditis ◽

Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

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Identification of Potential Autophagy-Related Genes in Steroid-Induced Osteonecrosis of the Femoral Head

10.21203/rs.3.rs-1066425/v1 ◽

2021 ◽

Author(s):

XueZhen LIANG ◽

Di LUO ◽

Yan-Rong CHEN ◽

Jia-Cheng LI ◽

Bo-Zhao YAN ◽

...

Keyword(s):

Femoral Head ◽

Correlation Analysis ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment

Abstract Purpose: Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people. Previous experimental studies had shown that autophagy might be involved in the pathological process of SONFH, but the pathogenesis of autophagy in SONFH remained unclear. We aim to identify and validate the key potential autophagy-related genes of SONFH to further illustrate the mechanism of autophagy in SONFH through bioinformatics analysis. Methods: The mRNA expression profile dataset GSE123568 was download from Gene Expression Omnibus (GEO) database, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples. The autophagy-related genes were obtained from the Human Autophagy Database (HADb). The autophagy-related genes of SONFH were screened by intersecting GSE123568 dataset with autophagy genes. The differentially expressed autophagy-related genes of SONFH were identified by R software. Besides, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted for the differentially expressed autophagy-related genes of SONFH by R software. Then, the correlation analysis between the expression levels of differentially expressed autophagy-related genes of SONFH was confirmed by R software. Moreover, the protein–protein interaction (PPI) network were analyzed by the Search Tool for the Retrieval of Interacting Genes (STRING), and the significant gene cluster modules were identified by the MCODE Cytoscape plugin, and hub genes of differentially expressed autophagy-related genes of SONFH were screened by the CytoHubba Cytoscape plugin. Finally, the expression levels of hub genes of differentially expressed autophagy-related genes of SONFH was validated in hip articular cartilage specimens from necrosis femur head (NFH) by GSE74089 dataset. Results: A total of 34 differentially expressed autophagy-related genes were identified between the peripheral blood of SONFH samples and non-SONFH Samples based on the defined criteria, including 25 up-regulated genes and 9 down-regulated genes. The GO and KEGG pathway enrichment analysis revealed that these 34 differentially expressed autophagy-related genes of SONFH were concentrated in death domain receptors, FOXO signaling pathway and apoptosis. The correlation analysis revealed a significant correlation among the 34 differentially expressed autophagy-related genes of SONFH. The PPI results demonstrated that the 34 differentially expressed autophagy-related genes interacted with each other. There were 10 hub genes identified by the MCC algorithms of Cytohubba. The results of GSE74089 dataset showed TNFSF10, PTEN and CFLAR were significantly upregulated while BCL2L1 were significantly downregulated in the hip cartilage specimens, which were consistent with the GSE123568 dataset. Conclusions: There were 34 potential autophagy-related genes of SONFH identified using bioinformatics analysis. TNFSF10, PTEN, CFLAR and BCL2L1 might serve as potential drug targets and biomarkers by regulating autophagy. These results would expand new insights into the autophagy-related understanding of SONFH and might be useful in the diagnosis and prognosis of SONFH.

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Integrative bioinformatics analysis for identification of hub genes and pathways responsible for early pathogenesis of retinoblastoma

10.21203/rs.3.rs-30876/v1 ◽

2020 ◽

Author(s):

Aditi Karmakar ◽

Md. Maqsood Ahamad Khan ◽

Nidhi Kumari ◽

Senthil Kumar

Keyword(s):

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Integrated Analysis ◽

P Value ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Diagnostic Biomarkers ◽

Appropriate Therapy ◽

Microarray Datasets

Abstract Background Retinoblastoma (Rb) is the most common childhood malignancy in which intra-ocular tumors developed at a very young age. It is very crucial to detect Rb at an earlier stage and start appropriate therapy to prevent further metastasis. With the recent advancement of multi-omics analysis of microarray data and sophisticated bioinformatics tools, we could possibly identify potential early diagnostic biomarkers and novel therapeutic targets for retinoblastoma. Methods Microarray datasets (DNA methylation-GSE57362, miRNA-GSE7072, & mRNA-GSE110811) were utilized from NCBI-GEO. The GEO2R were employed to discover Differentially Expressed Genes (DEGs) in Retinoblastoma. Further, an integrated analysis of these genes was performed and a co-expression network was formed to identify hub genes. GEPIA server was used to validate these genes which are responsible for the progression of retinoblastoma. Results Differentially expressed genes were identified on the basis of P-value ≤ 0.05 and log2fc ≥ 2. In GSE57362 a total no of 267 genes methylated status, in GSE7072 a total no. of 265 gene targets of miRNAs and in GSE110811 a total no. of 770 genes were shown to be differentially expressed. Further, 10 hub genes, 5 bottleneck genes, and 3 common genes were identified by constructing the co-expression network. Survival analysis was done to validate the identified candidate genes using the GEPIA web server. Pathway enrichment analysis and gene ontology demonstrated that these genes were mostly enriched in biological processes such as regulation of cell proliferation and involved in multiple pathways like p53 pathway, cell cycle, and apoptosis. Conclusion This study suggested that these hub genes possibly will play vital roles in the onset and progression of retinoblastoma and could be serve as potential biomarkers to facilitate the diagnosis and treatment of this disease in the future.

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Screening and identification of MSX1 for predicting progestin resistance in endometrial cancer

10.21203/rs.3.rs-17361/v1 ◽

2020 ◽

Author(s):

Linlin Yang ◽

Yunxia Cui ◽

Ting Huang ◽

Xiao Sun ◽

Yudong Wang

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Endometrial Cancer ◽

Endometrial Carcinoma ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Hub Genes

Abstract Background: Progestin resistance is a critical obstacle for endometrial conservative therapy. Therefore, the studies to acquire a more comprehensive understanding of the mechanisms and specific biomarkers to predict progestin resistance are very important. However, the pivotal roles of essential molecules of progestin resistance are still unexplored. Methods: We downloaded GSE121367 with gene expression profiles of medroxyprogesterone acetate (MPA) resistant and sensitive cell lines from the GEO database. The “limma” R language package was applied to identify differentially expressed genes (DEGs). Gene ontology and pathway enrichment analysis was performed through the database of DAVID. Meanwhile, we conducted GSEA analysis to identify pathway enrichments. Protein–protein interaction construction of top genes was conducted to screen hub genes by STRING and visualized in Cytoscape. A high connectivity degree of hub genes were picked out to perform the differential expression, methylation validation and overall survival analysis in the Gene Expression Profiling Interactive Analysis database, Human Protein Atlas database and Kaplan–Meier plotter online tool, respectively. In addition, microRNAs and upstream transcription factors of hub genes were predicted by miRTarBase and Network Analyst database. Results: A total number of 3282 differentially expressed genes were identified. Functional enrichment analysis demonstrated that these genes were mostly enriched in negative regulation of DNA binding, chronic inflammatory response and cell adhesion molecules pathway. We screened out ten hub genes including CDH1, JAG1, PTGES, EPCAM, CNTNAP2, TBX1, MSX1, KRT19, OAS1 and DAB2 among different groups. The genomic alteration rates of hub genes were low based on the current uterine corpus endometrial carcinoma sample sets. Their relevant microRNA and transcription factor were detected and has-miR-335-5p, has-miR-124-3p, MAZ and TFDP1 were the most prominent. The methylation status of CDH1, JAG1, EPCAM and MSX1 were decreased, corresponding to their high protein expression in endometrial cancers, which also indicated better overall survival. The homeobox protein of MSX1 showed significantly tissue specificity. Conclusions: Our study identified ten hub genes associated with progestin resistance of endometrial cancer and screened out the gene of MSX1 which promised to be the specific indicator. This would shed new light on the underlying biological marker to overcome the progestin resistance of endometrial cancer. Keywords : Bioinformatic analysis, Progestin resistance, Endometrial carcinoma, MSX1

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Lipidomics profiling of goose granulosa cell model of stearoyl-CoA desaturase function identifies a pattern of lipid droplets associated with follicle development

Cell & Bioscience ◽

10.1186/s13578-021-00604-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Yuan ◽

Shenqiang Hu ◽

Liang Li ◽

Chunchun Han ◽

Hehe Liu ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Lipid Droplets ◽

Cell Model ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Follicle Development ◽

Microscopy Analysis ◽

Stearoyl Coa Desaturase ◽

Underlying Mechanisms

Abstract Background Despite their important functions and nearly ubiquitous presence in cells, an understanding of the biology of intracellular lipid droplets (LDs) in goose follicle development remains limited. An integrated study of lipidomic and transcriptomic analyses was performed in a cellular model of stearoyl-CoA desaturase (SCD) function, to determine the effects of intracellular LDs on follicle development in geese. Results Numerous internalized LDs, which were generally spherical in shape, were dispersed throughout the cytoplasm of granulosa cells (GCs), as determined using confocal microscopy analysis, with altered SCD expression affecting LD content. GC lipidomic profiling showed that the majority of the differentially abundant lipid classes were glycerophospholipids, including PA, PC, PE, PG, PI, and PS, and glycerolipids, including DG and TG, which enriched glycerophospholipid, sphingolipid, and glycerolipid metabolisms. Furthermore, transcriptomics identified differentially expressed genes (DEGs), some of which were assigned to lipid-related Gene Ontology slim terms. More DEGs were assigned in the SCD-knockdown group than in the SCD-overexpression group. Integration of the significant differentially expressed genes and lipids based on pathway enrichment analysis identified potentially targetable pathways related to glycerolipid/glycerophospholipid metabolism. Conclusions This study demonstrated the importance of lipids in understanding follicle development, thus providing a potential foundation to decipher the underlying mechanisms of lipid-mediated follicle development.

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Screening the Hub Genes and Analyzing the Mechanisms in Discharged COVID-19 Patients Retesting Positive through Bioinformatics Analysis

10.21203/rs.3.rs-991635/v1 ◽

2021 ◽

Author(s):

Ke-Ying Fang ◽

Gui-Ning Liang ◽

Zhuo-Qing Zhuang ◽

Yong-Xin Fang ◽

Yu-Qian Dong ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Social Order ◽

Expression System ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Virus Reactivation ◽

Hub Genes ◽

Messenger Ribonucleic Acid ◽

Ppi Networks ◽

Significant Enrichment

Abstract Background: With the worldwide spread of COVID-19, people’s health and social order have been exposed to enormous risks. After encountering patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation.Methods: A separate microarray was acquired from the Integrated Gene Expression System (GEO), and its samples were divided into two groups: a “convalescent-RTP” group consisting of recovery and “retesting-positive” (RTP) patients (group CR) and a “health-RTP” group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the gene ontology (GO) and Kyoto pluripotent stem cells (KEGG) of the genes and genomes. Subsequently, the protein–protein interaction (PPI) networks of each group were established and the hub genes were discovered using the cytoHubba plug-in.Results: In this study, 20 differentially expressed genes were identified, and 6622 genes were identified in the group CR, consisting of 5003 up-regulated and 1619 down-regulated genes. Meanwhile, 7335 genes were screened in the group HR, including 4323 up-regulated and 3012 down-regulated ones. The GO and KEGG analysis of the two groups revealed significant enrichment of these differentially expressed genes in pathways associated with immune response and apoptosis. In the PPI network constructed, 10 hub genes in group CR were identified, including TP53BP1, SNRPD1, SNRPD2, SF3B1, SNRNP200, MRPS16, MRPS9, CALM1, PPP2R1A, YWHAZ. Similarly, TP53BP1, RPS15, EFTUD2, MRPL16, MRPL17, MRPS14, RPL35A, MRPL32, MRPS6, POLR2G were selected as hub genes.Conclusions: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we explore the pathogenesis of retesting positive in COVID-19 from the immune mechanism and molecular level. We found TP53BP1, SNRPD1 and SNRPD2 as hub genes in RTP patients. Hence, their regulatory pathway is vital to the management and prognostic prediction of RTP patients, rendering the further study of these hub genes necessary.

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Comprehensive Analysis of Differentially Expressed Genes and Key Pathways in Neuropathic Pain by Spinal Nerve Ligation

10.21203/rs.3.rs-74125/v1 ◽

2020 ◽

Author(s):

Chao Xu ◽

HuiFang Li ◽

YunPeng Zhang ◽

TianYu Liu ◽

Yi Feng

Keyword(s):

Functional Analysis ◽

Neuropathic Pain ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Spinal Nerve ◽

Spinal Nerve Ligation ◽

Differentially Expressed ◽

Hub Genes ◽

Long Term Treatment

Abstract Background: Neuropathic pain can cause significant physical and economic burden to people, and there are no effective long-term treatment methods for this condition. We conducted a bioinformatics analysis of microarray data to identify related mechanisms to determine strategies for more effective treatments of neuropathic pain.Methods: GSE24982 and GSE63442 microarray datasets were extracted from the Gene Expression Omnibus (GEO) database to analyze transcriptome differences of neuropathic pain in the dorsal root ganglions caused by spinal nerve ligation. We filtered the differentially expressed genes (DEGs) in the two datasets and Webgestalt was applied to conduct GeneOntology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the shared DEGs. String Database and Cytoscape software were used to construct the Protein-Protein Interaction (PPI) network to determine the hub genes, which were subsequently verified in the GSE30691 dataset. Finally, miRDB and miRWalk Databases were used to predict potential miRNA of the selected DEGs.Results: A total of 182 overlapped DEGs were found between GSE24982 and GSE63442 datasets. The GO functional analysis and KEGG enrichment analysis showed that the selected DEGs were mainly enriched in infection, transmembrane transport of ion channels, and synaptic transmission. Combining the results of PPI analysis and the verification of the GSE30691 dataset, we identified seven hub genes related to neuropathic pain (Atf3, Aif1, Ctss, Gfap, Scg2, Jun, and Vgf). Predicted miRNA targeting each selected hub genes were identified.Conclusion: Seven hub genes related to the pathogenesis of neuropathic pain and potential targeting miRNA were identified, expanding understanding of the mechanism of neuropathic pain and facilitating treatment development.

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Bioinformatics analysis of potential core genes for glioblastoma

Bioscience Reports ◽

10.1042/bsr20201625 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Yu Zhang ◽

Xin Yang ◽

Xiao-Lin Zhu ◽

Jia-Qi Hao ◽

Hao Bai ◽

...

Keyword(s):

Survival Analysis ◽

Protein Interactions ◽

Poor Prognosis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Normal Brain ◽

Hub Genes ◽

Potential Core ◽

Core Genes

Abstract Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes. Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool. Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis. Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.

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A competing endogenous RNA mechanism in glioblastoma is investigated by bioinformatics analysis

10.21203/rs.2.21101/v1 ◽

2020 ◽

Author(s):

Jinsheng Wang ◽

Yutao Wang ◽

Lei Gao ◽

Yuhua Zhao ◽

Junhua Liu ◽

...

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Lupus Erythematosus ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Dendritic Cell Maturation ◽

Vital Role ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

P53 Signaling

Abstract Background Glioblastoma (GBM) is the most aggressive and most lethal primary malignant brain tumor, the 5-year survival rate of which is less than 5%. Novel potential molecular and mechanism of GBM need to investigate.Materials and methods Microarray data of GSE15824 was downloaded from GEO. Differentially expressed genes and lncRNAs were screened by Limma package in R studio, and pathway enrichment analysis was performed by clusterprofiler package in R studio and IPA. The ceRNA mechanism was analyzed and predicted by several kinds of online public databases.ResultsThere were 567 differentially expressed genes and 121 differentially expressed lncRNAs in GBM. And differentially expressed genes were mainly enriched in Tuberculosis, Staphylococcus aureus infection, Systemic lupus erythematosus, Basal cell carcinoma, TGF-beta signaling pathway and p53 signaling pathway. Besides, Neuroinflammation signaling pathway, Role of NFAT in regulation of the immune response, and Dendritic cell maturation were significantly activated in GBM. According to the analysis of target miRNAs of SEM4D and OSER1-AS1, a possible ceRNA mechanism OSER1-AS1/hsa-miR-520h/SEMA4D axis was predicted in GBM.Conclusion Bioinformatics analysis was employed to analyze GSE15824 chip, and predict the potential mechanism. The results revealed that the ceRNA mechanism, OSER1-AS1/hsa-miR-520h/SEMA4D axis, might play a vital role in GBM.

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