Bioinformatic Analysis: Screening and Identification of Differentially Expressed Genes Modified by m6A in Ovarian Cancer

Abstract Background: N6-methyladenosine(m6A) is one of the most common RNA modifications that occurs at the nitrogen-6 position of adenine. Emerging evidence has revealed that regulatory functions of m6A play an essential role in the development of cancer. However the study of m6A in ovarian cancer(OC) is still in our infancy. In this work ,we aimed to identify and analysis the differentially expressed genes(DEGs） modified by m6A which can provide new therapeutic targets and key biomarkers in OC.Methods: We downloaded Microarray datasets GSE146553 and GSE124766 from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by GEO2R analysis tools. Subsequently, The DAVID database was used to construct Enrichment analysis of GO and KEGG pathways. Next, the DEGs modified by m6A were identified by m6AVar database. Finally, the functional analysis and clinical sample validation of these genes were verified by ONCOMINE, GEPIA, cBioPortal online platform and Kaplan-Meier Plotter.Results:152 DEGs were selected ,and the DEGs were mainly enriched in extracellular exosome, spindle microtubule, response to hypoxia and cell cycle .And we identified 15 DEGs which were modified by m6A:MAPK10、MXRA5、CHD7、MECOM、SCN7A、GREB、PRUNE2、MX2、TOP2A、JAM2、DST、LAPTM5、CDKN2A、GATM and ANGPTL1. After statistical analysis, two DEGs (SCN7A and GAMT) were selected for detailed study. We revealed that SCN7A and GAMT were expressed at a low level in OC. Afterwards, Survival analysis showed that SCN7A and GAMT expression were correlated with OC overall survival. And the expression of SCN7A and GAMT mRNA decreasing in different TNM stages. Finally, we presumed that the modification of m6A spongs GAMT via EIF4A3 or FUS to participate in the occcurrence and the development of OC.Conclusion: Altogether, the current study identified and analysised the DEGs modified by m6A in OC. It will help us to investigate the underlying mechanism and progression of OC. In addition, it can provide new diagnostic markers and potential therapeutic targets in OC.

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High SLC7A11 Expression Is Correlated With Poor Prognosis and Associated With Ferroptosis in Ovarian Cancer

10.21203/rs.3.rs-233820/v1 ◽

2021 ◽

Author(s):

Mengqi Deng ◽

Yanqin Zhang ◽

Xiangyu Chang ◽

Di Wu ◽

Chunyu Xu ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Protein Expression ◽

Differentially Expressed Genes ◽

Poor Prognosis ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Normal Tissues ◽

And Control

Abstract The current treatments of ovarian cancer (OC) do not yield satisfactory outcomes. Hence, it is necessary to find new treatment targets for OC. In this study, a comprehensive bioinformatic analysis was conducted to identify differentially expressed genes (DEGs) between OC and control tissues. Five datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by comparing gene expression between OC and control tissues. Module analysis of DEGs was performed on the STRING database and GEPIA. Kaplan Meier plotter and GEPIA database analysis the overall survival. Finally, SLC7A11 was found to be is the hubgene. And we confirm that the protein expression of SLC7A11 was increased in OC tissues. Analysis of a variety of tumor gene databases showed that SLC7A11 gene regulated the processes of OC. The low mutation rate of the gene (which were of amplified type) and high mRNA expression were associated with poor prognosis of OC patients.Using erastin-treated ovarian cancer (OC) cell lines, we examined the relationship between ferroptosis and OC. Results showed that OC tissues contained higher malondialdehyde (MDA) levels than normal tissues. Unlike normal ovarian epithelial cells which are not sensitive to erastin, the OC cell line, ES-2 is very sensitive to erastin. Here, we found that ferrostatin-1 treatment increased levels of reactive oxygen species (ROS), malondialdehyde, and SLC7A11 protein expression. These results provide an important theoretical basis for further studies into the role of SLC7A11, the effective biomarker and potential drug target, in the occurrence and development of OC.

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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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Identification of key genes, pathways and potential therapeutic agents for IgA nephropathy using an integrated bioinformatics analysis

Journal of the Renin-Angiotensin-Aldosterone System ◽

10.1177/1470320320919635 ◽

2020 ◽

Vol 21 (2) ◽

pp. 147032032091963

Author(s):

Xiaoxue Chen ◽

Mindan Sun

Keyword(s):

Differentially Expressed Genes ◽

Immunoglobulin A ◽

Therapeutic Targets ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Signal Pathways ◽

Immunoglobulin A Nephropathy ◽

Hub Genes ◽

Limma Package

Purpose: This study aims to identify immunoglobulin-A-nephropathy-related genes based on microarray data and to investigate novel potential gene targets for immunoglobulin-A-nephropathy treatment. Methods: Immunoglobulin-A-nephropathy chip data was obtained from the Gene Expression Omnibus database, which included 10 immunoglobulin-A-nephropathy and 22 normal samples. We used the limma package of R software to screen differentially expressed genes in immunoglobulin-A-nephropathy and normal glomerular compartment tissues. Functional enrichment (including cellular components, molecular functions, biological processes) and signal pathways were performed for the differentially expressed genes. The online analysis database (STRING) was used to construct the protein-protein interaction networks of differentially expressed genes, and Cytoscape software was used to identify the hub genes of the signal pathway. In addition, we used the Connectivity Map database to predict possible drugs for the treatment of immunoglobulin-A-nephropathy. Results: A total of 348 differentially expressed genes were screened including 107 up-regulated and 241 down-regulated genes. Functional analysis showed that up-regulated differentially expressed genes were mainly concentrated on leukocyte migration, and the down-regulated differentially expressed genes were significantly enriched in alpha-amino acid metabolic process. A total of six hub genes were obtained: JUN, C3AR1, FN1, AGT, FOS, and SUCNR1. The small-molecule drugs thapsigargin, ciclopirox and ikarugamycin were predicted therapeutic targets against immunoglobulin-A-nephropathy. Conclusion: Differentially expressed genes and hub genes can contribute to understanding the molecular mechanism of immunoglobulin-A-nephropathy and providing potential therapeutic targets and drugs for the diagnosis and treatment of immunoglobulin-A-nephropathy.

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Identification and Interaction Analysis of Significant Genes and MicroRNAs in Pterygium

BioMed Research International ◽

10.1155/2019/2767512 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Siying He ◽

Hui Sun ◽

Yifang Huang ◽

Shiqi Dong ◽

Chen Qiao ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Interaction Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Differentially Expressed Mirnas

Purpose. MiRNAs have been widely analyzed in the occurrence and development of many diseases, including pterygium. This study aimed to identify the key genes and miRNAs in pterygium and to explore the underlying molecular mechanisms. Methods. MiRNA expression was initially extracted and pooled by published literature. Microarray data about differentially expressed genes was downloaded from Gene Expression Omnibus (GEO) database and analyzed with the R programming language. Functional and pathway enrichment analyses were performed using the database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction network was constructed with the STRING database. The associations between chemicals, differentially expressed miRNAs, and differentially expressed genes were predicted using the online resource. All the networks were constructed using Cytoscape. Results. We found that 35 miRNAs and 301 genes were significantly differentially expressed. Functional enrichment analysis showed that upregulated genes were significantly enriched in extracellular matrix (ECM) organization, while downregulated genes were mainly involved in cell death and apoptotic process. Finally, we concluded the chemical-gene affected network, miRNA-mRNA interacted networks, and significant pathway network. Conclusion. We identified lists of differentially expressed miRNAs and genes and their possible interaction in pterygium. The networks indicated that ECM breakdown and EMT might be two major pathophysiological mechanisms and showed the potential significance of PI3K-Akt signalling pathway. MiR-29b-3p and collagen family (COL4A1 and COL3A1) might be new treatment target in pterygium.

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Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

10.21203/rs.3.rs-24106/v1 ◽

2020 ◽

Author(s):

Sheng Chang ◽

Yang Cao

Keyword(s):

Signaling Pathway ◽

Differentially Expressed Genes ◽

Osteogenic Sarcoma ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

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Identification of LINC01503 as Biomarker Regulated by CTBP1 with Prognostic and Diagnostic Role in Epithelial Ovarian Cancer

10.21203/rs.3.rs-870755/v1 ◽

2021 ◽

Author(s):

Yanchun Wang ◽

Zheng Wei ◽

Junping Zhang ◽

Xuemei Wang ◽

Xiaohua Li

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Underlying Mechanism ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

High Morbidity ◽

Promote Cell Proliferation ◽

Important Relationship

Abstract Background: Epithelial ovarian cancer (EOC) is a disease with high morbidity and mortality worldwide, which is seriously harmful to female health. LncRNA has an important relationship with the occurrence and development of tumors. Hence, the investigation of the underlying mechanism between LncRNA and EOC is of great importance.Results: In this study, we found that LINC01503 was highly expressed in EOC with a poor prognosis based on microarray datasets GSE119056 and GSE135886 obtained from Gene Expression Omnibus (GEO) database, and this result was verified by RT-qPCR. The database lncBase Predicted v.2 and starBase v2.0 were used to predict the targeted relationship of lncRNA-miRNA-mRNA, then the ceRNA network was established by Cytoscape software. Following, the expression and overall survival (OS) analysis of key lncRNAs were analyzed by GEPIA and Kaplan-Meier plotter database. Gene Ontology (GO) functional enrichment analysis was performed by DAVID database and enriched two cancer related biological processes (BP) that response to endoplasmic reticulum stress and IRE1-mediated unfolded protein. Moreover, we verified that LINC01503 was an oncogene regulated by C-terminal binding protein 1 (CTBP1) to promote cell proliferation, migration and inhibited cell apoptosis in ovarian cancer. Conclusion: In conclusion, these results identified LINC01503 as a potential gene for EOC diagnosis and prognosis.

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The Genetics Analysis of Molecular Pathogenesis for Alzheimer's Disease

European Neurology ◽

10.1159/000509799 ◽

2020 ◽

Vol 83 (5) ◽

pp. 458-467

Author(s):

Guanchuan Lin ◽

Kaiyuan Ji ◽

Shiyu Li ◽

Wenli Ma ◽

Xinghua Pan

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Brain Regions ◽

Molecular Pathogenesis ◽

Differentially Expressed ◽

Control Group ◽

Membrane Structures

Introduction: The molecular pathogenesis of Alzheimer’s disease (AD) is still not clear, and the relationship between gene expression profile for different brain regions has not been studied. Objective: Bioinformatic analysis at the genetic level has become the best way for the pathogenesis research of AD, which can analyze the abovementioned relationship. Methods: In this study, the datasets of AD were obtained from the Gene Expression Omnibus (GEO), and Qlucore Omics Explorer (QOE) software was used to screen differentially expressed genes of GSE36980 and GSE9770 and verify gene expression of GSE63060. The Gene Ontology (GO) function enrichment analysis of these selected genes was conducted by Database for Annotation, Visualization, and Integrated Discovery (DAVID), and then the gene/protein interaction network was established by STRING to find the related proteins. R language was used for drafting maps and plots. Results: There were 20 differentially expressed genes related to AD selected from GSE36980 (p = 6.2e−6, q = 2.9422e−4) and GSE9770 (p = 3.3e−4, q = 0.016606). Their expression levels of the AD group were lower than those in the control group and varied among different brain regions. Cellular morphogenesis and establishment or maintenance of cell polarity were enriched, and LRRTM1 and RASAL1 were identified by the integration network. Moreover, the analysis of GSE63060 verified the expression level of LRRTM1 and RASAL1 in Alzheimer’s patients, which was much lower than that in normal people aged >65 years. Conclusions: The pathogenesis of AD at molecular levels may link to cell membrane structures and signal transduction; hence, a list of 20 genes, including LRRTM1 and RASAL1,potentially are important for the discovery of treatment target or molecular marker of AD.

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Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

10.21203/rs.3.rs-952508/v1 ◽

2021 ◽

Author(s):

Zhengqiang He

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Actin Filament ◽

Enrichment Analysis ◽

Underlying Mechanism ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Protein Protein Interaction

Abstract More and more studies have suggested that type 2 diabetes mellitus (T2DM) was susceptible to trigger Alzheimer’s disease(AD), but the common underlying mechanism were unclear. We want to perform bioinformatic analysis with public databases. The T2DM dataset GSE95849 and AD dataset GSE97760 were selected from Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) and the communal DEGs between the two diseases, which perform to the enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis.We found 255 communal DEGs between T2DM and AD. They were enriched in negative regulation of actin filament depolymerization and regulation of actin filament depolymerization. Top 5 hub genes which identified from the PPI network were enriched in autophagy. The actin filament and autophagy may be the key association between the two diseases.

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Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

10.21203/rs.3.rs-52936/v1 ◽

2020 ◽

Author(s):

Ming Cao ◽

Chen Shen ◽

Jie Zhu ◽

YuHai Wang

Keyword(s):

Molecular Mechanisms ◽

Therapeutic Targets ◽

Microarray Dataset ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Benign Meningioma ◽

Protein Protein Interaction ◽

Kegg Pathways ◽

Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

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Based on bioinformatics analysis, NF1, BTRC and MAPK14 are the potential biomarkers in the progression of osteoarthritis synovitis

10.21203/rs.3.rs-349764/v1 ◽

2021 ◽

Author(s):

Mingyi Yang ◽

Yani Su ◽

Yao Ma ◽

Yirixiati Aihaiti ◽

Peng Xu

Keyword(s):

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Hub Gene ◽

Potential Biomarkers

Abstract Objective: To study the potential biomarkers and related pathways in osteoarthritis (OA) synovial lesions, and to provide theoretical basis and research directions for the pathogenesis and treatment of OA. Methods: Download the microarray data sets GSE12021 and GSE82107 from Gene Expression Omnibus. GEO2R recognizes differentially expressed genes. Perform functional enrichment analysis of differentially expressed genes and construct protein-protein interaction network. Cytoscape performs module analysis and enrichment analysis of top-level modules. Further identify the Hub gene and perform functional enrichment analysis. TargetScan, miRDB and miRWalk three databases predict the target miRNAs of Hub gene and identify key miRNAs. Results: Finally, 10 Hub genes and 17 key miRNAs related to the progression of OA synovitis were identified. NF1, BTRC and MAPK14 may play a vital role in OA synovial disease. Conclusion: The Hub genes and key miRNAs discovered in this study may be potential biomarkers in the development of OA synovitis, and provide research methods and target basis for the pathogenesis and treatment of OA.

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