Identification of Potential Core Genes and Potential Drugs in Vestibular Schwannoma

Abstract Vestibular schwannoma is a common intracranial benign tumor, but the current drug treatment effect is not obvious. Surgical treatment can usually lead to residual problems such as nerve damage. Therefore, there is no clear molecular target to facilitate better clinical treatment. We analyzed three microarray data sets (GSE39645, GSE54934 and GSE108524) derived from the Gene Expression Omnibus database (GEO). The GEO2R was used to screen for the differentially expressed genes (DEG) between vestibular schwannomas and normal tissues. The ontology function of genes and genome pathway enrichment analysis were performed using annotation, visualizative and comprehensive discovery databases to identify the pathways and functional annotation of DEGs. The protein-protein interactions of these DEGs were analyzed by searching the interaction gene database and visualized by Cytoscape software. The potential therapeutic drugs for vestibular schwannoma were searched by online gene drug interaction analysis.A total of 226 up-regulated and 148 down-regulated DEGs were identified. Among them, ten hub genes with high connectivity (EGFR, PPARG, CD86, CSF1R, SPP1, CDH2, CCND1, CAV1, CYBB and NCAM1) were selected as the central genes that may be closely related to the pathogenesis of vestibular schwannoma, which can be potential treatment targets of vestibular schwannoma. Afatinib and osimerinib may be potential therapeutic drugs.

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Construction of ceRNA co-expression network and screening of molecular targets in colorectal cancer

10.21203/rs.2.17910/v1 ◽

2019 ◽

Author(s):

Hui Zhao ◽

Zhanwei Wang ◽

Xi Yang ◽

Jin Liu ◽

Jing Zhuang ◽

...

Keyword(s):

Colorectal Cancer ◽

Protein Interactions ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Protein Protein Interactions ◽

Normal Tissues ◽

Cerna Network ◽

Using Data ◽

Cancer Tissues

Abstract Objective to screen some RNAs that correlated with colorectal cancer (CRC).Methods Differentially expressed miRNAs, lncRNAs, and mRNAs between cancer tissues and normal tissues in CRC were identified using data from the Gene Expression Omnibus (GEO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interactions (PPIs) were performed to do the functioal enrichment analysis. And a lncRNA-miRNA-mRNA network was constructed wich correlated with CRC. RNAs in this network were subjecte to analyze the relationship with the patient prognosis.Results A total of 688, 241, and 103 differentially expressed genes (diff-mRNA), diff-lncRNA, and diff-miRNA were obtained. between cancer tissues and normal tissues. A total of 315 edges were obtained in the ceRNA network. lncRNA RP11-108K3.2 and mRNA ONECUT2 correlated with prognosis.Conclusion The identified RNAs and constructed ceRNA network could provide great sources for the reasearches of therapy the CRC. And the lncRNA RP11-108K3.2 and mRNA ONECUT2 may serve novel prognostic predictor of CRC.

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Glycyrrhizic Acid for COVID-19: Findings of Targeting Pivotal Inflammatory Pathways Triggered by SARS-CoV-2

Frontiers in Pharmacology ◽

10.3389/fphar.2021.631206 ◽

2021 ◽

Vol 12 ◽

Author(s):

Wenjiang Zheng ◽

Xiufang Huang ◽

Yanni Lai ◽

Xiaohong Liu ◽

Yong Jiang ◽

...

Keyword(s):

Transcription Factor ◽

Protein Interactions ◽

Glycyrrhizic Acid ◽

Enrichment Analysis ◽

Host Factors ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Health Crisis ◽

Inflammatory Pathways ◽

Anti Inflammatory

Background: Coronavirus disease 2019 (COVID-19) is now a worldwide public health crisis. The causative pathogen is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Novel therapeutic agents are desperately needed. Because of the frequent mutations in the virus and its ability to cause cytokine storms, targeting the viral proteins has some drawbacks. Targeting cellular factors or pivotal inflammatory pathways triggered by SARS-CoV-2 may produce a broader range of therapies. Glycyrrhizic acid (GA) might be beneficial against SARS-CoV-2 because of its anti-inflammatory and antiviral characteristics and possible ability to regulate crucial host factors. However, the mechanism underlying how GA regulates host factors remains to be determined.Methods: In our report, we conducted a bioinformatics analysis to identify possible GA targets, biological functions, protein-protein interactions, transcription-factor-gene interactions, transcription-factor-miRNA coregulatory networks, and the signaling pathways of GA against COVID-19.Results: Protein-protein interactions and network analysis showed that ICAM1, MMP9, TLR2, and SOCS3 had higher degree values, which may be key targets of GA for COVID-19. GO analysis indicated that the response to reactive oxygen species was significantly enriched. Pathway enrichment analysis showed that the IL-17, IL-6, TNF-α, IFN signals, complement system, and growth factor receptor signaling are the main pathways. The interactions of TF genes and miRNA with common targets and the activity of TFs were also recognized.Conclusions: GA may inhibit COVID-19 through its anti-oxidant, anti-viral, and anti-inflammatory effects, and its ability to activate the immune system, and targeted therapy for those pathways is a predominant strategy to inhibit the cytokine storms triggered by SARS-CoV-2 infection.

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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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Construction of ceRNA Coexpression Network and Screening of Molecular Targets in Colorectal Cancer

Disease Markers ◽

10.1155/2020/2860582 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Zhao Hui ◽

Wang Zhanwei ◽

Yang Xi ◽

Liu Jin ◽

Zhuang Jing ◽

...

Keyword(s):

Colorectal Cancer ◽

Protein Interactions ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Differentially Expressed ◽

Normal Tissues ◽

Cerna Network ◽

Cancer Tissues

Objective. To screen some RNAs that correlated with colorectal cancer (CRC). Methods. Differentially expressed miRNAs, lncRNAs, and mRNAs between cancer tissues and normal tissues in CRC were identified using data from the Gene Expression Omnibus (GEO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interactions (PPIs) were performed to do the functional enrichment analysis. And a lncRNA-miRNA-mRNA network was constructed which correlated with CRC. RNAs in this network were subjected to analyze the relationship with the patient prognosis. Results. A total of 688, 241, and 103 differentially expressed genes (diff-mRNA), diff-lncRNA, and diff-miRNA were obtained between cancer tissues and normal tissues. A total of 315 edges were obtained in the ceRNA network. lncRNA RP11-108K3.2 and mRNA ONECUT2 correlated with prognosis. Conclusion. The identified RNAs and constructed ceRNA network could provide great sources for the researches of therapy of the CRC. And the lncRNA RP11-108K3.2 and mRNA ONECUT2 may serve as a novel prognostic predictor of CRC.

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pathDIP 4: an extended pathway annotations and enrichment analysis resource for human, model organisms and domesticated species

Nucleic Acids Research ◽

10.1093/nar/gkz989 ◽

2019 ◽

Cited By ~ 5

Author(s):

Sara Rahmati ◽

Mark Abovsky ◽

Chiara Pastrello ◽

Max Kotlyar ◽

Richard Lu ◽

...

Keyword(s):

Protein Interactions ◽

Enrichment Analysis ◽

Human Model ◽

Model Organisms ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Proteome Coverage ◽

Pathway Annotation ◽

Human Proteins ◽

Integrated Pathway

Abstract PathDIP was introduced to increase proteome coverage of literature-curated human pathway databases. PathDIP 4 now integrates 24 major databases. To further reduce the number of proteins with no curated pathway annotation, pathDIP integrates pathways with physical protein–protein interactions (PPIs) to predict significant physical associations between proteins and curated pathways. For human, it provides pathway annotations for 5366 pathway orphans. Integrated pathway annotation now includes six model organisms and ten domesticated animals. A total of 6401 core and ortholog pathways have been curated from the literature or by annotating orthologs of human proteins in the literature-curated pathways. Extended pathways are the result of combining these pathways with protein-pathway associations that are predicted using organism-specific PPIs. Extended pathways expand proteome coverage from 81 088 to 120 621 proteins, making pathDIP 4 the largest publicly available pathway database for these organisms and providing a necessary platform for comprehensive pathway-enrichment analysis. PathDIP 4 users can customize their search and analysis by selecting organism, identifier and subset of pathways. Enrichment results and detailed annotations for input list can be obtained in different formats and views. To support automated bioinformatics workflows, Java, R and Python APIs are available for batch pathway annotation and enrichment analysis. PathDIP 4 is publicly available at http://ophid.utoronto.ca/pathDIP.

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Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽

10.7717/peerj.9633 ◽

2020 ◽

Vol 8 ◽

pp. e9633

Author(s):

Jie Meng ◽

Rui Su ◽

Yun Liao ◽

Yanyan Li ◽

Ling Li

Keyword(s):

Colorectal Cancer ◽

Network Analysis ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Data Sets ◽

Ppi Network ◽

Hub Genes ◽

Normal Tissues ◽

Go Enrichment ◽

Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

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Predicting the Molecular Mechanism of Sini Jia Renshen Decoction in Treating Severe COVID-19 Patients Based on Network Pharmacology and Molecular Docking

Natural Product Communications ◽

10.1177/1934578x211059292 ◽

2021 ◽

Vol 16 (12) ◽

pp. 1934578X2110592

Author(s):

Yi Wen Liu ◽

Ai Xia Yang ◽

Li Lu ◽

Tie Hua Huang

Keyword(s):

Molecular Docking ◽

Protein Interactions ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Active Ingredients ◽

Active Compounds ◽

Pathway Network ◽

Function Enrichment Analysis

Objective: To explore the potential mechanism of Sini jia Renshen Decoction (SJRD) in the treatment of COVID-19 based on network pharmacology and molecular docking. Methods: The active compounds and potential therapeutic targets of SJRD were collected through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Then a string database was used to build a protein–protein interactions (PPI) network between proteins, and use the David database to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct an active ingredients-core target-signaling pathway network, and finally the active ingredients of SJRD were molecularly docked with the core targets to predict the mechanism of SJRD in the treatment of COVID-19. Results: A total of 136 active compounds, 51 core targets and 93 signaling pathways were selected. Molecular docking results revealed that quercetin, 3,22-dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid, 18α-hydroxyglycyrrhetic acid, gomisin B and ignavine had considerable binding ability with ADRB2, PRKACA, DPP4, PIK3CG and IL6. Conclusions: This study preliminarily explored the mechanism of multiple components，multiple targets，and multiple pathways of SJRD in the treatment of COVID-19 by network pharmacology．

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Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

BioMed Research International ◽

10.1155/2020/8959210 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Jianxia Wei ◽

Yang Wang ◽

Kejian Shi ◽

Ying Wang

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Protein Interactions ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Protein Protein Interactions ◽

Normal Tissues ◽

Interactive Analysis ◽

Kaplan Meier ◽

Search Tool

Purposes. Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes. Results. The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues. Conclusion. We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

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Predicting the Molecular Mechanism of Shenling Baizhu San in Treating Convalescent Patients With COVID-19 Based on Network Pharmacology and Molecular Docking

Natural Product Communications ◽

10.1177/1934578x211046069 ◽

2021 ◽

Vol 16 (10) ◽

pp. 1934578X2110460

Author(s):

Ying Zhang ◽

Li Lu ◽

YiWen Liu ◽

AiXia Yang ◽

Yanfang Yang

Keyword(s):

Molecular Docking ◽

Protein Interactions ◽

Enrichment Analysis ◽

Binding Energies ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Protein Protein Interactions ◽

Active Ingredients ◽

Active Components ◽

Pathway Network

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein–protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than −5 kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.

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The relationship between LncRNAs and lung adenocarcinoma as well as their ceRNA network

Cancer Biomarkers ◽

10.3233/cbm-203078 ◽

2021 ◽

pp. 1-12

Author(s):

Yuxin Zhao ◽

Shuwen Ma ◽

Zhigang Cui ◽

Sixuan Li ◽

Yao Chen ◽

...

Keyword(s):

Lung Cancer ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Lung Cancer Patients ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Cerna Network ◽

Downstream Analysis

BACKGROUND: More and more studies have shown that long non-coding RNA (LncRNA) as a competing endogenous RNA (ceRNA) plays an important role in lung cancer. Therefore, we analyzed the RNA expression profiles of 82 lung cancer patients which were all from Gene Expression Omnibus (GEO). METHODS: Firstly, we used BLASTN (evalue = 1e-10) to annotate the gene sets, performed in-group correction and batched normalization of the four data sets with R. Secondly, we used the limma and sva packages to compare tumor tissues with normal tissues. Then through WGCNA, we obtained the 4 gene modules most related to the trait. RESULTS: We intersected the genes of above 4 modules with the differential expression genes: 28 LncRNAs (up: 5, down: 23) and 265 mRNAs (up:11, down: 254). Based on these genes, we picked up 6 LncRNAs (CCDC39, FAM182A, SRGAP3-AS2, ADAMTS9-AS2, AC020907.2, SFTA1P), then set and visualized the LncRNA-miRNA-mRNA ceRNA network with 12 miRNAs related to 12 mRNAs. Finally, we performed downstream analysis of 265 mRNAs by Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Protein-Protein Interaction (PPI) network. CONCLUSION: After analyzing, we think this study provides a new direction for basic and clinical research related to LAD, and is expected to provide new targets for early diagnosis, prognostic evaluation and clinical treatment of lung cancer.

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