scholarly journals Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xinyu Chong ◽  
Rui Peng ◽  
Yan Sun ◽  
Luyu Zhang ◽  
Zheng Zhang
Keyword(s):  
Gastric Cancer ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Key Genes ◽  
Secreted Phosphoprotein 1 ◽  
Centrality Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

scholarly journals Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guangyu Gao ◽  
Zhen Yao ◽  
Jiaofeng Shen ◽  
Yulong Liu
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

scholarly journals Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yiting Tian ◽  
Yang Xing ◽  
Zheng Zhang ◽  
Rui Peng ◽  
Luyu Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Extracellular Matrix ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Future Research ◽  
Circular Rnas ◽  
Ppi Network ◽  
Protein Protein Interaction

Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.

scholarly journals Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Guangda Yang ◽  
Liumeng Jian ◽  
Xiangan Lin ◽  
Aiyu Zhu ◽  
Guohua Wen
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Prognostic Value ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Trastuzumab Resistance ◽  
Hub Genes ◽  
Her2 Positive ◽  
Protein Protein Interaction ◽  
Stem Cell Pluripotency

Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

scholarly journals Systematic Elucidation of the Mechanism of Quercetin against Gastric Cancer via Network Pharmacology Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Liangjun Yang ◽  
Zhipeng Hu ◽  
Jiajie Zhu ◽  
Qiting Liang ◽  
Hengli Zhou ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Molecular Docking ◽  
Network Pharmacology ◽  
Ppi Network ◽  
Online Tool ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Novel Approach ◽  
Therapeutic Mechanisms ◽  
Kaplan Meier Plotter

This study was aimed at elucidating the potential mechanisms of quercetin in the treatment of gastric cancer (GC). A network pharmacology approach was used to analyze the targets and pathways of quercetin in treating GC. The predicted targets of quercetin against GC were obtained through database mining, and the correlation of these targets with GC was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Next, the protein-protein interaction (PPI) network was constructed, and overall survival (OS) analysis of hub targets was performed using the Kaplan–Meier Plotter online tool. Finally, the mechanism was further analyzed via molecular docking of quercetin with the hub targets. Thirty-six quercetin-related genes were identified, 15 of which overlapped with GC-related targets. These targets were further mapped to 319 GO biological process terms and 10 remarkable pathways. In the PPI network analysis, six hub targets were identified, including AKT1, EGFR, SRC, IGF1R, PTK2, and KDR. The high expression of these targets was related to poor OS in GC patients. Molecular docking analysis confirmed that quercetin can bind to these hub targets. In conclusion, this study provided a novel approach to reveal the therapeutic mechanisms of quercetin on GC, which will ease the future clinical application of quercetin in the treatment of GC.

scholarly journals Bioinformatics Analysis to Identify Key Genes and Pathways Associated with Sex Differences in Rheumatoid Arthritis

2021 ◽  
Author(s):  
Siwei Su ◽  
Wenjun Jiang ◽  
Xiaoying Wang ◽  
Sen Du ◽  
Lu Zhou ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Males And Females ◽  
Key Genes

Abstract ObjectiveThis study aims to explore the key genes and investigated the different signaling pathways of rheumatoid arthritis (RA) between males and females.Data and MethodsThe gene expression data of GSE55457, GSE55584, and GSE12021 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software. Then, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of DEGs were constructed by Cytoscape 3.6.0. ResultsA total of 416 upregulated DEGs and 336 downregulated DEGs were identified in males, and 744 upregulated DEGs and 309 downregulated DEGs were identified in females.IL6, MYC, EGFR, FOS and JUN were considered as hub genes in RA pathogenesis in males, while IL6, ALB, PTPRC, CXCL8 and CCR5 were considered as hub genes in RA pathogenesis in females. ConclusionIdentified DEG may be involved in the different mechanisms of RA disease progression between males and females, and they are treated as prognostic markers or therapeutic targets for males and females. The pathogenesis mechanism of RA is sex-dependent.

scholarly journals Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingni Wu ◽  
Xiaomeng Xia ◽  
Ye Hu ◽  
Xiaoling Fang ◽  
Sandra Orsulic
Keyword(s):  
Network Analysis ◽  
Topological Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

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

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.

scholarly journals Identification of the Key Genes and Pathways in Esophageal Carcinoma

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peng Su ◽  
Shiwang Wen ◽  
Yuefeng Zhang ◽  
Yong Li ◽  
Yanzhao Xu ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Esophageal Carcinoma ◽  
Gene Expression Omnibus ◽  
Expression Level ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Key Genes ◽  
Microarray Datasets ◽  
Upregulated Genes

Objective. Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it.Methods. 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC.Results. A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis.Conclusion. The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.

scholarly journals COL1A1, COL1A2, COL3A1 and DCN are Potential Biomarkers to Predict Progression from Smokers to Suffering from Lung Adenocarcinoma

2020 ◽  
Author(s):  
Jixin Chen ◽  
Shuqi Chen ◽  
Feiye Wang ◽  
Sumei Wang ◽  
Wanyin Wu
Keyword(s):  
Lung Adenocarcinoma ◽  
Single Cell ◽  
Genetic Alterations ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Expression Atlas ◽  
Targeted Drug ◽  
Key Genes ◽  
Potential Biomarkers

Abstract Background: To explore novel related genes and potential biomarkers that predict progression from smokers to lung adenocarcinoma (LA). Methods: Three datasets from GEO (Gene Expression Omnibus) database were used to identify differentially expressed genes (DEGs) between LA tissue (LAT) and normal tumor adjacent tissue (TAT). The overlap of DEGs could be found and enriched in gene oncology (GO) and pathways to discover the potential biological mechanisms. Protein-protein interaction (PPI) network was applied to find the relationship among proteins. Survival analysis contributed to the definiteness of key genes. The expression of key genes in LA patients who smoke was verified. Furthermore, genetic alterations, co-expression and pathways of key genes were explored. To obtain more information, key genes were further analyzed in immune infiltration, drug target and the distribution of single cell in LA. Results: 245 DEGs were revealed in 3 datasets from GEO. In Kaplan Meier plotter, we found that high expression of COL1A1, COL1A2 and COL3A1 was associated with poorer survival while low expression of DCN was contributed to poorer survival in LAs who smoke. Thus, three up-regulated genes (COL1A1, COL1A2, COL3A1) and one down-regulated gene (DCN) were defined as key genes. Their genetic alterations were more common in female LA smokers and co-expression genes/proteins of them mainly functioned at extracellular matrix. Furthermore, COL1A1, COL1A2, COL3A1 genes had a common targeted drug called Collagenase clostridium histolyticum (DB00048) and DCN gene had a targeted drug called Tromethamine (DB03754). In the Single Cell Expression Atlas of EMBL-EBL, COL3A1 gene was specifically highly expressed in female LA patients with brain metastasis. Conclusions: COL1A1, COL1A2, COL3A1 and DCN could be regarded as novel potential biomarkers that predict progression from smokers to lung adenocarcinoma.

scholarly journals Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yanzhe Wang ◽  
Wenjuan Cai ◽  
Liya Gu ◽  
Xuefeng Ji ◽  
Qiusheng Shen
Keyword(s):  
Atrial Fibrillation ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Cell Counting ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Protein Protein Interaction ◽  
Endothelial Growth Factor

Purpose. Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods. The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result. 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion. Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.

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