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.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

scholarly journals Weighted gene co-expression network analysis to define pivotal modules and genes in diabetic heart failure

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weiwei Liang ◽  
FangFang Sun
Keyword(s):  
Heart Failure ◽  
Kegg Pathway ◽  
Diabetic Heart ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Light Yellow ◽  
Go Enrichment ◽  
Key Genes ◽  
Functional Analyses

Abstract This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

scholarly journals Construction of a MicroRNA-mRNA Network Underlying Decidualized Endometriotic Cyst Stromal Cells Using Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Junzui Li ◽  
Bin Zhao ◽  
Cui Yang ◽  
Qionghua Chen
Keyword(s):  
Stromal Cells ◽  
Regulatory Networks ◽  
Target Genes ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
Overlapping Genes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Endometriotic Cyst

Background. Decidualization of ectopic endometrium often leads to the extensive proliferation of local tissue and is easily misdiagnosed as malignant tumors. The study is aimed at constructing a microRNA- (miRNA-) mRNA network underlying decidualized endometriotic cyst stromal cells (ECSCs). Methods. All data were collected from the Gene Expression Omnibus (GEO) database. Firstly, the differentially expressed genes (DEGs, adj. P‐Val<0.05, | log FC | ≥1) and miRNAs (DEMs, P‐Val<0.05, ∣log FC∣≥1) were analyzed by the limma package. Secondly, we predicted the target genes (TGs) of these DEMs through the TargetScan, miRDB, and miRTarBase databases. The overlapping genes between DEGs and TGs were screened out. Thirdly, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the overlapping genes were performed for integrated discovery, visualization, and annotation. Then, the protein-protein interaction (PPI) network of the overlapping genes was conducted by the STRING database. Finally, we combined the PPI network and the miRNA-mRNA pairs to build a miRNA-mRNA network. Results. There are 29 DEMs and 523 DEGs. Fourteen overlapping genes were screened out, and these genes were significantly enriched in metabolism and immunity. What is more, a miRNA-mRNA network, including 14 mRNAs and 9 miRNAs, was successfully constructed. Conclusions. Taken together, the miRNA-mRNA regulatory networks described in this study may provide new insights in the decidualization of ECSCs, suggesting further investigations in novel pathogenic mechanisms.

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 Bioinformatics Analysis of C3 and CXCR4 act as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma (ccRCC)

2021 ◽  
Author(s):  
Jing Quan ◽  
YUCHEN BAI ◽  
YunBei Yang ◽  
ErLei Han ◽  
Hong Bai ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
High Expression ◽  
Individual Treatment ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Protein Protein Interaction ◽  
Microarray Datasets ◽  
Upregulated Genes

Abstract Background: The molecular pathogenesis of ccRCC was still unknown. Hence, the ccRCC-associated genes needs to explored.Methods: Three ccRCC expression microarray datasets (GSE14762, GSE66270 and GSE53757) downloaded from gene expression omnibus (GEO) database. The distinguish of expressed genes (DEGs) between ccRCC and normal tissue was discuss and explored. the function of our DEGs was analyzed by Gene Ontology (GO) ,Kyoto Encyclopedia of Genes and Genomes (KEGG) .Then the protein‑protein interaction network (PPI) was established in order to screen the hub genes. Then the expressions of hub genes were identified by oncomine database.The prognostic values of hub genes were analyzed by GEPIA database in ccRCC patients. Result: A total of 137 DREs were analyzed, which including 63 upregulated genes and 74 downregulated genes. According to our result,137 DREs were mainly enriched in 82 functional terms and 24 pathways. 14 highest-scoring genes were screened as hub gene in the PPI network which including 12 upregulated candidate genes and 2 downregulated candidate genes. The result reveals that patients with higher C3 expression related to poor OS, while patients with high expression of CTSS and TLR3 related to better OS. Patients with high C3 and CXCR4 expression had a poor DFS, while ccRCC patients with high expression of TLR3 had better DFS. At last, C3 and CXCR4 were selected to detect the prognosis of patients with ccRCC.Conclusion: The result identified the C3 and CXCR4 as candidate biomarkers and potential therapeutic targets in the molecular mechanism and individual treatment of ccRCC.

scholarly journals Identification of Molecular Biomarkers and Key Pathways for Esophageal Carcinoma (EsC): A Bioinformatics Approach

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Md. Rakibul Islam ◽  
Mohammad Khursheed Alam ◽  
Bikash Kumar Paul ◽  
Deepika Koundal ◽  
Atef Zaguia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Esophageal Carcinoma ◽  
Gene Expression Omnibus ◽  
Molecular Biomarkers ◽  
Functional Study ◽  
Ppi Network ◽  
Network Information ◽  
Protein Protein Interaction ◽  
Cancer Group ◽  
Significant Gene

Esophageal carcinoma (EsC) is a member of the cancer group that occurs in the esophagus; globally, it is known as one of the fatal malignancies. In this study, we used gene expression analysis to identify molecular biomarkers to propose therapeutic targets for the development of novel drugs. We consider EsC associated four different microarray datasets from the gene expression omnibus database. Statistical analysis is performed using R language and identified a total of 1083 differentially expressed genes (DEGs) in which 380 are overexpressed and 703 are underexpressed. The functional study is performed with the identified DEGs to screen significant Gene Ontology (GO) terms and associated pathways using the Database for Annotation, Visualization, and Integrated Discovery repository (DAVID). The analysis revealed that the overexpressed DEGs are principally connected with the protein export, axon guidance pathway, and the downexpressed DEGs are principally connected with the L13a-mediated translational silencing of ceruloplasmin expression, formation of a pool of free 40S subunits pathway. The STRING database used to collect protein-protein interaction (PPI) network information and visualize it with the Cytoscape software. We found 10 hub genes from the PPI network considering three methods in which the interleukin 6 (IL6) gene is the top in all methods. From the PPI, we found that identified clusters are associated with the complex I biogenesis, ubiquitination and proteasome degradation, signaling by interleukins, and Notch-HLH transcription pathway. The identified biomarkers and pathways may play an important role in the future for developing drugs for the EsC.

scholarly journals Identification of Key Pathways and Genes in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

2020 ◽  
Author(s):  
Hao Li ◽  
Shimin Zong ◽  
Yingying Wen ◽  
Peiyu Du ◽  
Wenting Yu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Cell Cycle Checkpoints ◽  
Ppi Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Nasopharyngeal Tissue ◽  
Microarray Datasets ◽  
Selection Of

Abstract Purpose: The purpose of this study is to identify novel molecular markers and potential molecular targets for NPC based on bioinformatics analysis.Methods: We used bioinformatics to analyze one miRNA and two mRNA expression microarray datasets from the Gene Expression Omnibus database. The study included nasopharyngeal tissue samples from 57 patients with NPC and 32 patients without NPC. Fifty-one screened differentially expressed genes (DEGs) were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analyses, and a protein-protein interaction (PPI) network was constructed. Results: The GO analysis results showed that the DEGs were mainly related to cell cycle checkpoints, cell division, and DNA synthesis during DNA repair. The KEGG analysis results suggested that the DEGs were mainly associated with extracellular matrix receptor interactions. In the PPI network, we identified RAD51AP1, MAD2L1, SPP1, CCNE2, CNTNAP2, and MELK as hub genes, clustered a key module, and identified eight key transcription factors: TFII-I, Pax-5, STAT4, GR-alpha, YY1, C/EBPβ, GRβ, and TFIID. Conclusion: The hub genes and signaling pathways identified above may play an important role in NPC development and provide ideas for the selection of valuable prognostic markers and the development of new molecular-targeted drugs.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

Identification of Potential Genes Associated with Vemurafenib Efficacy and Melanoma Prognosis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Qi ◽  
GuiE Ma
Keyword(s):  
Signaling Pathway ◽  
Estrogen Signaling ◽  
Akt Signaling Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Ampk Signaling ◽  
Melanoma Patients ◽  
Key Genes ◽  
Neurotrophin Signaling ◽  
Upregulated Genes

Objective: This work aimed to investigate the molecular mechanisms underlying the efficacy of vemurafenib as a treatment for melanoma. Methods: The GSE52882 dataset, which includes A375 and A2058 melanoma cell lines treated with vemurafenib and dimethyl sulfoxide (DMSO), and clinical information associated with melanoma patients, were acquired from the Gene Expression Omnibus (GEO) database and University of California Santa Cruz (UCSC), respectively. Functional enrichment analysis, protein-protein interaction (PPI) network construction, sub-module analysis, and transcriptional regulation analysis were performed on overlapping differentially expressed genes (DEGs) identified in both cell lines. Finally, we performed a survival analysis based on the genes identified. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Results: A total of 447 consistently overlapping DEGs (176 up- and 271 down-regulated DEGs) were screened. Upregulated genes were enriched in pathways of neurotrophin signaling, estrogen signaling, and transcriptional misregulation in cancer. Downregulated DEGs played essential roles in melanogenesis, pathways of cancer, PI3K-Akt signaling pathway, and AMPK signaling pathway. Upregulated (MMP2, JUN, KAT28, and PIK3R3) and downregulated genes (CXCL8, CCND1, IGF1R, and ITGB3) were considered as hub genes in the PPI network. Additionally, PIK3R3 and LEF1 served as key genes in the regulatory network. The overexpression of MMP2 and CXCL8 was associated with a poor prognosis in melanoma patients. Conclusion: MMP2, CXCL8, PIK3R3, ITGB3, and LEF1 may play roles in the efficacy of vemurafenib treatment in melanoma; for example, MMP2 and PIK3R3 are likely associated with vemurafenib resistance. These findings will contribute to the development of novel therapies for melanoma.

scholarly journals Proteomic Analysis of Capacitated and Non-Capacitated Spermatozoa of Yanbian Yellow Cattle

2020 ◽  
Author(s):  
Xuan Chen ◽  
Zuochen Li ◽  
Yanqiu Lv ◽  
Yichao Xu ◽  
Mimi Cheng ◽  
...  
Keyword(s):  
Protein Transport ◽  
Expression Level ◽  
Molecular Processes ◽  
Proteomics Data ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Yellow Cattle ◽  
Specific Proteins ◽  
The Difference

Abstract Background: Sperm capacitation is a process which occurs prior to fertilization, and is essential for producing high-quality living embryos. The main purpose of this study was to explore the difference of proteomics between capacitated and non-capacitated sperm of Yanbian yellow cattle. Bioinformatic analyses of LC-MS/MS data included GO enrichment, KEGG pathway enrichment, and protein-protein interaction (PPI) analysis. Results: The results revealed 23 specific proteins in the capacitated group and 345 in the non-capacitated group. Compared with non-capacitated sperm, capacitated sperm exhibited 89 upregulated proteins and 509 downregulated proteins. Western blotting was used to confirm our proteomics data. The expression level of PSMD1 in the capacitated sperm group was significantly lower than that in the non-capacitated sperm group, and the expression level of HSPA5 was significantly higher than in the non-capacitated sperm group. Conclusions: Our results revealed that many proteins were differentially expressed between capacitated and non-capacitated sperm, particularly those involved in the proteasome signaling and protein transport signaling pathways. This work enhances our understanding of molecular processes involved in sperm viability in Yanbian yellow cattle, and provides a framework for future studies.

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