scholarly journals Identification of Core Biomarkers Associated with Outcome in Glioma: Evidence from Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Rong-Xin Geng ◽  
Ning Li ◽  
Yang Xu ◽  
Jun-hui Liu ◽  
Fan-en Yuan ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Bioinformatics Analysis ◽  
Receptor Interaction ◽  
Normal Brain ◽  
Gabaergic Synapse ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Kaplan Meier ◽  
Search Tool ◽  
Upregulated Genes

Glioma is the most common neoplasm of the central nervous system (CNS); the progression and outcomes of which are affected by a complicated network of genes and pathways. We chose a gene expression profile of GSE66354 from GEO database to search core biomarkers during the occurrence and development of glioma. A total of 149 samples, involving 136 glioma and 13 normal brain tissues, were enrolled in this article. 1980 differentially expressed genes (DEGs) including 697 upregulated genes and 1283 downregulated genes between glioma patients and healthy individuals were selected using GeoDiver and GEO2R tool. Then, gene ontology (GO) analysis as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plug-in was employed to imagine protein-protein interaction (PPI) of these DEGs. The upregulated genes were enriched in cell cycle, ECM-receptor interaction, and p53 signaling pathway, while the downregulated genes were enriched in retrograde endocannabinoid signaling, glutamatergic synapse, morphine addiction, GABAergic synapse, and calcium signaling pathway. Subsequently, 4 typical modules were discovered by the PPI network utilizing MCODE software. Besides, 15 hub genes were chosen according to the degree of connectivity, including TP53, CDK1, CCNB1, and CCNB2, the Kaplan-Meier analysis of which was further identified. In conclusion, this bioinformatics analysis indicated that DEGs and core genes, such as TP53, might influence the development of glioma, especially in tumor proliferation, which were expected to be promising biomarkers for diagnosis and treatment of glioma.

Bioinformatics Analysis Identifies CPZ as a Tumor Immunology Biomarker For Gastric Cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Yuan Gu ◽  
Ying Gao ◽  
Xiaodan Tang ◽  
Huizhong Xia ◽  
Kunhe Shi
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Signaling Pathway ◽  
Tumor Immunology ◽  
Bioinformatics Analysis ◽  
Human Cancer ◽  
Disease Free Survival ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Pathway Regulation

Background: Gastric cancer (GC) is one of the most common malignancies worldwide. However, the biomarkers for the prognosis and diagnosis of Gastric cancer were still need. Objective: The present study aimed to evaluate whether CPZ could be a potential biomarker for GC. Method: Kaplan-Meier plotter (http://kmplot.com/analysis/) was used to determine the correlation between CPZ expression and overall survival (OS) and disease-free survival (DFS) time in GC [9]. We analyzed CPZ expression in different types of cancer and the correlation of CPZ expression with the abundance of immune infiltrates, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells, via gene modules using TIMER Database. Results: The present study identified that CPZ was overexpressed in multiple types of human cancer, including Gastric cancer. We found that overexpression of CPZ correlates to the poor prognosis of patients with STAD. Furthermore, our analyses show that immune infiltration levels and diverse immune marker sets are correlated with levels of CPZ expression in STAD. Bioinformatics analysis revealed that CPZ was involved in regulating multiple pathways, including PI3K-Akt signaling pathway, cGMP-PKG signaling pathway, Rap1 signaling pathway, TGF-beta signaling pathway, regulation of cell adhesion, extracellular matrix organization, collagen fibril organization, collagen catabolic process. Conclusion: This study for the first time provides useful information to understand the potential roles of CPZ in tumor immunology and validate it to be a potential biomarker for GC.

scholarly journals Transcriptomic Analysis of Spleen Revealed Mechanism of Dexamethasone-Induced Immune Suppression in Chicks

Genes ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 513 ◽  
Author(s):  
Yujie Guo ◽  
Aru Su ◽  
Huihui Tian ◽  
Minxi Zhai ◽  
Wenting Li ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Immune Suppression ◽  
Molecular Genetic ◽  
Daily Injection ◽  
Receptor Interaction ◽  
P Value ◽  
Specific Mechanism ◽  
Base Pairs ◽  
Protein Protein Interaction ◽  
Iga Production

Stress-induced immunosuppression is a common problem in the poultry industry, but the specific mechanism of its effect on the immune function of chicken has not been clarified. In this study, 7-day-old Gushi cocks were selected as subjects, and a stress-induced immunosuppression model was successfully established via daily injection of 2.0 mg/kg (body weight) dexamethasone. We characterized the spleen transcriptome in the control (B_S) and model (D_S) groups, and 515 significant differentially expressed genes (SDEGs) (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced (FPKM) > 1, adjusted p-value (padj) < 0.05 and Fold change (|FC|) ≥ 2) were identified. The cytokine-cytokine receptor interaction signaling pathway was identified as being highly activated during stress-induced immunosuppression, including the following SDEGs—CXCL13L2, CSF3R, CSF2RB, CCR9, CCR10, IL1R1, IL8L1, IL8L2, GHR, KIT, OSMR, TNFRSF13B, TNFSF13B, and TGFBR2L. At the same time, immune-related SDEGs including CCR9, CCR10, DMB1, TNFRSF13B, TNFRSF13C and TNFSF13B were significantly enriched in the intestinal immune network for the IgA production signaling pathway. The SDEG protein-protein interaction module analysis showed that CXCR5, CCR8L, CCR9, CCR10, IL8L2, IL8L1, TNFSF13B, TNFRSF13B and TNFRSF13C may play an important role in stress-induced immunosuppression. These findings provide a background for further research on stress-induced immunosuppression. Thus, we can better understand the molecular genetic mechanism of chicken stress-induced immunosuppression.

scholarly journals The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins

2019 ◽  
Author(s):  
Jarmila Nahálková
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Age Related ◽  
P53 Signaling Pathway ◽  
Protein Protein Interaction Network

The protein-protein interaction network of seven pleiotropic proteins (PIN7) contains proteins with multiple functions in the aging and age-related diseases (TPPII, CDK2, MYBBP1A, p53, SIRT6, SIRT7, and BSG). At the present work, the pathway enrichment, the gene function prediction and the protein node prioritization analysis were applied for the examination of main molecular mechanisms driving PIN7 and the extended network. Seven proteins of PIN7 were used as an input for the analysis by GeneMania, a Cytoscape application, which constructs the protein interaction network. The software also extends it using the interactions retrieved from databases of experimental and predicted protein-protein and genetic interactions. The analysis identified the p53 signaling pathway as the most dominant mediator of PIN7. The extended PIN7 was also analyzed by Cytohubba application, which showed that the top-ranked protein nodes belong to the group of histone acetyltransferases and histone deacetylases. These enzymes are involved in the reverse epigenetic regulation mechanisms linked to the regulation of PTK2, NFκB, and p53 signaling interaction subnetworks of the extended PIN7. The analysis emphasized the role of PTK2 signaling, which functions upstream of the p53 signaling pathway and its interaction network includes all members of the sirtuin family. Further, the analysis suggested the involvement of molecular mechanisms related to metastatic cancer (prostate cancer, small cell lung cancer), hemostasis, the regulation of the thyroid hormones and the cell cycle G1/S checkpoint. The additional data-mining analysis showed that the small protein interaction network MYBBP1A-p53-TPPII-SIRT6-CD147 controls Warburg effect and MYBBP1A-p53-TPPII-SIRT7-BSG influences mTOR signaling and autophagy. Further investigations of the detail mechanisms of these interaction networks would be beneficial for the development of novel treatments for aging and age-related diseases.

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

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.

scholarly journals Bcl-2 Is Involved in Cardiac Hypertrophy through PI3K-Akt Pathway

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xianwei Meng ◽  
Jun Cui ◽  
Guibin He
Keyword(s):  
Gene Expression ◽  
Cardiac Hypertrophy ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
B Cell Lymphoma ◽  
Akt Signaling ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Akt Signaling Pathway ◽  
Upregulated Genes

Cardiac hypertrophy (CH) is a common cause of sudden cardiac death and heart failure, resulting in a significant medical burden. The present study is aimed at exploring potential CH-related pathways and the key downstream effectors. The gene expression profile of GSE129090 was obtained from the Gene Expression Omnibus database (GEO), and 1325 differentially expressed genes (DEGs) were identified, including 785 upregulated genes and 540 downregulated genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway enrichment analysis of DEGs were then performed. Although there were no pathways enriched by downregulated genes, many CH-related pathways were identified by upregulated genes, including PI3K-Akt signaling pathway, extracellular matrix- (ECM-) receptor interaction, regulation of actin cytoskeleton, and hypertrophic cardiomyopathy (HCM). In the deeper analysis of PI3K-Akt signaling pathway, we found all the signaling transduction pointed to B cell lymphoma-2- (Bcl-2-) mediated cell survival. We then demonstrated that PI3K-Akt signaling pathway was indeed activated in cardiac hypertrophy. Furthermore, no matter LY294002, an inhibitor of the PI3K/AKT signaling pathway, or Venetoclax, a selective Bcl-2 inhibitor, protected against cardiac hypertrophy. In conclusion, these data indicate that Bcl-2 is involved in cardiac hypertrophy as a key downstream effector of PI3K-Akt signaling pathway, suggesting a potential therapeutic target for the clinical management of cardiac hypertrophy.

scholarly journals Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases

2021 ◽  
Author(s):  
Qiangqiang Zheng ◽  
Shihui Min ◽  
Qinghua Zhou
Keyword(s):  
Signaling Pathway ◽  
Clinical Features ◽  
Enrichment Analysis ◽  
Expression Level ◽  
Normal Lung ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Viral Carcinogenesis ◽  
Relationship Of

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The current study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan-Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5,930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM-receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression level of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression level of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD, and may be potential therapeutic targets for LUAD.

scholarly journals Differential Expression of MicroRNAs and their Possible Roles in Patients with Chronic Idiopathic Urticaria and Active Hives

2017 ◽  
Vol 8 (2) ◽  
pp. ar.2017.8.0199 ◽  
Author(s):  
Ching-Kow E. Lin ◽  
John S. Kaptein ◽  
Javed Sheikh
Keyword(s):  
Signaling Pathway ◽  
Transforming Growth Factor Beta ◽  
Transforming Growth Factor ◽  
Chronic Idiopathic Urticaria ◽  
Receptor Interaction ◽  
Messenger Rnas ◽  
P53 Signaling ◽  
Autoimmune Urticaria ◽  
Complicated Skin ◽  
P21 Activated Kinase

Background Chronic idiopathic urticaria (CIU) is a complicated skin disease with unknown pathophysiology. MicroRNAs (miRNA) have been shown to be active in cellular regulation. The goal of this pilot study was to examine whether miRNAs may be involved in the regulation of CIU or as biomarkers for CIU. Methods Four groups of three patients each were selected: patients with either active hives or no hives and with positive or negative chronic urticaria (CU) index results. MiRNAs were isolated from patient plasma and analyzed by using miRNA microarray technology to determine the amount of each of the 2567 known human miRNAs. Results A total of 16 miRNAs were found to be differentially expressed in patients with active hives. Among them, five (2355–3p, 4264, 2355–5p, 29c-5p, and 361–3p) were significantly increased in samples with positive CU index results, which could be useful biomarkers for patients with chronic autoimmune urticaria. The miRNA data bases were used to find the targets of these selected miRNA sequences. These potential targets were then compared against a list of 154 urticaria-related genes. Twenty-five genes were found to match. These included eight that were significantly downregulated and eight that were significantly upregulated; however, seven of the eight downregulated genes (FBXL20, OPHN1, YPEL2, STARD9, EZH1, KLHL24, ING4) and five of the eight upregulated genes (BYSL, PNO1, ADAMTS9, STEAP4, SRGN) have no reported roles in signaling. For the 13 genes with reported roles in signaling, the following pathways were found: transforming growth factor beta signaling pathway (NRC31, KITLG, THBS1, CCL2), glucocorticoid receptor signaling pathway (NR3C1, SELE, CCL2), p53 signaling pathway (CCNG2, THBS1, CCL2), p21-activated kinase pathway (PAK1IP1, KITLG, CCL2), phosphoinositide-3 kinase protein kinase B signaling pathway (KITLG, CHRM, THBS1), and neuroactive ligand-receptor interaction (NRC31, HRH1, CHRM), which could play important roles in CIU. Conclusion A better understanding of those genes with undefined function and simultaneous quantitation of both miRNAs and messenger RNAs are needed to fully understand CIU disease.

scholarly journals Identification of key genes involved in type 2 diabetic islet dysfunction: a bioinformatics study

2019 ◽  
Vol 39 (5) ◽  
Author(s):  
Ming Zhong ◽  
Yilong Wu ◽  
Weijie Ou ◽  
Linjing Huang ◽  
Liyong Yang
Keyword(s):  
Type 2 Diabetes ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Atp Citrate Lyase ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Search Tool

Abstract Aims: To identify the key differentially expressed genes (DEGs) in islet and investigate their potential pathway in the molecular process of type 2 diabetes. Methods: Gene Expression Omnibus (GEO) datasets (GSE20966, GSE25724, GSE38642) of type 2 diabetes patients and normal controls were downloaded from GEO database. DEGs were further assessed by enrichment analysis based on the Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8. Then, by using Search Tool for the Retrieval Interacting Genes (STRING) 10.0 and gene set enrichment analysis (GSEA), we identified hub gene and associated pathway. At last, we performed quantitative real-time PCR (qPCR) to validate the expression of hub gene. Results: Forty-five DEGs were co-expressed in the three datasets, most of which were down-regulated. DEGs are mostly involved in cell pathway, response to hormone and binding. In protein–protein interaction (PPI) network, we identified ATP-citrate lyase (ACLY) as hub gene. GSEA analysis suggests low expression of ACLY is enriched in glycine serine and threonine metabolism, drug metabolism cytochrome P450 (CYP) and NOD-like receptor (NLR) signaling pathway. qPCR showed the same expression trend of hub gene ACLY as in our bioinformatics analysis. Conclusion: Bioinformatics analysis revealed that ACLY and the pathways involved are possible target in the molecular mechanism of type 2 diabetes.

scholarly journals A Network Pharmacology-based Analysis of the Molecular Mechanism of Tanreqing in the Treatment of the Coronavirus Disease 2019 (COVID-19)

2020 ◽  
Author(s):  
Liuliu Yang ◽  
Minyong Wen ◽  
Wenjiang Zheng ◽  
Xiaohong Liu ◽  
yong wang
Keyword(s):  
Signaling Pathway ◽  
Molecular Mechanism ◽  
Chemical Constituents ◽  
Hypoxia Inducible Factor ◽  
Enrichment Analysis ◽  
Clinical Treatment ◽  
Laboratory Research ◽  
Network Pharmacology ◽  
Receptor Interaction ◽  
Protein Protein Interaction

Abstract Background: This paper discusses the molecular mechanism of Tanreqing (TRQ) in the treatment of the coronavirus disease 2019 (COVID-19) using the network pharmacology approach. Our study provides new ideas on the laboratory research and clinical treatment of the disease. Method: Information on the chemical constituents of TRQ and the genes targeted by the disease was collected. The common gene targets of the drug and the disease were input into the Search Tool for the Retrieval of Interacting Genes/Proteins（STRING）database to understand the interaction among target proteins. The protein–protein interaction (PPI) network and a network of the chemical constituents of TRQ and their targets were constructed using Cytoscape. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the R program and other relevant software packages. Results: Twenty-eight active chemical constituents and 365 gene targets were identified for TRQ. Out of these genes, 113 were also found to be involved in the pathogenesis of the disease. Enrichment analysis revealed the therapeutic role that TRQ could have played in the treatment of COVID-19, via the regulation of important pathways such as the renin–angiotension system, neuroactive ligand–receptor interaction, phospholipase D (PLD) signaling pathway, calcium signaling pathway, and the hypoxia-inducible factor 1 (HIF-1) signaling pathway. Conclusions: This study attempts to predict the molecular mechanism of TRQ in the treatment of COVID-19, and suggests TRQ intervention through multiple targets and pathways in processes including inflammatory response, immune regulation, and apoptosis during the treatment of the disease. This study indicates the potential rational application of TRQ in the clinical treatment of COVID-19.

scholarly journals Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

2021 ◽  
Author(s):  
Congcong Wang ◽  
Jianping Guo ◽  
Xiaoyang Zhao ◽  
Jia Jia ◽  
Wenting Xu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Analysis Tool ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Analysis ◽  
Prognostic Assessment ◽  
Scheme Selection

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

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