scholarly journals EFNB1 Acts as a Novel Prognosis Marker in Glioblastoma through Bioinformatics Methods and Experimental Validation

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yaohong Shi ◽  
Yuanyuan Sun ◽  
Hongyan Cheng ◽  
Chen Wang
Keyword(s):  
Survival Analysis ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Poor Overall Survival ◽  
Online Tool ◽  
Interactive Analysis ◽  
Kaplan Meier ◽  
Maximum Cluster ◽  
And Migration

Purpose. Ephrin B1 (EFNB1), the Eph-associated receptor tyrosine kinase ligand, is suggested to have an important function in neurodevelopment. However, its contribution to glioblastoma multiforme (GBM) remains uncertain. This study aimed to determine the prognostic power and immune implication of EFNB1 in GBM. Methods. We first identified differentially coexpressed genes within GBM relative to noncarcinoma samples from GEO and TCGA databases by WGCNA. The STRING online database and the maximum cluster centrality (MCC) algorithm in Cytoscape software were used to design for predicting protein-protein interactions (PPI) and calculating pivot nodes, respectively. The expression of hub genes in cancer and noncancer tissues was verified by an online tool gene expression profile interactive analysis (GEPIA). Thereafter, the TISIDB online tool with Cox correlation regression method was employed to screen for immunomodulators associated with EFNB1 and to model the risk associated with immunomodulators. Results. Altogether 201 differentially expressed genes (DEGs) were discovered. After that, 10 hub genes (CALB2, EFNB1, ENO2, EPHB4, NES, OBSCN, RAB9B, RPL23A, STMN2, and THY1) were incorporated to construct the PPI network. As revealed by survival analysis, EFNB1 upregulation predicted poor overall survival (OS) for GBM cases. Furthermore, we developed a prognostic risk signature according to the EFNB1-associated immunomodulators. Kaplan–Meier survival analysis and receiver operating characteristic method were adopted for analysis, which revealed that our signature showed favorable accuracy of prognosis prediction. Finally, EFNB1 inhibition was found to block cell proliferation and migration in GBM cells. Conclusion. The above results indicate that EFNB1 participates in cancer immunity and progression, which is the candidate biomarker for GBM.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
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.

scholarly journals Identification of the hub genes RUNX2 and FN1 in gastric cancer

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 403-412
Author(s):  
Chao Han ◽  
Lei Jin ◽  
Xuemei Ma ◽  
Qin Hao ◽  
Huajun Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Protein Interactions ◽  
Lentiviral Vector ◽  
Gene Expression Omnibus ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Poorly Differentiated ◽  
Key Genes ◽  
Kaplan Meier Plotter

AbstractBackgroundThis study identified key genes in gastric cancer (GC) based on the mRNA microarray GSE19826 from the Gene Expression Omnibus (GEO) database and preliminarily explored the relationships among the key genes.MethodsDifferentially expressed genes (DEGs) were obtained using the GEO2R tool. The functions and pathway enrichment of the DEGs were analyzed using the Enrichr database. Protein–protein interactions (PPIs) were established by STRING. A lentiviral vector was constructed to silence RUNX2 expression in MGC-803 cells. The expression levels of RUNX2 and FN1 were measured. The influences of RUNX2 and FN1 on overall survival (OS) were determined using the Kaplan–Meier plotter online tool.ResultsIn total, 69 upregulated and 65 downregulated genes were identified. Based on the PPI network of the DEGs, 20 genes were considered hub genes. RUNX2 silencing significantly downregulated the FN1 expression in MGC-803 cells. High expression of RUNX2 and low expression of FN1 were associated with long survival time in diffuse, poorly differentiated, and lymph node-positive GC.ConclusionHigh RUNX2 and FN1 expression were associated with poor OS in patients with GC. RUNX2 can negatively regulate the secretion of FN1, and both genes may serve as promising targets for GC treatment.

scholarly journals A85 THE CLINICAL IMPACT OF COVID-19 DELAYS ON PLASTIC BILIARY STENT REMOVAL IN NOVA SCOTIA

2021 ◽  
Vol 4 (Supplement_1) ◽  
pp. 54-56
Author(s):  
D R Lim ◽  
M Tsai ◽  
S E Gruchy ◽  
J Jones ◽  
G Williams ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Nova Scotia ◽  
Stent Placement ◽  
Biliary Stent ◽  
Biliary Stents ◽  
Stent Occlusion ◽  
Stent Removal ◽  
Kaplan Meier ◽  
And Migration

Abstract Background The COVID-2019 pandemic continues to restrict access to endoscopy, resulting in delays or cancellation of non-urgent endoscopic procedures. A delay in the removal or exchange of plastic biliary stents may lead to stent occlusion with consensus recommendation of stent removal or exchange at three-month intervals [1–4]. We postulated that delayed plastic biliary stent removal (DPBSR) would increase complication rates. Aims We aim to report our single-centre experience with complications arising from DPBSR. Methods This was a retrospective, single-center, observational cohort study. All subjects who had ERCP-guided plastic biliary stent placement in Halifax, Nova Scotia between Dec 2019 and June 2020 were included in the study. DPBSR was defined as stent removal >=90 days from insertion. Four endpoints were assigned to patients: 1. Stent removed endoscopically, 2. Died with stent in-situ (measured from stent placement to documented date of death/last clinical encounter before death), 3. Pending removal (subjects clinically well, no liver enzyme elevation, not expired, endpoint 1 Nov 2020), and 4. Complication requiring urgent reintervention. Kaplan-Meier survival analysis was used to represent duration of stent patency (Fig.1). Results 102 (47.2%) had plastic biliary stents placed between 2/12/2019 and 29/6/2020. 49 (48%) were female, and the median age was 68 (R 16–91). Median follow-up was 167.5 days, 60 (58.8%) subjects had stent removal, 12 (11.8%) died before replacement, 21 (20.6%) were awaiting stent removal with no complications (median 230d, R 30–332), 9 (8.8%) had complications requiring urgent ERCP. Based on death reports, no deaths were related to stent-related complications. 72(70.6%) of patients had stents in-situ for >= 90 days. In this population, median time to removal was 211.5d (R 91-441d). 3 (4.2%) subjects had stent-related complications requiring urgent ERCP, mean time to complication was 218.3d (R 94–441). Stent removal >=90 days was not associated with complications such as occlusion, cholangitis, and migration (p=1.0). Days of stent in-situ was not associated with occlusion, cholangitis, and migration (p=0.57). Sex (p=0.275), cholecystectomy (p=1.0), cholangiocarcinoma (p=1.0), cholangitis (p=0.68) or pancreatitis (p=1.0) six weeks prior to ERCP, benign vs. malignant etiology (p=1.0) were not significantly associated with stent-related complications. Conclusions Plastic biliary stent longevity may have been previously underestimated. The findings of this study agree with CAG framework recommendations [5] that stent removal be prioritized as elective (P3). Limitations include small sample size that could affect Kaplan-Meier survival analysis. Despite prolonged indwelling stent time as a result of COVID-19, we did not observe an increased incidence of stent occlusion or other complications. Funding Agencies None

scholarly journals Shared Blood Transcriptomic Signatures between Alzheimer’s Disease and Diabetes Mellitus

Biomedicines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Taesic Lee ◽  
Hyunju Lee
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Expression Patterns ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Gene Modules ◽  
Gene Regulatory ◽  
The Brain

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

Protein-Protein Interactions in the Tetraspanin Web

Physiology ◽  
2005 ◽  
Vol 20 (4) ◽  
pp. 218-224 ◽  
Author(s):  
Shoshana Levy ◽  
Tsipi Shoham
Keyword(s):  
Signal Transduction ◽  
Cell Proliferation ◽  
Protein Interactions ◽  
Membrane Microdomains ◽  
Protein Protein Interactions ◽  
Host Parasite Interactions ◽  
Evolutionarily Conserved ◽  
And Migration ◽  
Host Parasite ◽  
Partner Proteins

Tetraspanins are evolutionarily conserved membrane proteins that tend to associate laterally with one another and to cluster dynamically with numerous partner proteins in membrane microdomains. Consequently, members of this family are involved in the coordination of intracellular and intercellular processes, including signal transduction; cell proliferation, adhesion, and migration; cell fusion; and host-parasite interactions.

scholarly journals Bioinformatics analysis of potential core genes for glioblastoma

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.

scholarly journals RAB11FIP1: An Indicator for Tumor Immune Microenvironment and Prognosis of Lung Adenocarcinoma from a Comprehensive Analysis of Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenyi Zhang ◽  
Ting Chen ◽  
Jun Liu ◽  
Shali Yu ◽  
Lei Liu ◽  
...  
Keyword(s):  
T Cells ◽  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Protein Interactions ◽  
Potential Mechanism ◽  
Protein Protein Interactions ◽  
Immune Markers ◽  
Immune Microenvironment ◽  
Kaplan Meier ◽  
Tumor Immune Microenvironment

Lung adenocarcinoma (LUAD) was the first one all over the world. RAB11FIP1 was found to be expressed differently in a critical way among different cancers. However, the prognostic value and immune infiltration of RAB11FIP1 expression in LUAD are unclear. In this study, the expression of RAB11FIP1 in LUAD was investigated in the Oncomine, TCGA, GEO, and UALCAN databases. Kaplan-Meier analysis was chosen to compare the association between RAB11FIP1 expression and overall survival (OS) in LUAD patients. The dataset of TCGA was used to analyze the pertinence between RAB11FIP1 and clinicpathological factors. GO, KEGG, and network analysis of protein-protein interactions (PPI) were conducted to investigate the potential mechanism of RAB11FIP1. In the end, the relevance of RAB11FIP1 to cancer-immune infiltrates was investigated. RAB11FIP1 was found to be down-regulated by tumors compared with adjacent normal tissue in multiple LUAD cohorts. RAB11FIP1 is an independent prognostic factor in lung adenocarcinoma. There was a high correlation between low RAB11FIP1 in tumors and worse OS in LUAD. Functional network analysis suggested that RAB11FIP1 was associated with multiple pathways. Besides, the expression of RAB11FIP1 was closely related to the infiltration levels of B cell, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. RAB11FIP1 expression in LUAD occurred with a variety of immune markers. Our findings suggest that RAB11FIP1 is related to prognosis and immune infiltrates in LUAD.

scholarly journals Screening and Identification of Key Biomarkers in Breast Cancer with Brain Metastasis: Evidence from Bioinformatic Analysis

2020 ◽  
Author(s):  
tao ming Shao ◽  
zhi yang Hu ◽  
wen wei Li ◽  
long yun Pan
Keyword(s):  
Breast Cancer ◽  
Protein Interactions ◽  
Poor Prognosis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Clinical Prognosis ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Purpose. Breast cancer (BC) has a poor prognosis when brain metastases (BM) occur, and the treatment effect is limited. In this study, we aim to identify representative candidate biomarkers for clinical prognosis of patients with BM and explore the mechanisms underlying the progression of BC.Methods. Herein, we examined the Microarray datasets (GSE125989) obtained from the Gene Expression Omnibus database to find the target genes in BC patients with BM. We employed the GEO2R tool to filter the differentially expressed genes (DEGs) that participate in primary BC and BC with BM. Subsequently, using the DAVID tool, we conducted an enrichment analysis with the screened DEGs based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional annotation. The STRING database was employed to analyze the protein-protein interactions of the DEGs and visualized using Cytoscape software. Lastly, the Kaplan-Meier plotter database was employed to determine the prognostic potential of hub genes in BC.Results. We screened out 311 upregulated DEGs and 104 downregulated DEGs. The enrichment analyses revealed that all the DEGs were` enriched in the biological process of extracellular matrix organization, cell adhesion, proteolysis, collagen catabolic process and immune response. The significant enrichment pathways were focal adhesion, protein absorption and digestion, ECM-receptor interaction, PI3K-Akt signalling pathway, and Pathways in cancer. The top ten hub nodes screened out included FN1, VEGFA, COL1A1, MMP2, COL3A1, COL1A2, POSTN, DCN, BGN and LOX. The Kaplan-Meier plotter results showed that the three hub genes (FN1, VEGFA and DCN) are candidate biomarkers for clinical prognosis of patients with BM.Conclusion. we identified seven genes related to poor prognosis in BCBM. FN1, VEGFA and DCN can be considered as potential prognostic markers for BCBM. Meantime, COL1A1, POSTN, BGN and LOX may be linked to the distant transformation of BC.

scholarly journals Integrative analysis identified common and unique molecular signatures in hepatobiliary cancers

2021 ◽  
Author(s):  
Nabanita Roy ◽  
Ria Lodh ◽  
Anupam Sarma ◽  
Dhruba Kumar Bhattacharyya ◽  
Pankaj Barah
Keyword(s):  
Protein Interactions ◽  
Metabolic Pathways ◽  
Expression Patterns ◽  
Molecular Signature ◽  
Cancer Type ◽  
Molecular Signatures ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Cellular Processes ◽  
Differential Gene

Hepatobiliary cancers (HBCs) are the most aggressive and sixth most diagnosed cancers globally. Biomarkers for timely diagnosis and targeted therapy in HBCs are still limited. Considering the gap, our objective is to identify unique and overlapping molecular signatures associated with HBCs. We analyzed publicly available transcriptomic datasets on Gallbladder cancer (GBC), Hepatocellular carcinoma (HCC), and Intrahepatic cholangiocarcinoma (ICC) to identify potential biomarkers using integrative systems approaches. An effective Common and Unique Molecular Signature Identification (CUMSI) approach has been employed, which contains analysis of differential gene expression (DEG), gene co-expression networks (GCN), and protein-protein interactions (PPIs) networks. Functional analysis of the DEGs unique for GBC, HCC, and ICC indicated that GBC is associated with cellular processes, HCC is associated with immune signaling pathways, and ICC is associated with lipid metabolic pathways. Our findings shows that the hub genes and pathways identified for each individual cancer type of the HBS are related with the primary function of each organ and each cancer exhibit unique expression patterns despite being part of the same organ system.

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