scholarly journals Gene-gene interaction network analysis of ovarian cancer using TCGA data

2013 ◽  
Vol 6 (1) ◽  
pp. 88 ◽  
Author(s):  
Huanchun Ying ◽  
Jing Lv ◽  
Tianshu Ying ◽  
Shanshan Jin ◽  
Jingru Shao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network

scholarly journals Retraction Note: Gene-gene interaction network analysis of ovarian cancer using TCGA data

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Huanchun Ying ◽  
Jing Lv ◽  
Tianshu Ying ◽  
Shanshan Jin ◽  
Jingru Shao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network

Elucidating the multi-drug resistance mechanism of Enterococcus faecalis V583: A gene interaction network analysis

Gene ◽  
2020 ◽  
Vol 748 ◽  
pp. 144704 ◽  
Author(s):  
Aniket Naha ◽  
Sravan Kumar Miryala ◽  
Reetika Debroy ◽  
Sudha Ramaiah ◽  
Anand Anbarasu
Keyword(s):  
Drug Resistance ◽  
Network Analysis ◽  
Enterococcus Faecalis ◽  
Resistance Mechanism ◽  
Interaction Network ◽  
Gene Interaction ◽  
Multi Drug Resistance ◽  
Gene Interaction Network

scholarly journals Comprehensive Analysis of Prognostic Value of MEX3A and Its Relationship with Immune Infiltrates in Ovarian Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Panpan Zhang ◽  
Tong Su ◽  
Shu Zhang
Keyword(s):  
Ovarian Cancer ◽  
Prognostic Value ◽  
Rna Binding ◽  
Interaction Network ◽  
Gene Interaction ◽  
Physical Interaction ◽  
Biological Functions ◽  
Gene Interaction Network ◽  
Immune Infiltration

MEX3A is a critical RNA-binding ubiquitin ligase that is upregulated in various types of cancer. However, the correlations of MEX3A with prognosis and its molecular mechanism in ovarian cancer (OC) remain unclear. The expression level, prognostic values, and the genetic variations of MEX3A were analyzed via Gene Expression Profiling Interactive Analysis (GEPIA) Oncomine, Kaplan–Meier plotter, and cBioPortal. We used the LinkedOmics database to investigate the functions of MEX3A coexpressed genes and performed visualizing gene interaction network analysis on the GeneMANIA website. The correlations between MEX3A and cancer immune infiltration were analyzed by the Tumor Immune Estimation Resource (TIMER) site and the TISIDB database. Furthermore, in vitro analysis was performed to evaluate the biological functions of MEX3A in OC cells. Our study showed that the expression of the MEX3A in OC was higher than in normal tissues; it had the greatest prognostic value in OC, and strong physical interaction with PABPC1, LAMTOR2, KHDRBS2, and IGF2BP2, which indicated the association between MEX3A and immune infiltration. We also found that MEX3A was negatively related to infiltrating levels of several types of immune cells, including macrophages, neutrophils, dendritic cells (DCs), B cells, and CD8+ T cells. Additionally, in vitro experiments demonstrated that MEX3A promotes proliferation and migration in OC cells. Taken together, MEX3A might influence the biological functions of OC cells by regulating the immune infiltration in the microenvironment as a prognostic biomarker and a potential therapeutic target.

Identification of potential genomic biomarkers for Parkinson’s disease using data pooling of gene expression microarrays

2021 ◽  
Author(s):  
Zhijian Lin ◽  
Lishu Zhou ◽  
Yaosha Li ◽  
Suni Liu ◽  
Qizhi Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Analysis ◽  
Interaction Network ◽  
Gene Interaction ◽  
Microarray Gene Expression Data ◽  
Gene Interaction Network ◽  
Microarray Gene Expression ◽  
Chemokine Signaling

Aim: In this study, we aimed to identify potential diagnostic biomarkers Parkinson’s disease (PD) by exploring microarray gene expression data of PD patients. Materials & methods: Differentially expressed genes associated with PD were screened from the GSE99039 dataset using weighted gene co-expression network analysis, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, gene–gene interaction network analysis and receiver operator characteristics analysis. Results: We identified two PD-associated modules, in which genes from the chemokine signaling pathway were primarily enriched. In particular, CS, PRKCD, RHOG and VAMP2 directly interacted with known PD-associated genes and showed higher expression in the PD samples, and may thus be potential biomarkers in PD diagnosis. Conclusion: A DFG-analysis identified a four-gene panel ( CS, PRKCD, RHOG, VAMP2) as a potential diagnostic predictor to diagnose PD.

Gene interaction network analysis suggests differences between high and low doses of acetaminophen

2006 ◽  
Vol 215 (3) ◽  
pp. 306-316 ◽  
Author(s):  
Hiroyoshi Toyoshiba ◽  
Hideko Sone ◽  
Takeharu Yamanaka ◽  
Frederick M. Parham ◽  
Richard D. Irwin ◽  
...  
Keyword(s):  
Network Analysis ◽  
Interaction Network ◽  
Gene Interaction ◽  
Low Doses ◽  
Gene Interaction Network

scholarly journals Community Detection in Large-Scale Bipartite Biological Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Genís Calderer ◽  
Marieke L. Kuijjer
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Interaction Network ◽  
Gene Interaction ◽  
Community Structures ◽  
Gene Interaction Network ◽  
Structure Detection ◽  
Wide Range ◽  
Disease Associations ◽  
Or Gene

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

Sign in / Sign up

Export Citation Format

Share Document