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.

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

scholarly journals Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions

Pharmaceutics ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2117
Author(s):  
Vlad Groza ◽  
Mihai Udrescu ◽  
Alexandru Bozdog ◽  
Lucreţia Udrescu
Keyword(s):  
Interaction Network ◽  
Drug Repurposing ◽  
Gene Interaction ◽  
New Drugs ◽  
Interaction Data ◽  
Gene Interaction Network ◽  
Drug Databases ◽  
Drug Similarity

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list.

Incorporating K-mers Highly Correlated to Epigenetic Modifications for Bayesian Inference of Gene Interactions

2020 ◽  
Vol 15 ◽  
Author(s):  
Dariush Salimi ◽  
Ali Moeini
Keyword(s):  
Bayesian Inference ◽  
Biological Networks ◽  
Interaction Network ◽  
Gene Interaction ◽  
Epigenetic Modifications ◽  
Physical Interaction ◽  
Gene Interaction Network ◽  
Human Genes ◽  
Highly Correlated ◽  
Specific Pathway

Objective: A gene interaction network, along with its related biological features, has an important role in computational biology. Bayesian network, as an efficient model, based on probabilistic concepts is able to exploit known and novel biological casual relationships between genes. Success of Bayesian networks in predicting the relationships greatly depends on selecting priors Methods: K-mers have been applied as the prominent features to uncover similarity between genes in a specific pathway, suggesting that this feature can be applied to study genes dependencies. In this study, we propose k-mer (4,5 and 6-mers) highly correlated with epigenetic modifications, including 17 modifications, as a new prior for Bayesian inference in gene interaction network Result: Employing this model on a network of 23 human genes and on a network based on 27 genes related to yeast resulted in F-measure improvements in different biological networks Conclusion: The improvements in the best case are 12%, 36% and 10% in pathway, co-expression, and physical interaction, respectively.

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 In vivo reconstitution finds multivalent RNA-RNA interactions as drivers of mesh-like condensates

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Weirui Ma ◽  
Gang Zheng ◽  
Wei Xie ◽  
Christine Mayr
Keyword(s):  
Surface Area ◽  
Protein Trafficking ◽  
Rna Binding ◽  
Interaction Network ◽  
Large Surface Area ◽  
High Propensity ◽  
Membrane Protein Trafficking ◽  
Protein Components

Liquid-like condensates have been thought to be sphere-like. Recently, various condensates with filamentous morphology have been observed in cells. One such condensate is the TIS granule network that shares a large surface area with the rough endoplasmic reticulum and is important for membrane protein trafficking. It has been unclear how condensates with mesh-like shapes, but dynamic protein components are formed. In vitro and in vivo reconstitution experiments revealed that the minimal components are a multivalent RNA-binding protein that concentrates RNAs that are able to form extensive intermolecular mRNA-mRNA interactions. mRNAs with large unstructured regions have a high propensity to form a pervasive intermolecular interaction network that acts as condensate skeleton. The underlying RNA matrix prevents full fusion of spherical liquid-like condensates, thus driving the formation of irregularly shaped membraneless organelles. The resulting large surface area may promote interactions at the condensate surface and at the interface with other organelles.

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.

scholarly journals Bioinformatic Analysis of Neuroimmune Mechanism of Neuropathic Pain

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hao Yu ◽  
Yang Liu ◽  
Chao Li ◽  
Jianhao Wang ◽  
Bo Yu ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Inflammatory Responses ◽  
Interaction Network ◽  
Gene Interaction ◽  
Bioinformatic Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Pathway Enrichment ◽  
Protein Protein Interaction

Background. Neuropathic pain (NP) is a devastating complication following nerve injury, and it can be alleviated by regulating neuroimmune direction. We aimed to explore the neuroimmune mechanism and identify some new diagnostic or therapeutic targets for NP treatment via bioinformatic analysis. Methods. The microarray GSE18803 was downloaded and analyzed using R. The Venn diagram was drawn to find neuroimmune-related differentially expressed genes (DEGs) in neuropathic pain. Gene Ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network were used to analyze DEGs, respectively. Besides, the identified hub genes were submitted to the DGIdb database to find relevant therapeutic drugs. Results. A total of 91 neuroimmune-related DEGs were identified. The results of GO and pathway enrichment analyses were closely related to immune and inflammatory responses. PPI analysis showed two important modules and 8 hub genes: PTPRC, CD68, CTSS, RAC2, LAPTM5, FCGR3A, CD53, and HCK. The drug-hub gene interaction network was constructed by Cytoscape, and it included 24 candidate drugs and 3 hub genes. Conclusion. The present study helps us better understand the neuroimmune mechanism of neuropathic pain and provides some novel insights on NP treatment, such as modulation of microglia polarization and targeting bone resorption. Besides, CD68, CTSS, LAPTM5, FCGR3A, and CD53 may be used as early diagnostic biomarkers and the gene HCK can be a therapeutic target.

scholarly journals A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

10.1186/gm404 ◽  
2012 ◽  
Vol 4 (12) ◽  
Author(s):  
Raymond J Louie ◽  
Jingyu Guo ◽  
John W Rodgers ◽  
Rick White ◽  
Najaf A Shah ◽  
...  
Keyword(s):  
Interaction Network ◽  
Gene Interaction ◽  
Gene Interaction Network ◽  
Protein Biogenesis
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