scholarly journals Identification of circular RNAs as novel biomarkers and potentially functional competing endogenous RNA network for myelodysplastic syndrome patients

2021 ◽  
Author(s):  
Wan‐ling Wu ◽  
Shuang Li ◽  
Guang‐jie Zhao ◽  
Nian‐yi Li ◽  
Xiao‐Qin Wang
Keyword(s):  
Myelodysplastic Syndrome ◽  
Circular Rnas ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network ◽  
Novel Biomarkers

Differentially expressed circular RNAs and the competing endogenous RNA network associated with preeclampsia

Placenta ◽  
2021 ◽  
Vol 103 ◽  
pp. 232-241
Author(s):  
Bo Ma ◽  
Huanqiang Zhao ◽  
Lili Gong ◽  
Xirong Xiao ◽  
Qiongjie Zhou ◽  
...  
Keyword(s):  
Differentially Expressed ◽  
Circular Rnas ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network

Identification of novel biomarkers for intracerebral hemorrhage via long noncoding RNA-associated competing endogenous RNA network

2021 ◽  
Author(s):  
Chunyu Yang ◽  
Jiao Wu ◽  
Xi Lu ◽  
Shuang Xiong ◽  
Xiaoxue Xu
Keyword(s):  
Intracerebral Hemorrhage ◽  
Cause Of Death ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network ◽  
Novel Biomarkers ◽  
Non Coding Rnas

Intracerebral hemorrhage (ICH) is a leading cause of death and disability worldwide. This study aimed to examine the involvement of long non-coding RNAs (lncRNAs), a group of non-coding transcripts, in...

scholarly journals AFCMEasyModel: An Easy Interface for Modeling Competing Endogenous RNA Networks using ODEs

2017 ◽  
Author(s):  
Mohammad M. Tarek
Keyword(s):  
Gene Expression ◽  
Public Access ◽  
Regulatory Function ◽  
Circular Rnas ◽  
Messenger Rnas ◽  
Competing Endogenous Rna ◽  
Shiny App ◽  
Competing Endogenous Rna Network ◽  
Non Coding Rnas ◽  
User Friendly

AbstractCompeting endogenous RNA networks have been considered to be important regulators of genetic data expression. Circular RNAs and microRNAs interact to form a circular sponge that have been shown to regulate messenger RNAs and hence regulating gene expression. The kinetics by which these non-coding RNAs interact together affecting gene expression are crucial to understand the mechanism of their regulatory function. Herein, we developed AFCMEasyModel as a user-friendly shiny app that enables users to modify regulation parameters of a competing endogenous RNA network based on interaction between circular RNAs and microRNAs in the simulation environment to form a sponge complex. The App provides the source-code for more customized models and allow users to download simulation plots for supplementation of their publications.The App was made available for public-access at: https://mohammadtarek.shinyapps.io/afcmeasymodel/

scholarly journals mRNAsi Index-based Integrative Analysis for the Identification of New Prognostic Models for Ovarian Cancer

2020 ◽  
Author(s):  
Xue Pan ◽  
Xiaoxin Ma
Keyword(s):  
Ovarian Cancer ◽  
Clinical Characteristics ◽  
Malignant Tumors ◽  
Lymphatic Invasion ◽  
Prognostic Models ◽  
Overall Survival Rate ◽  
Female Reproductive System ◽  
Competing Endogenous Rna ◽  
Competing Endogenous Rna Network ◽  
Grade 3

Abstract Ovarian cancer (OC) has the highest mortality rate among all female reproductive system malignant tumors worldwide. In this study, we aimed to investigate OC from several perspectives by using machine learning. Our results showed that the mRNA expression-stemness index (mRNAsi) is closely related to clinical characteristics of OC patients, as OC patients with venous or lymphatic invasion had higher mRNAsi score compared to patients with no invasion. Furhter grade 3/4 patient group had higher mRNAsi scores compared to the grade1/2 group. We also found that mRNAsi is closely related to immune infiltration in OC. We also built a competing endogenous RNA network, which contained 4 miRNAs, 5 lncRNAs, and 1 mRNA, by using Cytoscape based on the differentially expressed genes of the high- and low-mRNAsi groups. Through Lassio regression, we also established a model including 7 lncRNAs and 2miRNAs, which could effectively categorize OC patients into two groups based on the median risk score. We then developed a nomogram model which could effectively forecast the overall survival rate of OC for 1-, 3-, and 5-year period. The models assessed in this study showed potential for clinical application in treatment decisions for OC.

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