Improved method for prioritization of disease associated lncRNAs based on ceRNA theory and functional genomics data

Wang, Peng; Guo, Qiuyan; Gao, Yue; Zhi, Hui; Zhang, Yan; Liu, Yue; Zhang, Jizhou; Ming, Yue; Guo, Maoni; Ning, Shangwei; Zhang, Guangmei; Li, Xia

doi:10.18632/oncotarget.13964

Cited by 20 publications

(13 citation statements)

References 75 publications

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“…Our hypothesis was that ceRNA pairs should satisfy two criteria: one is the pair should have high miRNA regulation similarity. The other is that the pair should have strong co-expression [ 18 , 34 , 35 ]. Firstly, a hypergeometric test was used to evaluate the significance of shared miRNAs for each possible ceRNA pair.…”

Section: Methodsmentioning

confidence: 99%

Identification of bladder cancer prognostic biomarkers using an ageing gene-related competitive endogenous RNA network

Wang¹,

Chen²,

Yang³

et al. 2017

Oncotarget

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Competitive endogenous RNAs (ceRNAs) are a newly proposed RNA interaction mechanism that has been associated with the initiation and progression of various cancers. In this study, we constructed an ageing gene related ceRNA network (AgeingCeNet) in bladder cancer. Network analysis revealed that ageing gene ceRNAs have a larger degree and closeness centrality than ageing genes themselves. Notably, the difference of betweenness centrality of ageing genes and their ceRNAs is not significant, suggesting that the ceRNAs of ageing genes and ageing genes themselves both play important communication roles in AgeingCeNet. KEGG pathway enrichment analysis for genes in AgeingCeNet revealed that AgeingCeNet genes are enriched in cancer pathways and several cancer related singaling pathways. We also identified 37 core modules from AgeingCeNet using CFinder software. Next, we identified 2 potential prognostic modules, named K11M14 and K13M4, whose prognostic ability is better than that of age and gender. Finally, we identified microRNAs (miRNAs) regulating the two modules, which include miR-15b-5p, miR-195-5p, miR-30 family members, and several other cancer-related miRNAs. Our study demonstrated that constructing an ageing gene related ceRNA network is a feasible strategy to explore the mechanism of initiation and progression of bladder cancer, which might benefit the treatment of this disease.

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Section: Methodsmentioning

confidence: 99%

Identification of bladder cancer prognostic biomarkers using an ageing gene-related competitive endogenous RNA network

Wang¹,

Chen²,

Yang³

et al. 2017

Oncotarget

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“…The year 2017 witnessed a notable increase in the publication of studies identifying genome-wide ceRNA networks in AD based on the use of distinct disease models ( Cai et al, 2017 ; Wang L.K. et al, 2017 ; Wang P. et al, 2017 ; Zhang et al, 2017 ); this contributed substantially toward a systematic and comprehensive elucidation of ceRNA regulation in AD. Cai et al (2017) identified one of the first AD-associated lncRNA–miRNA–mRNA networks based on the APP/PSEN1 mouse model, which is a widely used FAD model; whole-transcriptome sequencing and miRNA-seq of the APP/PSEN1 and wild-type mouse cortex were leveraged to identify a ceRNA network that includes 4 hub lncRNAs, 5 miRNAs, and 1,082 mRNA targets.…”

Section: Competing Endogenous Rna In Neurodegenerative Disordersmentioning

confidence: 99%

“…DisLncPri, a disease-lncRNA prioritization method, was created to identify unknown disease-lncRNA associations based on a ceRNA theory. Among the top 20 AD-associated lncRNAs, 3 previously unrecognized lncRNAs were identified: MEG3, PVT1, and LINC01616 ( Wang P. et al, 2017 ). All these efforts devoted toward discovering various types of ceRNA regulation in AD not only enhance our understanding of newly identified aspects of ceRNA regulatory mechanisms, but also provide new insights to the complexity of AD pathogenesis.…”

Section: Competing Endogenous Rna In Neurodegenerative Disordersmentioning

confidence: 99%

Competing Endogenous RNA Regulations in Neurodegenerative Disorders: Current Challenges and Emerging Insights

Cai

Wan

2018

Front. Mol. Neurosci.

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The past decade has witnessed exciting breakthroughs that have contributed to the richness and complexity of a burgeoning modern RNA world, and one particular breakthrough—the competing endogenous RNA (ceRNA) hypothesis—has been described as the “Rosetta Stone” for decoding the RNA language used in regulating RNA crosstalk and modulating biological functions. The proposed far-reaching mechanism unites diverse RNA species and provides new insights into previously unrecognized RNA–RNA interactions and RNA regulatory networks that perhaps determine gene expression in an organized, hierarchical manner. The recently uncovered ceRNA regulatory loops and networks have emphasized the power of ceRNA regulation in a wide range of developmental stages and pathological contexts, such as in tumorigenesis and neurodegenerative disorders. Although the ceRNA hypothesis drastically enhanced our understanding of RNA biology, shortly after the hypothesis was proposed, disputes arose in relation mainly to minor discrepancies in the reported effects of ceRNA regulation under physiological conditions, and this resulted in ceRNA regulation becoming an extensively studied and fast-growing research field. Here, we focus on the evidence supporting ceRNA regulation in neurodegenerative disorders and address three critical points related to the ceRNA regulatory mechanism: the microRNA (miRNA) and ceRNA hierarchies in cross-regulations; the balance between destabilization and stable binding in ceRNA–miRNA interactions; and the true extent to which ceRNA regulatory mechanisms are involved in both health and disease, and the experimental shortcomings in current ceRNA studies.

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“…Moreover, Cheng et al developed information flow modelling based LDA prediction model (IntNetLncSim) by combining lncRNA-associated transcriptional information with post-transcriptional information [29]. Wang et al developed a competing endogenous RNAs (ceRNAs) based LDA prediction model (DisLncPri) by mapping lncRNAs to their functional genomics context [30]. Fu et al proposed a matrix factorization based LDA prediction model (MFLDA) by decomposing multiple data matrices into low-rank matrices to identify their interior structure [31].…”

Section: Introductionmentioning

confidence: 99%

A random forest based computational model for predicting novel lncRNA-disease associations

Yao

Zhan

et al. 2020

BMC Bioinformatics

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Background: Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results: To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions: Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

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Improved method for prioritization of disease associated lncRNAs based on ceRNA theory and functional genomics data

Cited by 20 publications

References 75 publications

Identification of bladder cancer prognostic biomarkers using an ageing gene-related competitive endogenous RNA network

Identification of bladder cancer prognostic biomarkers using an ageing gene-related competitive endogenous RNA network

Competing Endogenous RNA Regulations in Neurodegenerative Disorders: Current Challenges and Emerging Insights

A random forest based computational model for predicting novel lncRNA-disease associations

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