Dataset Information

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

ABSTRACT: Motivation:Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of interactions between diseases and LMPairs at disease level. Results:It is well known that genes with similar functions are often associated with similar diseases. In this article, a novel model named PADLMP for predicting associations between diseases and LMPairs is proposed. In this model, a Disease-LncRNA-miRNA (DLM) tripartite network was designed firstly by integrating the lncRNA-disease association network and miRNA-disease association network; then we constructed the disease-LMPairs bipartite association network based on the DLM network and lncRNA-miRNA association network; finally, we predicted potential associations between diseases and LMPairs based on the newly constructed disease-LMPair network. Simulation results show that PADLMP can achieve AUCs of 0.9318, 0.9090 ± 0.0264, and 0.8950 ± 0.0027 in the LOOCV, 2-fold, and 5-fold cross validation framework, respectively, which demonstrate the reliable prediction performance of PADLMP.

SUBMITTER: Zhou S

PROVIDER: S-EPMC5960578 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

Zhou Shunxian S Xuan Zhanwei Z Wang Lei L Wang Lei L Ping Pengyao P Pei Tingrui T

Computational and mathematical methods in medicine 20180506

<h4>Motivation</h4>Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of dis ...[more]

PMID: 29853986

Similar Datasets

Project description:BackgroundAccumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well.ResultsIn this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA.ConclusionThe simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

Project description:Lung adenocarcinoma (LUAD) is the main cause of cancer-related deaths worldwide. Long noncoding RNAs have been reported to play an important role in various cancers due to their special functions. Therefore, identifying the lncRNAs involved in LUAD tumorigenesis and development can help improve therapeutic strategies. The TCGA-LUAD RNA expression profile was downloaded from The Cancer Genome Atlas, and a total of 49 differential lncRNAs, 112 differential miRNAs, and 2,953 differential mRNAs were screened. Through Kaplan-Meier curves, interaction networks, hub RNAs (lncRNAs, miRNAs, and mRNAs) were obtained. These hub genes are mainly involved in cell proliferation, cell cycle, lung development, and tumor-related signaling pathways. Two lncRNAs (SMIM25 and PCAT19) more significantly related to the prognosis of LUAD were screened by univariate Cox analysis, multivariate Cox analysis, and risk model analysis. The qPCR results showed that the expression levels of SMIM25 and PCAT19 were downregulated in clinical tissues, A549 and SPC-A1 cells, which were consistent with the bioinformatics analysis results. Subsequently, the PCAT19/miR-143-3p pairs were screened through the weighted gene co-expression network analysis and miRNA-lncRNA regulatory network. Dual luciferase detection confirmed that miR-143-3p directly targets PCAT19, and qPCR results indicated that the expression of the two is positively correlated. Cell function tests showed that overexpression of PCAT19 could significantly inhibit the proliferation, migration, and invasion of A549 and SPC-A1 cells. In contrast, knockout of PCAT19 can better promote the proliferation and migration of A549 and SPC-A1 cells. The expression of PCAT19 was negatively correlated with tumor grade, histological grade, and tumor mutation load in LUAD. In addition, co-transfection experiments confirmed that the miR-143-3p mimic could partially reverse the effect of PCAT19 knockout on the proliferation of A549 and SPC-A1 cells. In summary, PCAT19 is an independent prognostic factor in patients with LUAD that can regulate the proliferation, migration, and invasion of LUAD cells and may be a potential biomarker for the diagnosis of LUAD. PCAT19/miR-143-3p plays a very important regulatory role in the occurrence and development of LUAD.

Dataset Information

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

Publications

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets