Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network

Sun, Jie; Shi, Hang; Wang, Zhenzhen; Zhang, Changjian; Liu, Lin; Wang, Letian; He, Weiwei; Hao, Dapeng; Liu, Shulin; Zhou, Meng

doi:10.1039/c3mb70608g

Cited by 264 publications

(199 citation statements)

References 40 publications

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“…3c). Similar to the most of the reported biological networks, the degree of this MFSN also shows a scale-free distribution [5,9,[35][36][37]. It means that most of the miRNAs only have a few functionally similar miRNAs, and a few of miRNAs have a numerous functional similar miRNA (Fig.…”

Section: Mirna Functional Similarity Networksupporting

confidence: 79%

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DisSetSim: An online system for calculating similarity between disease sets

Zhao

Liu

et al. 2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Background: Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS) limits this type of application. Results: Here, we introduce DisSetSim, an online system to solve this problem in this article. Five state-of-the-art methods involving Resnik's, Lin's, Wang's, PSB, and SemFunSim methods were implemented to measure the similarity score of pairwise diseases (SSD) first. And then "pair-wise-best pairs-average" (PWBPA) method was implemented to calculated the SSDS by the SSD. The system was applied for calculating the functional similarity of miRNAs based on their induced disease sets. The results were further used to predict potential disease-miRNA relationships. Conclusions: The high area under the receiver operating characteristic curve AUC (0.9296) based on leave-one-out cross validation shows that the PWBPA method achieves a high true positive rate and a low false positive rate. The system can be accessed from http://www.bio-annotation.cn:8080/DisSetSim/.

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Section: Mirna Functional Similarity Networksupporting

confidence: 79%

“…For comparing multiple aspects, the best measure is the PWBPA method, which is widely utilized in calculating similarity of DO and GO term sets [1,7,9,12].…”

Section: Introductionmentioning

confidence: 99%

DisSetSim: An online system for calculating similarity between disease sets

Zhao

Liu

et al. 2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…This review summarizes the potential impact of dysregulated lncRNAs in HCC and the known interactions between lncRNAs and DNA, RNA, and proteins in hepatocellular carcinoma. Sun et al [46] proposed a global network-based computational framework to infer potential human lncRNA-disease associations by implementing the random walk with restart method on a lncRNA functional similarity network. In total, they observed 371 lncRNA-lncRNA functional associations between 117 lncRNAs in the lncRNA functional similarity network (LFSN).…”

Section: Resultsmentioning

confidence: 99%

Roles of long noncoding RNAs in Hepatocellular Carcinoma

2016

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Long noncoding RNAs (lncRNAs) are nonprotein coding transcripts longer than 200 nucleotides. Aberrant expression of lncRNAs has been found to be associated with hepatocellular carcinoma, one of the most malignant tumors. In this paper, we give a systematic and comprehensive review of existing literature about the involvement of lncRNAs in hepatocellular carcinoma. To date, evidence suggests that a number of lncRNAs, including HEIH, H19, HOTAIR, MALAT1, and PVT1, may regulate the transcription of target genes by recruiting histone-modifying complexes. Under certain circumstances, lncRNAs form RNA-dsDNA triplexes. Certain lncRNAs, such as HULC, HOTAIR, H19, HOTTIP and PTENP1, exhibit their biological roles by associating with microRNAs (miRNAs). In addition, by complementary base pairing with mRNAs or forming complexes with RNA binding proteins (RBPs), lncRNA-ATB, MALAT1 and PCNA-AS1 may mediate mRNA stability and splicing. In conclusion, interactions with DNA, RNA and proteins appears to be involved in lncRNAs' participation in tumorigenesis and developmental processes related to hepatocellular carcinoma.

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“…In the previous studies of disease-related gene prediction, a random walk on a single gene network has been used to predict candidate disease miRNAs or lncRNA such as RWRMDA 38 and RWRlncD. 21 Thus, a performance comparison between RWRHLD and RWRlncD was implemented. The difference between RWRHLD and RWRlncD is that the RWRlncD method uses a random walking algorithm only on an lncRNA network.…”

Section: Comparison With Other Similar Methodsmentioning

confidence: 99%

Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network

et al. 2015

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Accumulated evidence has shown that long non-coding RNAs (lncRNA) act as a widespread layer in gene regulatory networks and are involved in a wide range of biological processes. The dysregulation of lncRNA has been implicated in various complex human diseases. Although several computational methods have been developed to predict disease-related lncRNA, this still remains a considerable challenging task. In this study, we tried to construct an lncRNA-lncRNA crosstalk network by examining the significant co-occurrence of shared miRNA response elements on lncRNA transcripts from the competing endogenous RNAs viewpoint. As expected, functional analysis showed that lncRNA sharing significantly enriched interacting miRNAs tend to be involved in similar diseases and have more functionally related flanking gene sets. We further proposed a novel rank-based method, RWRHLD, to prioritize candidate lncRNA-disease associations by integrating three networks (miRNA-associated lncRNA-lncRNA crosstalk network, disease-disease similarity network and known lncRNA-disease association network) into a heterogeneous network and implementing a random walk with restart on this heterogeneous network. We used leave-one-out cross-validation to test the performance of this rank-based method in this study based on known experimentally verified lncRNA-disease associations and obtained a reliable AUC value of 0.871, which is much higher than RWR merely based on an lncRNA network, hypergeometric test and random situation. Furthermore, several novel lncRNA-disease associations predicted in case studies of ovarian cancer and prostate cancer have been confirmed in new studies by literature surveys.

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Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network

Cited by 264 publications

References 40 publications

DisSetSim: An online system for calculating similarity between disease sets

DisSetSim: An online system for calculating similarity between disease sets

Roles of long noncoding RNAs in Hepatocellular Carcinoma

Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network

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