miR2Disease: a manually curated database for microRNA deregulation in human disease

Jiang, Qinghua; Wang, Yadong; Hao, Yangyang; Juan, Liran; Teng, Mingxiang; Zhang, Xinjun; Li, Meimei; Wang, Guohua; Liu, Yunlong

doi:10.1093/nar/gkn714

Cited by 1,242 publications

(957 citation statements)

References 56 publications

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“…Through mining candidates on mimiRNA (Ritchie et al, 2010), miR2Disease Base (Jiang et al, 2009) and miRNAMap (Hsu et al, 2008) database, miR-135a caught our attention and its property of brain-enriched was confirmed by quantitative real-time PCR (qPCR) (Figure 1a). …”

Section: Glia-enriched Mir-135a Is Downregulated In Malignant Gliomamentioning

confidence: 99%

MiR-135a functions as a selective killer of malignant glioma

Lin

et al. 2011

Oncogene

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Glioma is the most common and fatal primary brain tumor. Thus far, therapeutic strategies to efficiently and specifically antagonize glioma are limited and poorly developed. Here we report that glia-enriched miR-135a, a microRNA that is dramatically downregulated in malignant glioma and correlated with the pathological grading, is capable of inducing mitochondria-dependent apoptosis of malignant glioma by regulating various genes including STAT6, SMAD5 and BMPR2, as well as affecting the signaling pathway downstream. Moreover, this lethal effect is selectively towards malignant glioma cells, but not neurons and glial cells, through a novel mechanism. Our findings suggest an important role of miR-135a in glioma etiology and provide a potential candidate for malignant glioma therapy.

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Section: Glia-enriched Mir-135a Is Downregulated In Malignant Gliomamentioning

confidence: 99%

MiR-135a functions as a selective killer of malignant glioma

Lin

et al. 2011

Oncogene

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“…In the first type of case studies, the top 10 and top 50 predicted miRNAs for the investigated diseases were validated by another two miRNA‐disease databases, namely dbDEMC 29 and miR2Disease 30…”

Section: Resultsmentioning

confidence: 99%

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

Chen

Wang

Huang

2018

J Cellular Molecular Medi

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In recent years, microRNAs (miRNAs) are attracting an increasing amount of researchers’ attention, as accumulating studies show that miRNAs play important roles in various basic biological processes and that dysregulation of miRNAs is connected with diverse human diseases, particularly cancers. However, the experimental methods to identify associations between miRNAs and diseases remain costly and laborious. In this study, we developed a computational method named Network Distance Analysis for MiRNA‐Disease Association prediction (NDAMDA) which could effectively predict potential miRNA‐disease associations. The highlight of this method was the use of not only the direct network distance between 2 miRNAs (diseases) but also their respective mean network distances to all other miRNAs (diseases) in the network. The model's reliable performance was certified by the AUC of 0.8920 in global leave‐one‐out cross‐validation (LOOCV), 0.8062 in local LOOCV and the average AUCs of 0.8935 ± 0.0009 in fivefold cross‐validation. Moreover, we applied NDAMDA to 3 different case studies to predict potential miRNAs related to breast neoplasms, lymphoma, oesophageal neoplasms, prostate neoplasms and hepatocellular carcinoma. Results showed that 86%, 72%, 86%, 86% and 84% of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, NDAMDA is a reliable method for predicting disease‐related miRNAs.

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“…Specifically, we selected 16 common diseases among the four databases (i.e. dbDEMC[39], miR2Disease[40], miRwayDB[41] and PhenomiR[42]) for the subsequent case studies and validated the prediction results across all the databases. The 16 common diseases are Breast Neoplasms, Cervical Intraepithelial Neoplasia, Colorectal Neoplasms, Hepatocellular Carcinoma, Lymphoma, Lung Neoplasms, Leukemia, Nasopharyngeal Neoplasms, Liver Neoplasms, Ovarian Neoplasms, Pancreatic Neoplasms, Prostatic Neoplasms, Stomach Neoplasms, Thyroid Neoplasms, Urinary Bladder Neoplasms and Uterine Cervical Neoplasms.…”

Section: Resultsmentioning

confidence: 99%

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Liang

Xiao

et al. 2018

RNA Biology

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Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

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miR2Disease: a manually curated database for microRNA deregulation in human disease

Cited by 1,242 publications

References 56 publications

MiR-135a functions as a selective killer of malignant glioma

MiR-135a functions as a selective killer of malignant glioma

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

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