dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers

Yang, Zhen; Wu, Liangcai; Wang, Anqiang; Tang, Wei; Zhao, Yi; Zhao, Haitao; Teschendorff, Andrew E.

doi:10.1093/nar/gkw1079

Cited by 307 publications

(214 citation statements)

References 38 publications

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“…The prediction results were verified by another two databases dbDEMC 52 and miR2Disease, 53 both of which record experimentally verified disease-related miRNAs. Our prediction results showed that 10 of the top 10 and 49 of the top 50 candidate miRNAs were verified to be associated with BN by at least one of the two databases.…”

Section: Case Studymentioning

confidence: 73%

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Liang

Xiao

et al. 2018

J Cellular Molecular Medi

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MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably 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 matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.

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Section: Case Studymentioning

confidence: 73%

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

Liang

Xiao

et al. 2018

J Cellular Molecular Medi

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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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“…While approximately 300 miRNAs have demonstrated a role in carcinogenesis, more than 2000 miRNAs have been found differentially expressed in human cancers (Yang et al, 2017) and evidence that different types of cancer can be discriminated by miRNA profiling has been reported (Iorio & Croce, 2017;Petrovic, Ergün, & Isenovic, 2017). In cutaneous melanoma, there is increasing evidence for a significant role of specific miRNAs as oncogenes or tumour suppressor genes (Mione & Bosserhoff, 2015;Mirzaei et al, 2016;Varamo, Occelli, Vivenza, Merlano, & Lo Nigro, 2017) as well as their involvement in other aspects of melanoma biology (Leibowitz-Amit, Sidi, & Avni, 2012;Luo, Weber, Osen, Bosserhoff, & Eichmuller, 2014;Sun, Zhou, Majid, Kashani-Sabet, & Dar, 2014;Voller, Ott, & Bosserhoff, 2013).…”

Section: G Ener Al Fe Ature S Of Mirna Smentioning

confidence: 99%

Circulating cell‐free microRNAs in cutaneous melanoma staging and recurrence or survival prognosis

Polini

Carpi

Romanini

et al. 2019

Pigment Cell Melanoma Res

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Cutaneous melanoma is a skin cancer with increasing incidence. Identification of novel clinical biomarkers able to detect the stage of disease and suggest prognosis could improve treatment and outcome for melanoma patients. Cell‐free microRNAs (cf‐miRNAs) are the circulating copies of short non‐coding RNAs involved in gene expression regulation. They are released into the interstitial fluid, are detectable in blood and other body fluids and have interesting features of ideal biomarker candidates. They are stable outside the cell, tissue specific, vary along with cancer development and are sensitive to change in the disease course such as progression or therapeutic response. Moreover, they are accessible by non‐invasive methods or venipuncture. Some articles have reported different cf‐miRNAs with the potential of diagnostic tools for melanoma staging, recurrence and survival prediction. Although some concordance of results is already emerging, differences in analytical methods, normalization strategies and tumour staging still will require further research and standardization prior to clinical usage of cf‐miRNA analysis. This article reviews this literature with the aim of contributing to a shared focusing on these new promising tools for melanoma treatment and care.

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dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers

Cited by 307 publications

References 38 publications

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

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

Circulating cell‐free microRNAs in cutaneous melanoma staging and recurrence or survival prognosis

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