Non-coding RNA and disease

Diederichs, Sven

doi:10.4161/rna.20972

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…In UTRs, conserved cisregulatory elements were discovered to serve as binding sites for antisense RNAs and proteins, thus regulating the stability, localization, and translation of mRNAs (Shabalina & Spiridonov, 2004). Importantly, many of these processes are misregulated in a variety of human diseases, ranging from cancers to neurodegenerative and neuromuscular diseases (Cooper, Wan, & Dreyfuss, 2009;Diederichs, 2012;Esteller, 2011). The RNAs involved in regulating these processes were found to be differentially expressed in disease states.…”

Section: Mammalian Rnas As Therapeutic Targetsmentioning

confidence: 99%

Targeting RNA in mammalian systems with small molecules

2018

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The recognition of RNA functions beyond canonical protein synthesis has challenged the central dogma of molecular biology. Indeed, RNA is now known to directly regulate many important cellular processes, including transcription, splicing, translation, and epigenetic modifications. The misregulation of these processes in disease has led to an appreciation of RNA as a therapeutic target. This potential was first recognized in bacteria and viruses, but discoveries of new RNA classes following the sequencing of the human genome have invigorated exploration of its disease-related functions in mammals. As stable structure formation is evolving as a hallmark of mammalian RNAs, the prospect of utilizing small molecules to specifically probe the function of RNA structural domains and their interactions is gaining increased recognition. To date, researchers have discovered bioactive small molecules that modulate phenotypes by binding to expanded repeats, microRNAs, G-quadruplex structures, and RNA splice sites in neurological disorders, cancers, and other diseases. The lessons learned from achieving these successes both call for additional studies and encourage exploration of the plethora of mammalian RNAs whose precise mechanisms of action remain to be elucidated. Efforts toward understanding fundamental principles of small molecule-RNA recognition combined with advances in methodology development should pave the way toward targeting emerging RNA classes such as long noncoding RNAs. Together, these endeavors can unlock the full potential of small molecule-based probing of RNA-regulated processes and enable us to discover new biology and underexplored avenues for therapeutic intervention in human disease. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions RNA in Disease and Development > RNA in Disease.

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Section: Mammalian Rnas As Therapeutic Targetsmentioning

confidence: 99%

Targeting RNA in mammalian systems with small molecules

2018

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“…They were found to regulate transcription, translation, RNA modification, mRNA stability, chromatin structure, and signaling pathways by interacting with various biological molecules 2 . Most of these processes are related to a variety of human diseases, including cancers and neurodegenerative and neuromuscular diseases 3, 4. These discoveries have validated the potential of RNAs as therapeutic targets 5 .…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm

Chen

Sun

et al. 2019

Molecular Therapy - Nucleic Acids

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Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.

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“…miRNAs are involved in almost every process in human cells. Therefore, researchers believe that every disease is influenced by a miRNAs component [ 6 ]. miR-140-5p and miR-146a can target Sirt2, Nrf2, TAF9b/P53, and other pathways, that they take a important place in doxorubicin-induced cardiotoxicity [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization

Ai¹,

Liang²,

Yuan³

et al. 2023

BMC Genomics

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Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good complement to wet experiment methods, computational prediction methods can greatly save experimental costs. However, high false-negative data and insufficient use of multi-source information can affect the performance of computational prediction methods. Furthermore, many computational methods do not have good robustness and generalization on different datasets. In this work, we propose an effective end-to-end computing framework, called GDCL-NcDA, of deep graph learning and deep matrix factorization (DMF) with contrastive learning, which identifies the latent ncRNA-disease association on diverse multi-source heterogeneous networks (MHNs). The diverse MHNs include different similarity networks and proven associations among ncRNAs (miRNAs, circRNAs, and lncRNAs), genes, and diseases. Firstly, GDCL-NcDA employs deep graph convolutional network and multiple attention mechanisms to adaptively integrate multi-source of MHNs and reconstruct the ncRNA-disease association graph. Then, GDCL-NcDA utilizes DMF to predict the latent disease-associated ncRNAs based on the reconstructed graphs to reduce the impact of the false-negatives from the original associations. Finally, GDCL-NcDA uses contrastive learning (CL) to generate a contrastive loss on the reconstructed graphs and the predicted graphs to improve the generalization and robustness of our GDCL-NcDA framework. The experimental results show that GDCL-NcDA outperforms highly related computational methods. Moreover, case studies demonstrate the effectiveness of GDCL-NcDA in identifying the associations among diversiform ncRNAs and diseases.

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Non-coding RNA and disease

Cited by 13 publications

References 19 publications

Targeting RNA in mammalian systems with small molecules

Targeting RNA in mammalian systems with small molecules

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm

GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization

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