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Computational Models in Non-Coding RNA and Human Disease

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 53847

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Special Issue Editor

Prof. Dr. Xing Chen

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Guest Editor

Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou 221116, China
Interests: bioinformatics; computational biology; systems biology; non-coding RNA; drug discovery; machine learning; network algorithm
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

Recent transcriptomic and bioinformatics studies suggest that ncRNAs appear to comprise a hidden layer of internal signals that control various levels of gene expression in physiology and development. Furthermore, ncRNAs have also been revealed to contribute to diseases, including cancer, autism, Alzheimer's, and so on. Predicting ncRNA–disease associations could, not only boost human disease diagnostics and prognostics, but also improve new drug development. However, little efforts have been made to understand and predict ncRNA–disease associations on a large scale until now. Additionally, traditional methods are both expensive and time-consuming. In contrast to the traditional experimental approaches, the aim of us is to assess these ncRNA–disease associations on a large-scale, in humans, with computational methods based on big data accumulated by previous experimental methods. To find associations between ncRNAs and their corresponding diseases, various statistical and computational techniques could be employed. Similar to the research on the ncRNA–disease association, research on ncRNA–protein interaction, function, and the structure of ncRNAs, and even drug effects associated with ncRNAs, are all fields that we will devote ourselves to.

Prof. Dr. Xing Chen
Guest Editor

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Keywords

ncRNA-disease association prediction
small molecule-ncRNA association prediction
ncRNA-protein interaction prediction
ncRNA-environmental factor interaction prediction
ncRNA functional similarity network construction
ncRNA function prediction
miRNA-transcriptional factor interaction prediction
ncRNA biomarker detection

Published Papers (13 papers)

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Editorial

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5 pages, 197 KiB

Open AccessEditorial

Computational Models in Non-Coding RNA and Human Disease

by Xing Chen, Chun-Chun Wang and Na-Na Guan

Int. J. Mol. Sci. 2020, 21(5), 1557; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21051557 - 25 Feb 2020

Cited by 12 | Viewed by 2361

Abstract

The central dogma of molecular biology has told that DNA sequences encode proteins through RNAs, which function as an information intermediary [...] Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

Research

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11 pages, 2281 KiB

Open AccessCommunication

Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery

by Michelle E. Pewarchuk, Mateus C. Barros-Filho, Brenda C. Minatel, David E. Cohn, Florian Guisier, Adam P. Sage, Erin A. Marshall, Greg L. Stewart, Leigha D. Rock, Cathie Garnis and Wan L. Lam

Int. J. Mol. Sci. 2019, 20(22), 5697; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20225697 - 14 Nov 2019

Cited by 7 | Viewed by 2229

Abstract

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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14 pages, 2310 KiB

Open AccessArticle

Identification of an Interferon-Stimulated Long Noncoding RNA (LncRNA ISR) Involved in Regulation of Influenza A Virus Replication

by Qidong Pan, Zhonghui Zhao, Yuan Liao, Shih-Hsin Chiu, Song Wang, Biao Chen, Na Chen, Yuhai Chen and Ji-Long Chen

Int. J. Mol. Sci. 2019, 20(20), 5118; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20205118 - 16 Oct 2019

Cited by 31 | Viewed by 3821

Abstract

Long noncoding RNAs (lncRNAs) are involved in a diversity of biological processes. It is known that differential expression of thousands of lncRNAs occurs in host during influenza A virus (IAV) infection. However, only few of them have been well characterized. Here, we identified a lncRNA, named as interferon (IFN)-stimulated lncRNA (ISR), which can be significantly upregulated in response to IAV infection in a mouse model. A sequence alignment revealed that lncRNA ISR is present in mice and human beings, and indeed, we found that it was expressed in several human and mouse cell lines and tissues. Silencing lncRNA ISR in A549 cells resulted in a significant increase in IAV replication, whereas ectopic expression of lncRNA ISR reduced the viral replication. Interestingly, interferon-β (IFN-β) treatment was able to induce lncRNA ISR expression, and induction of lncRNA ISR by viral infection was nearly abolished in host deficient of IFNAR1, a type I IFN receptor. Furthermore, the level of IAV-induced lncRNA ISR expression was decreased either in retinoic acid-inducible gene I (RIG-I) knockout A549 cells and mice or by nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) inhibitor treatment. Together, these data elucidate that lncRNA ISR is regulated by RIG-I-dependent signaling that governs IFN-β production during IAV infection, and has an inhibitory capacity in viral replication. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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16 pages, 722 KiB

Open AccessArticle

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

by Xiaoyong Pan, Lei Chen, Kai-Yan Feng, Xiao-Hua Hu, Yu-Hang Zhang, Xiang-Yin Kong, Tao Huang and Yu-Dong Cai

Int. J. Mol. Sci. 2019, 20(9), 2185; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20092185 - 02 May 2019

Cited by 30 | Viewed by 5170

Abstract

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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30 pages, 3024 KiB

Open AccessArticle

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods

by Hui Zhang, Yanchun Liang, Siyu Han, Cheng Peng and Ying Li

Int. J. Mol. Sci. 2019, 20(6), 1284; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20061284 - 14 Mar 2019

Cited by 28 | Viewed by 4963

Abstract

Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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14 pages, 350 KiB

Open AccessArticle

RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction

by Cheng Peng, Siyu Han, Hui Zhang and Ying Li

Int. J. Mol. Sci. 2019, 20(5), 1070; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20051070 - 01 Mar 2019

Cited by 49 | Viewed by 4866

Abstract

Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-binding proteins. The research on ncRNA–protein interaction is the key to understanding the function of ncRNA. However, the biological experiment techniques for identifying RNA–protein interactions (RPIs) are currently still expensive and time-consuming. Due to the complex molecular mechanism of ncRNA–protein interaction and the lack of conservation for ncRNA, especially for long ncRNA (lncRNA), the prediction of ncRNA–protein interaction is still a challenge. Deep learning-based models have become the state-of-the-art in a range of biological sequence analysis problems due to their strong power of feature learning. In this study, we proposed a hierarchical deep learning framework RPITER to predict RNA–protein interaction. For sequence coding, we improved the conjoint triad feature (CTF) coding method by complementing more primary sequence information and adding sequence structure information. For model design, RPITER employed two basic neural network architectures of convolution neural network (CNN) and stacked auto-encoder (SAE). Comprehensive experiments were performed on five benchmark datasets from PDB and NPInter databases to analyze and compare the performances of different sequence coding methods and prediction models. We found that CNN and SAE deep learning architectures have powerful fitting abilities for the k-mer features of RNA and protein sequence. The improved CTF coding method showed performance gain compared with the original CTF method. Moreover, our designed RPITER performed well in predicting RNA–protein interaction (RPI) and could outperform most of the previous methods. On five widely used RPI datasets, RPI369, RPI488, RPI1807, RPI2241 and NPInter, RPITER obtained

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of 0.821, 0.911, 0.990, 0.957 and 0.985, respectively. The proposed RPITER could be a complementary method for predicting RPI and constructing RPI network, which would help push forward the related biological research on ncRNAs and lncRNAs. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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12 pages, 2217 KiB

Open AccessArticle

Systematical Identification of Breast Cancer-Related Circular RNA Modules for Deciphering circRNA Functions Based on the Non-Negative Matrix Factorization Algorithm

by Shuyuan Wang, Peng Xia, Li Zhang, Lei Yu, Hui Liu, Qianqian Meng, Siyao Liu, Jie Li, Qian Song, Jie Wu, Weida Wang, Lei Yang, Yun Xiao and Chaohan Xu

Int. J. Mol. Sci. 2019, 20(4), 919; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20040919 - 20 Feb 2019

Cited by 16 | Viewed by 3711

Abstract

Circular RNA (circRNA), a kind of special endogenous RNA, has been shown to be implicated in crucial biological processes of multiple cancers as a gene regulator. However, the functional roles of circRNAs in breast cancer (BC) remain to be poorly explored, and relatively incomplete knowledge of circRNAs handles the identification and prediction of BC-related circRNAs. Towards this end, we developed a systematic approach to identify circRNA modules in the BC context through integrating circRNA, mRNA, miRNA, and pathway data based on a non-negative matrix factorization (NMF) algorithm. Thirteen circRNA modules were uncovered by our approach, containing 4164 nodes (80 circRNAs, 2703 genes, 63 miRNAs and 1318 pathways) and 67,959 edges in total. GO (Gene Ontology) function screening identified nine circRNA functional modules with 44 circRNAs. Within them, 31 circRNAs in eight modules having direct relationships with known BC-related genes, miRNAs or disease-related pathways were selected as BC candidate circRNAs. Functional enrichment results showed that they were closely related with BC-associated pathways, such as ‘KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAYS IN CANCER’, ‘REACTOME IMMUNE SYSTEM’ and ‘KEGG MAPK SIGNALING PATHWAY’, ‘KEGG P53 SIGNALING PATHWAY’ or ‘KEGG WNT SIGNALING PATHWAY’, and could sever as potential circRNA biomarkers in BC. Comparison results showed that our approach could identify more BC-related functional circRNA modules in performance. In summary, we proposed a novel systematic approach dependent on the known disease information of mRNA, miRNA and pathway to identify BC-related circRNA modules, which could help identify BC-related circRNAs and benefits treatment and prognosis for BC patients. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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11 pages, 1125 KiB

Open AccessArticle

Molecular Network-Based Drug Prediction in Thyroid Cancer

by Xingyu Xu, Haixia Long, Baohang Xi, Binbin Ji, Zejun Li, Yunyue Dang, Caiying Jiang, Yuhua Yao and Jialiang Yang

Int. J. Mol. Sci. 2019, 20(2), 263; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20020263 - 11 Jan 2019

Cited by 16 | Viewed by 3889

Abstract

As a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher’s exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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20 pages, 1389 KiB

Open AccessArticle

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases

by Haochen Zhao, Linai Kuang, Xiang Feng, Quan Zou and Lei Wang

Int. J. Mol. Sci. 2019, 20(1), 110; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20010110 - 28 Dec 2018

Cited by 14 | Viewed by 3014

Abstract

Accumulating evidence progressively indicated that microRNAs (miRNAs) play a significant role in the pathogenesis of diseases through many experimental studies; therefore, developing powerful computational models to identify potential human miRNA–disease associations is vital for an understanding of the disease etiology and pathogenesis. In this paper, a weighted interactive network was firstly constructed by combining known miRNA–disease associations, as well as the integrated similarity between diseases and the integrated similarity between miRNAs. Then, a new computational method implementing the newly weighted interactive network was developed for discovering potential miRNA–disease associations (WINMDA) by integrating the T most similar neighbors and the shortest path algorithm. Simulation results show that WINMDA can achieve reliable area under the receiver operating characteristics (ROC) curve (AUC) results of 0.9183 ± 0.0007 in 5-fold cross-validation, 0.9200 ± 0.0004 in 10-fold cross-validation, 0.9243 in global leave-one-out cross-validation (LOOCV), and 0.8856 in local LOOCV. Furthermore, case studies of colon neoplasms, gastric neoplasms, and prostate neoplasms based on the Human microRNA Disease Database (HMDD) database were implemented, for which 94% (colon neoplasms), 96% (gastric neoplasms), and 96% (prostate neoplasms) of the top 50 predicting miRNAs were confirmed by recent experimental reports, which also demonstrates that WINMDA can effectively uncover potential miRNA–disease associations. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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15 pages, 907 KiB

Open AccessArticle

An Enrichment Analysis for Cardiometabolic Traits Suggests Non-Random Assignment of Genes to microRNAs

by Rima Mustafa, Mohsen Ghanbari, Marina Evangelou and Abbas Dehghan

Int. J. Mol. Sci. 2018, 19(11), 3666; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms19113666 - 20 Nov 2018

Cited by 2 | Viewed by 3369

Abstract

MicroRNAs (miRNAs) regulate the expression of the majority of genes. However, it is not known whether they regulate genes in random or are organized according to their function. To this end, we chose cardiometabolic disorders as an example and investigated whether genes associated with cardiometabolic disorders are regulated by a random set of miRNAs or a limited number of them. Single-nucleotide polymorphisms (SNPs) reaching genome-wide level significance were retrieved from most recent genome-wide association studies on cardiometabolic traits, which were cross-referenced with Ensembl to identify related genes and combined with miRNA target prediction databases (TargetScan, miRTarBase, or miRecords) to identify miRNAs that regulate them. We retrieved 520 SNPs, of which 355 were intragenic, corresponding to 304 genes. While we found a higher proportion of genes reported from all GWAS that were predicted targets for miRNAs in comparison to all protein-coding genes (75.1%), the proportion was even higher for cardiometabolic genes (80.6%). Enrichment analysis was performed within each database. We found that cardiometabolic genes were over-represented in target genes for 29 miRNAs (based on TargetScan) and 3 miRNAs (miR-181a, miR-302d and miR-372) (based on miRecords) after Benjamini-Hochberg correction for multiple testing. Our work provides evidence for non-random assignment of genes to miRNAs and supports the idea that miRNAs regulate sets of genes that are functionally related. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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18 pages, 1223 KiB

Open AccessArticle

Tissue Expression Difference between mRNAs and lncRNAs

by Lei Chen, Yu-Hang Zhang, Xiaoyong Pan, Min Liu, Shaopeng Wang, Tao Huang and Yu-Dong Cai

Int. J. Mol. Sci. 2018, 19(11), 3416; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms19113416 - 31 Oct 2018

Cited by 55 | Viewed by 4615

Abstract

Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon’s entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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20 pages, 4296 KiB

Open AccessArticle

LncRNA RP11-79H23.3 Functions as a Competing Endogenous RNA to Regulate PTEN Expression through Sponging hsa-miR-107 in the Development of Bladder Cancer

by Hong Chi, Rui Yang, Xiaying Zheng, Luyu Zhang, Rong Jiang and Junxia Chen

Int. J. Mol. Sci. 2018, 19(9), 2531; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms19092531 - 26 Aug 2018

Cited by 36 | Viewed by 3606

Abstract

Accumulating evidence indicates that the aberrant expression of long noncoding RNAs (lncRNAs) is involved in tumorigenesis and cancer development. However, the biological functions and underlying mechanisms of lncRNAs in bladder cancer (BC) remain largely unknown. Here, we analyzed the lncRNA and mRNA expression profiles in BC using a microarray assay. We found that lncRNA RP11-79H23.3 and phosphatase and tensin homolog (PTEN) were significantly downregulated in BC tissues and cells. Meanwhile, RP11-79H23.3 expression was negatively correlated with clinical stage in BC. Functionally, we found that overexpression of RP11-79H23.3 could suppress cell proliferation, migration, and cell cycle progression, rearrange the cytoskeleton, and induce apoptosis in vitro. Moreover, upregulation of RP11-79H23.3 inhibited the angiogenesis, tumorigenesis, and lung metastasis in vivo, whereas RP11-79H23.3 knockdown exerted a contrary role. Mechanistically, we identified that RP11-79H23.3 could directly bind to miR-107 and abolish the suppressive effect on target gene PTEN, which leads to inactivation of the PI3K/Akt signaling pathway. Taken together, we first demonstrated that RP11-79H23.3 might suppress the pathogenesis and development of BC by acting as a sponge for miR-107 to increase PTEN expression. Our research revealed that RP11-79H23.3 could be a potential target for diagnosis and therapy of BC. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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Review

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15 pages, 948 KiB

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Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases

by Chen Zhao, Yu Zhang and Aleksander S. Popel

Int. J. Mol. Sci. 2019, 20(2), 421; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms20020421 - 19 Jan 2019

Cited by 16 | Viewed by 7207

Abstract

MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR–target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases. Full article

(This article belongs to the Special Issue Computational Models in Non-Coding RNA and Human Disease)

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