miRNA Target Binding Research Articles

MiR-128-3p suppresses tumor growth and enhances chemosensitivity in tongue squamous cell carcinoma through MAP2K7 targeting.

MicroRNAs (miRNAs), which are key players in cancer cell resistance to chemotherapy, notably target genes associated with drug resistance. While miRNA-128-3p is recognized for its involvement in various cancers, its specific role in tumorigenesis and cisplatin (CIS) resistance in tongue cancer remains unclear. Therefore, in the present study, we endeavoured to elucidate the significance of miR-128-3p in tongue squamous cell carcinoma (TSCC), shedding light on its intricate functions and underlying mechanisms. We quantified the expression of miR-128-3p and its target genes using qRT-PCR, followed by a series of functional assays in vitro, such as proliferation and migration assays, flow cytometry analysis, and western blotting to unravel the mechanisms underlying the functions of miR-128-3p. Additionally, we validated the ability of miR-128-3p to target MAP2K7 genes through luciferase reporter assays. We observed that increased expression of miR-128-3p significantly inhibited TSCC cell migration, proliferation, and epithelial-mesenchymal transition (EMT), possibly by regulating MAP2K7 in the JNK/MAP kinase pathway through miRNA target binding. Furthermore, we showed that increased miR-128-3p levels enhanced the sensitivity of TSCC cells to CIS through the JNK/c-Jun cascade. We observed that miR-128-3p reduces the expression of c-Jun and ABC transporter genes by targeting MAP2K7, affecting JNK1/2. This inhibition possibly decreases drug efflux and thus enhances the TSCC sensitivity to CIS treatment. Our findings demonstrate oncosuppressive behaviour of miR-128-3p, which also potentially enhances the sensitivity of TSCC cells to CIS by suppressing MAP2K7 and JNK1/2, leading to evasion of apoptosis.

MiRNASNP-v4: a comprehensive database for miRNA-related SNPs across 17 species.

Single nucleotide polymorphisms (SNPs) within microRNAs (miRNAs) and their target binding sites can influence miRNA biogenesis and target regulation, thereby participating in a variety of diseases and biological processes. Current miRNA-related SNP databases are often species-limited or based on outdated data. Therefore, we updated our miRNASNP database to version 4 by updating data, expanding the species from Homo sapiens to 17 species, and introducing several new features. In miRNASNP-v4, 82580 SNPs in miRNAs and 24836179 SNPs in 3'UTRs of genes across 17 species were identified and their potential effects on miRNA secondary structure and target binding were characterized. In addition, compared to the last release, miRNASNP-v4 includes the following improvements: (i) gene enrichment analysis for gained or lost miRNA target genes; (ii) identification of miRNA-related SNPs associated with drug response and immune infiltration in human cancers; (iii) inclusion of experimentally supported immune-related miRNAsand (iv) online prediction tools for 17 animal species. With the extensive data and user-friendly web interface, miRNASNP-v4 will serve as an invaluable resource for functional studies of SNPs and miRNAs in multiple species. The database is freely accessible at http://gong_lab.hzau.edu.cn/miRNASNP/.

MiR-185–5p rewires cisplatin resistance by restoring miR-203a-3p expression via downregulation of SOX9

Chemotherapeutic drug resistance is a challenge for the effective treatment of OSCC. There are a couple of studies on the involvement of microRNAs (miRNAs) in chemoresistance of oral squamous cell carcinoma (OSCC), but the exact molecular events in many cases are not clearly understood. In this work, we intend to track down key miRNA(s) and unveil their regulatory molecular mechanisms in imparting chemoresistance in this lethal cancer. We analyzed gene and miRNA array profiles of drug-resistant OSCC cells, predicted miRNA targets, performed enrichment analysis, and validated our findings in cisplatin-sensitive and cisplatin-resistant SCC9 and H357 OSCC cells. We evaluated the anticancer and chemosensitivity roles of selected miRNA by adopting several molecular assays like qRT-PCR, MTT assay, wound healing assay, fluorescence imaging by DCFHDA, AO/EB staining, DAPI, and γ-H2AX accumulation assay. We also validated the miRNA-target binding by qRT-PCR and luciferase reporter assay. Among the enriched miRNAs, we found miR-185–5p downregulated in cisplatin-resistant OSCC cells as a signature miRNA modulating chemoresistance. The upregulation of miR-185–5p by mimic transfection restores cisplatin sensitivity by decreasing cell viability in a dose-dependent manner and increasing ROS-induced DNA damage and apoptosis. miR-185–5p overexpression increases miR-203a-3p expression through negative regulation of SOX9. siRNA-mediated silencing of the SOX9 also shows similar results. Mechanistically, miR-185–5p dependent miR-203a-3p expression decreases cisplatin efflux and cisplatin-induced DNA damage repair by regulating ABCC1, ABCB1, RRM2, and RAN. This study will pave the way for employing this miR-185–5p as a combination therapeutic strategy to combat cisplatin resistance in oral cancer.

Abstract 3513: A CNN-based approach with efficient transfer learning improves microRNA-mRNA prediction

Abstract MicroRNAs play a crucial role in post-transcriptional regulation, influencing over 60% of human protein-coding genes by targeting specific mRNA sites to suppress protein translation. Various predictive algorithms aim to discern potential microRNA-mRNA pairs. Current approaches primarily employ sequence alignment, machine learning, and deep learning, yet encounter challenges such as complex data pre-processing, time-consuming model generation, and limited binding site precision. Additionally, some methods rely on inefficient RNN-based models, resulting in sluggish predictions. To address these issues, we proposed a CNN-based algorithm combined with transfer learning for direct and precise prediction of microRNA-mRNA binding sites, eliminating the need for extensive preprocessing. We introduced two models in this study: the per-based model and the miRNA-target binding decision model. The former screens potential target sites on 3’-UTR sequences, while the latter guides the decision-making process for miRNA-target pairs. The per-based model utilized a public database, extracting 786,447 human microRNA-mRNA pairs verified by CLIP-seq. It employed sequence alignment approaches to determine putative binding sites and per-base binding states. MicroRNA sequences and seed regions served as the initial convolutional kernels in the deep learning model, combined with encoded full-length 3’-UTR sequences of mRNAs as inputs for the fine-tuned U-Net architecture. For the miRNA-target binding decision model, we excluded the per-based model's decoder and initialized a new classification layer for target-site prediction. Leveraging experimental validation datasets from public databases, we extracted 2,846 binding and 1,058 non-binding human microRNA-mRNA pairs. The pre-trained model was fine-tuned on these 3,904 pairs. Both models underwent training with cycle learning rate, focal loss, gradient clipping, and weighted decay to address dataset imbalances. The dataset was split into 80% training and 20% testing data, with balanced accuracy as the evaluation metric. The per-based model achieved a robust 82.14% balanced accuracy on the test data, excelling in handling imbalanced datasets in per-based tasks. It swiftly and accurately identified nucleotide binding states. The miRNA-target binding decision model outperformed existing methods with a balanced accuracy of 80.39% on the test data. In contrast to many deep learning methods requiring additional preprocessing, our algorithm directly predicts per-base binding states from full-length sequences. It seamlessly transfers knowledge from the per-based to the miRNA-target model. Importantly, our approach relies solely on seed regions, eliminating the need for prior knowledge and enhancing microRNA target prediction reliability. This advancement holds promise for biomedical and clinical researchers, offering valuable insights. Citation Format: Chen-Hao Peng, Hui-Yu Chen, Da-Chuan Cheng, Eric Y. Chuang, Chien-Yueh Lee. A CNN-based approach with efficient transfer learning improves microRNA-mRNA prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3513.

Bifunctional Y-shaped probe combined with dual amplification for colorimetric sensing and molecular logic operation of two miRNAs

The abnormal expression of miRNA is closely related to various human diseases. In particular, the sensitivity detection of miRNA expression level is of great significance for the early diagnosis and prognosis of cancer. In this paper, we designed a Y-shaped DNA probe, using miRNA-21 and miRNA-141 as the dual input signals of AND logic gate. By combining with EXO III assisted target recycle and DNA hybridization chain reaction (HCR), we have realized dual signal amplification for detection of two miRNAs. In short, the Y-shaped DNA probe consists of two parts: the miRNA target binding region and the HCR initiator. When the two miRNAs are present at the same time, the target binding region specifically recognizes the target to generate two circulators, and then the HCR initiator is released. The EXO III specific cleavage two circulator, and release the target again which achieves the first step of signal amplification. After that, HCR was started by the split initiator generated in the first stage of continuous cycle, and the second step of signal amplification was realized. Thanks to the sensitive color change of gold nanoparticles in response to salt, we achieved ultra-high sensitivity for visual detection of miRNA-21 and miRNA-141. Under optimal conditions, the detection limit of both miRNA is 3 pM and the linear range is 10 pM to 0.4 nM. The method we designed could be applied in early detection and diagnosis of cancer.

In Silico Identification of Cassava Genome-Encoded MicroRNAs with Predicted Potential for Targeting the ICMV-Kerala Begomoviral Pathogen of Cassava.

Cassava mosaic disease (CMD) is caused by several divergent species belonging to the genus Begomovirus (Geminiviridae) transmitted by the whitefly Bemisia tabaci cryptic species group. In India and other parts of Asia, the Indian cassava mosaic virus-Kerala (ICMV-Ker) is an emergent begomovirus of cassava causing damage that results in reduced yield loss and tuber quality. Double-stranded RNA-mediated interference (RNAi) is an evolutionary conserved mechanism in eukaryotes and highly effective, innate defense system to inhibit plant viral replication and/or translation. The objective of this study was to identify and characterize cassava genome-encoded microRNAs (mes-miRNA) that are predicted to target ICMV-Ker ssDNA-encoded mRNAs, based on four in silico algorithms: miRanda, RNA22, Tapirhybrid, and psRNA. The goal is to deploy the predicted miRNAs to trigger RNAi and develop cassava plants with resistance to ICMV-Ker. Experimentally validated mature cassava miRNA sequences (n = 175) were downloaded from the miRBase biological database and aligned with the ICMV-Ker genome. The miRNAs were evaluated for base-pairing with the cassava miRNA seed regions and to complementary binding sites within target viral mRNAs. Among the 175 locus-derived mes-miRNAs evaluated, one cassava miRNA homolog, mes-miR1446a, was identified to have a predicted miRNA target binding site, at position 2053 of the ICMV-Ker genome. To predict whether the cassava miRNA might bind predicted ICMV-Ker mRNA target(s) that could disrupt viral infection of cassava plants, a cassava locus-derived miRNA-mRNA regulatory network was constructed using Circos software. The in silico-predicted cassava locus-derived mes-miRNA-mRNA network corroborated interactions between cassava mature miRNAs and the ICMV-Ker genome that warrant in vivo analysis, which could lead to the development of ICMV-Ker resistant cassava plants.

Analysis of the regulatory mechanisms of prognostic immune factors in thyroid cancer.

To explore the regulatory mechanism of immune prognostic factors in thyroid cancer. Based on the TCGA database and GEO database, this study used bioinformatics methods to study the potential regulatory mechanism of thyroid cancer prognosis, analyzed the differentially expressed genes and differential miRNAs between thyroid cancer and normal paracancerous tissues by R software, and constructed lasso risk factors. The immune prognostic factors of thyroid cancer were obtained from the model, and the miRDB website was used to predict the possibility of differential miRNA target binding of the immune prognostic factors and correlation analysis was performed, and finally verified by cell experiments. There were 1413 differentially expressed genes between thyroid cancer and normal paracancerous tissues, among which 21 immune-related genes were prognostic factors with significant differences in expression; lasso risk model obtained AKAP12, APOC1, TIMP3, ADAMTS9, ANK2, HTRA3, SYNDIG1 , ADAMTS5 and DACT1 were nine prognostic factors. A total of 58 differential miRNAs were found in thyroid cancer tissues and non-cancerous tissues. The possibility of differential miRNA targeting and binding of immune prognostic factors on the miRDB website and cell experiments was analyzed. The potential miRNA regulatory mechanism of immune prognostic factors in thyroid cancer has been explored.

Genome-Wide Identification and Characterization of RNA/DNA Differences Associated with Drought Response in Wheat.

RNA/DNA difference (RDD) is a post-transcriptional RNA modification to enrich genetic information, widely involved in regulating diverse biological processes in eukaryotes. RDDs in the wheat nuclear genome, especially those associated with drought response or tolerance, were not well studied up to now. In this study, we investigated the RDDs related to drought response based on the RNA-seq data of drought-stressed and control samples in wheat. In total, 21,782 unique RDDs were identified, of which 265 were found to be drought-induced, representing the first drought-responsive RDD landscape in the wheat nuclear genome. The drought-responsive RDDs were located in 69 genes, of which 35 were differentially expressed under drought stress. Furthermore, the effects of RNA/DNA differences were investigated, showing that they could result in changes of RNA secondary structure, miRNA-target binding as well as protein conserved domains in the RDD-containing genes. In particular, the A to C mutation in TraesCS2A02G053100 (orthology to OsRLCK) led to the loss of tae-miR9657b-5p targeting, indicating that RNA/DNA difference might mediate miRNA to regulate the drought-response process. This study reported the first drought-responsive RDDs in the wheat nuclear genome. It sheds light on the roles of RDD in drought tolerance, and may also contribute to wheat genetic improvement based on epi-transcriptome methods.

N 6-methyladenosine enhances post-transcriptional gene regulation by microRNAs.

Motivation N 6-methyladenosine (m6A) is the most prevalent modification in eukaryotic messenger RNAs. MicroRNAs (miRNAs) are abundant post-transcriptional regulators of gene expression. Correlation between m6A and miRNA-targeting sites has been reported to suggest possible involvement of m6A in miRNA-mediated gene regulation. However, it is unknown what the regulatory effects might be. In this study, we performed comprehensive analyses of high-throughput data on m6A and miRNA target binding and regulation.ResultsWe found that the level of miRNA-mediated target suppression is significantly enhanced when m6A is present on target mRNAs. The evolutionary conservation for miRNA-binding sites with m6A modification is significantly higher than that for miRNA-binding sites without modification. These findings suggest functional significance of m6A modification in post-transcriptional gene regulation by miRNAs. We also found that methylated targets have more stable structure than non-methylated targets, as indicated by significantly higher GC content. Furthermore, miRNA-binding sites that can be potentially methylated are significantly less accessible without methylation than those that do not possess potential methylation sites. Since either RNA-binding proteins or m6A modification by itself can destabilize RNA structure, we propose a model in which m6A alters local target secondary structure to increase accessibility for efficient binding by Argonaute proteins, leading to enhanced miRNA-mediated regulation.Availability and implementationN/A.

MicroRNA Variants and HLA-miRNA Interactions are Novel Rheumatoid Arthritis Susceptibility Factors.

Genome-wide association studies have identified >100 genetic risk factors for rheumatoid arthritis. However, the reported genetic variants could only explain less than 40% heritability of rheumatoid arthritis. The majority of the heritability is still missing and needs to be identified with more studies with different approaches and populations. In order to identify novel function SNPs to explain missing heritability and reveal novel mechanism pathogenesis of rheumatoid arthritis, 4 HLA SNPs (HLA-DRB1, HLA-DRB9, HLA-DQB1, and TNFAIP3) and 225 common SNPs located in miRNA, which might influence the miRNA target binding or pre-miRNA stability, were genotyped in 1,607 rheumatoid arthritis and 1,580 matched normal individuals. We identified 2 novel SNPs as significantly associated with rheumatoid arthritis including rs1414273 (miR-548ac, OR = 0.84, p = 8.26 × 10−4) and rs2620381 (miR-627, OR = 0.77, p = 2.55 × 10−3). We also identified that rs5997893 (miR-3928) showed significant epistasis effect with rs4947332 (HLA-DRB1, OR = 4.23, p = 0.04) and rs2967897 (miR-5695) with rs7752903 (TNFAIP3, OR = 4.43, p = 0.03). In addition, we found that individuals who carried 8 risk alleles showed 15.38 (95%CI: 4.69–50.49, p < 1.0 × 10−6) times more risk of being affected by RA. Finally, we demonstrated that the targets of the significant miRNAs showed enrichment in immune related genes (p = 2.0 × 10−5) and FDA approved drug target genes (p = 0.014). Overall, 6 novel miRNA SNPs including rs1414273 (miR-548ac, p = 8.26 × 10−4), rs2620381 (miR-627, p = 2.55 × 10−3), rs4285314 (miR-3135b, p = 1.10 × 10−13), rs28477407 (miR-4308, p = 3.44 × 10−5), rs5997893 (miR-3928, p = 5.9 × 10−3) and rs45596840 (miR-4482, p = 6.6 × 10−3) were confirmed to be significantly associated with RA in a Chinese population. Our study suggests that miRNAs might be interesting targets to accelerate understanding of the pathogenesis and drug development for rheumatoid arthritis.

Dissecting the Pathogenesis of Diabetic Retinopathy Based on the Biological ceRNA Network and Genome Variation Disturbance.

Background Diabetic retinopathy (DR) is the most important manifestation of diabetic microangiopathy. It is essential to explore the gene regulatory relationship and genomic variation disturbance of biological networks in DR progression. Methods In this study, we constructed a comprehensive lncRNA-mRNA ceRNA network of DR procession (CLMN) and explored its topological characteristics. Results Modular and functional analysis indicated that the organization of CLMN performed fundamental and specific functions in diabetes and DR pathology. The differential expression of hub ceRNA nodes and positive correlation reveals the highly connected ceRNA regulation and important roles in the regulating of DR pathology. A large proportion of SNPs in the TFBS, DHS, and enhancer regions of lncRNAs will affect lncRNA transcription and further cause expression variation. Some SNPs were found to disrupt the lncRNA functional elements such as miRNA target binding sites. These results indicate the complex nature of genotypic effects in the disturbing of CLMN and further contribute to gene expression variation and different disease phenotypes. Conclusion The identification of individual genomic variations and analysis of biological network disturbance by these genomic variations will help provide more personalized treatment plans and promote the development of precision medicine for DR.

Global RNA editing identification and characterization during human pluripotent-to-cardiomyocyte differentiation

SummaryRNA editing is widely involved in stem cell differentiation and development; however, RNA editing events during human cardiomyocyte differentiation have not yet been characterized and elucidated. Here, we identified genome-wide RNA editing sites and systemically characterized their genomic distribution during four stages of human cardiomyocyte differentiation. It was found that the expression level of ADAR1 affected the global number of adenosine to inosine (A-to-I) editing sites but not the editing degree. Next, we identified 43, 163, 544, and 141 RNA editing sites that contribute to changes in amino acid sequences, variation in alternative splicing, alterations in miRNA-target binding, and changes in gene expression, respectively. Generally, RNA editing showed a stage-specific pattern with 211 stage-shared editing sites. Interestingly, cardiac muscle contraction and heart-disease-related pathways were enriched by cardio-specific editing genes, emphasizing the connection between cardiomyocyte differentiation and heart diseases from the perspective of RNA editing. Finally, it was found that these RNA editing sites are also related to several congenital and noncongenital heart diseases. Together, our study provides a new perspective on cardiomyocyte differentiation and offers more opportunities to understand the mechanisms underlying cell fate determination, which can promote the development of cardiac regenerative medicine and therapies for human heart diseases.

Identification and Analysis of RNA Editing Events in Ovarian Serous Cystadenoma Using RNA-seq Data.

Recent studies have revealed thousands of A-to-I RNA editing events in primates. These events are closely related to the occurrence and development of multiple cancers, but the origination and general functions of these events in ovarian cancer remain incompletely understood. To further the determination of molecular mechanisms of ovarian cancer from the perspective of RNA editing. Here, we used the SNP-free RNA editing Identification Toolkit (SPRINT) to detect RNA editing sites. These editing sites were then annotated, and related functional analysis was performed. In this study, about 1.7 million RES were detected in each sample, and 98% of these sites were due to A-to-G editing and were mainly distributed in non-coding regions. More than 1,000 A-- to-G RES were detected in CDS regions, and nearly 700 could lead to amino acid changes. Our results also showed that editing in the 3'UTR regions could influence miRNA-target binding. We predicted the network of changed miRNA-mRNA interaction caused by the A-to-I RNA editing sites. We also screened the differential RNA editing sites between ovarian cancer and adjacent normal tissues. We then performed GO and KEGG pathway enrichment analysis on the genes that contained these differential RNA editing sites. Finally, we identified the potential dysregulated RNA editing events in ovarian cancer samples. This study systematically identified and analyzed RNA editing events in ovarian cancer and laid a foundation to explore the regulatory mechanism of RNA editing and its function in ovarian cancer.

MiRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets.

MicroRNAs (miRNAs) related single-nucleotide variations (SNVs), including single-nucleotide polymorphisms (SNPs) and disease-related variations (DRVs) in miRNAs and miRNA-target binding sites, can affect miRNA functions and/or biogenesis, thus to impact on phenotypes. miRNASNP is a widely used database for miRNA-related SNPs and their effects. Here, we updated it to miRNASNP-v3 (http://bioinfo.life.hust.edu.cn/miRNASNP/) with tremendous number of SNVs and new features, especially the DRVs data. We analyzed the effects of 7 161 741 SNPs and 505 417 DRVs on 1897 pre-miRNAs (2630 mature miRNAs) and 3′UTRs of 18 152 genes. miRNASNP-v3 provides a one-stop resource for miRNA-related SNVs research with the following functions: (i) explore associations between miRNA-related SNPs/DRVs and diseases; (ii) browse the effects of SNPs/DRVs on miRNA-target binding; (iii) functional enrichment analysis of miRNA target gain/loss caused by SNPs/DRVs; (iv) investigate correlations between drug sensitivity and miRNA expression; (v) inquire expression profiles of miRNAs and their targets in cancers; (vi) browse the effects of SNPs/DRVs on pre-miRNA secondary structure changes; and (vii) predict the effects of user-defined variations on miRNA-target binding or pre-miRNA secondary structure. miRNASNP-v3 is a valuable and long-term supported resource in functional variation screening and miRNA function studies.

Position-wise binding preference is important for miRNA target site prediction.

It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms. We developed a Markov model to characterize position-wise pairing patterns of miRNA-target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools. The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html. Supplementary data are available at Bioinformatics online.

Genomic Identification of RNA Editing Through Integrating Omics Datasets and the Clinical Relevance in Hepatocellular Carcinoma.

RNA editing is a widespread post-transcriptional mechanism to introduce single nucleotide changes to RNA in human cancers. Here, we characterized the global RNA editing profiles of 373 hepatocellular carcinoma (HCC) and 50 adjacent normal liver samples from The Cancer Genome Atlas (TCGA) and revealed that most editing events tend to occur in minor percentage of samples with moderate editing degrees (20–30%). Moreover, these RNA editing prefer to be A-to-I RNA editing in protein coding genes, especially in 3′UTR regions. Considering the association between DNA mutation and RNA editing, our analysis found that RNA editing maybe a complementary event for DNA mutation of HCC risk genes in HCC patients. We next identified 454 HCC-related editing sites, and many locate on the same genes with the same editing patterns. The functional consequences of editing revealed 2,086 functional editing sites and demonstrated that most editing in coding regions are non-synonymous variations. Furthermore, our results showed that editing in the 3′UTR regions tend to influence miRNA–target binding, and the editing degree seems to be negatively correlated with gene expression. Finally, we found that 46 HCC-related editing sites with consequence are able to distinguish the prognosis differences of HCC patients, suggesting their clinical relevance. Together, our results highlight RNA editing as a valuable molecular resource for investigating HCC mechanisms and clinical treatments.

Evaluation of polymorphisms in microRNA-binding sites and pancreatic cancer risk in Chinese population.

As promising biomarkers and therapy targets, microRNAs (miRNAs) are involved in various physiological and tumorigenic processes. Genetic variants in miRNA‐binding sites can lead to dysfunction of miRNAs and contribute to disease. However, systematic investigation of the miRNA‐related single nucleotide polymorphisms (SNPs) for pancreatic cancer (PC) risk remains elusive. We performed integrative bioinformatics analyses to select 31 SNPs located in miRNA‐target binding sites using the miRNASNP v2.0, a solid database providing miRNA‐related SNPs for genetic research, and investigated their associations with risk of PC in two large case‐control studies totally including 1847 cases and 5713 controls. We observed that the SNP rs3802266 is significantly associated with increased risk of PC (odds ratio (OR) = 1.21, 95% confidence intervals (CI) = 1.11‐1.31, P = 1.29E‐05). Following luciferase reporter gene assays show that rs3802266‐G creates a stronger binding site for miR‐181a‐2‐3p in 3′ untranslated region (3′UTR) of the gene ZHX2. Expression quantitative trait loci (eQTL) analysis suggests that ZHX2 expression is lower in individuals carrying rs3802266‐G with increased PC risk. In conclusion, our findings highlight the involvement of miRNA‐binding SNPs in PC susceptibility and provide new clues for PC carcinogenesis.

Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data

We perform a large-scale RNA sequencing study to experimentally identify genes that are downregulated by 25 miRNAs. This RNA-seq dataset is combined with public miRNA target binding data to systematically identify miRNA targeting features that are characteristic of both miRNA binding and target downregulation. By integrating these common features in a machine learning framework, we develop and validate an improved computational model for genome-wide miRNA target prediction. All prediction data can be accessed at miRDB (http://mirdb.org).

Accurate prediction of human miRNA targets via graph modeling of the miRNA-target duplex.

miRNAs are involved in many critical cellular activities through binding to their mRNA targets, e.g. in cell proliferation, differentiation, death, growth control, and developmental timing. Accurate prediction of miRNA targets can assist efficient experimental investigations on the functional roles of miRNAs. Their prediction, however, remains a challengeable task due to the lack of experimental data about the tertiary structure of miRNA-target binding duplexes. In particular, correlations of nucleotides in the binding duplexes may not be limited to the canonical Watson Crick base pairs (BPs) as they have been perceived; methods based on secondary structure prediction (typically minimum free energy (MFE)) have only had mix success. In this work, we characterized miRNA binding duplexes with a graph model to capture the correlations between pairs of nucleotides of an miRNA and its target sequences. We developed machine learning algorithms to train the graph model to predict the target sites of miRNAs. In particular, because imbalance between positive and negative samples can significantly deteriorate the performance of machine learning methods, we designed a novel method to re-sample available dataset to produce more informative data learning process. We evaluated our model and miRNA target prediction method on human miRNAs and target data obtained from mirTarBase, a database of experimentally verified miRNA-target interactions. The performance of our method in target prediction achieved a sensitivity of 86% with a false positive rate below 13%. In comparison with the state-of-the-art methods miRanda and RNAhybrid on the test data, our method outperforms both of them by a significant margin. The source codes, test sets and model files all are available at http://rna-informatics.uga.edu/?f=software&p=GraB-miTarget .

The deterministic role of 5-mers in microRNA-gene targeting

ABSTRACTMiRNAs play a central role in physiological and pathological processes. Both for the biological understanding and for their clinical application, it is essential to understand the interaction of miRNAs and their targets. Target identification largely hinges on in-silico prediction, which requires a complete consideration of miRNA binding sites within the UTRs of target genes. Here, we show that 5-mer sites might also play an essential role for human miRNA-target binding. We implemented and employed an algorithm to all pairs of 2,588 human miRNAs annotated in miRBase and the 3′ UTRs of 16725 genes (>43 million combinations). Our in-silico analysis showed a highly significant enrichment (p = 1.4 × 10−69) of 5-mer binding sites in 3′ UTRs across all experimentally validated miRNA-target gene pairs. We next confirmed the central role of 5-mer binding sites by reporter assays and demonstrated that two non-canonical 5-mer sites of miR-34a in the 3′ UTR of T-cell receptor alpha (TCRA) have a significantly stronger influence on its posttranscriptional regulation than the canonical binding sites. These observations indicate an essential role of 5-mer binding sites for the miRNA targeting in human cells.