Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.
Ontology highlight
ABSTRACT:
SUBMITTER: Shao M
PROVIDER: S-EPMC4414601 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
Publications
Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.
Shao Mingyu M Sun Yanni Y Zhou Shuigeng S
PloS one 20150429 4
MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying can ...[more]
Similar Datasets
Project description:BackgroundIdentifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.ResultsIn this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: http://nugget.unisa.edu.au/Thuc/cancersubtypes/.
| S-EPMC4818025 | biostudies-literature
Project description:Nitidine chloride (NC) has reported tumor suppressive activities for various human cancers, including hepatocellular carcinoma (HCC). Nevertheless, the pharmacological mechanism of NC on HCC has not previously been elucidated. SMMC7721 HCC cell lines, before and after the treatment of NC, were injected into nude mice for a subcutaneous tumor xenograft model. MiRNA and mRNA sequencing were performed for both control and treated xenograft tissues to further analyze differential expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs). The ten most significant DEmiRNAs were selected for prediction of transcription factors (TFs) and target genes. We constructed an interconnected network composed of TFs the ten most significant DEmiRNAs, the 100 most significant DEmRNAs, and selected target genes from online programs. Hub genes chosen from a protein-to-protein interaction network of hub genes were validated by correlation analysis, expression analysis, and Kaplan-Meier survival analysis. The five most up-regulated miRNAs (hsa-miR-628-5p, hsa-miR-767-5p, hsa-miR-767-3p, hsa-miR-1257, and hsa-miR-33b-3p) and the five most down-regulated miRNAs (hsa-miR-378d, hsa-miR-136-5p, hsa-miR-451a, hsa-miR-144-5p, and hsa-miR-378b) were singled out from the DEmiRNAs. Functional annotations indicated that potential target genes of the top five up-regulated miRNAs were mainly clustered in molecular processes concerning epithelial-to-mesenchymal transition. Hub genes, such as ITGA6 and ITGB4, were validated as up-regulated in HCC; both IFIT2 and IFIT3 were revealed by Kaplan-Meier survival curves as good prognostic factors for HCC. In summary, the regulating axes of NC-DEmiRNAs-DEmRNAs and TFs-DEmiRNAs-DEmRNAs in HCC that were discovered in this study may shed light on the possible molecular mechanism of NC in HCC.
| S-EPMC6943467 | biostudies-literature
Project description:BackgroundmicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments.ResultsIn this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method.ConclusionsThis research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.
| S-EPMC6419852 | biostudies-literature
Project description:BACKGROUND:Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test. RESULTS:n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network. CONCLUSIONS:Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.
| S-EPMC6933624 | biostudies-literature
Project description:BackgroundMicroRNA (miRNA) sponges with multiple tandem miRNA binding sequences can sequester miRNAs from their endogenous target mRNAs. Therefore, miRNA sponge acting as a decoy is extremely important for long-term loss-of-function studies both in vivo and in silico. Recently, a growing number of in silico methods have been used as an effective technique to generate hypotheses for in vivo methods for studying the biological functions and regulatory mechanisms of miRNA sponges. However, most existing in silico methods only focus on studying miRNA sponge interactions or networks in cancer, the module-level properties of miRNA sponges in cancer is still largely unknown.ResultsWe propose a novel in silico method, called miRSM (miRNA Sponge Module) to infer miRNA sponge modules in breast cancer. We apply miRSM to the breast invasive carcinoma (BRCA) dataset provided by The Cancer Genome Altas (TCGA), and make functional validation of the computational results. We discover that most miRNA sponge interactions are module-conserved across two modules, and a minority of miRNA sponge interactions are module-specific, existing only in a single module. Through functional annotation and differential expression analysis, we also find that the modules discovered using miRSM are functional miRNA sponge modules associated with BRCA. Moreover, the module-specific miRNA sponge interactions among miRNA sponge modules may be involved in the progression and development of BRCA. Our experimental results show that miRSM is comparable to the benchmark methods in recovering experimentally confirmed miRNA sponge interactions, and miRSM outperforms the benchmark methods in identifying interactions that are related to breast cancer.ConclusionsAltogether, the functional validation results demonstrate that miRSM is a promising method to identify miRNA sponge modules and interactions, and may provide new insights for understanding the roles of miRNA sponges in cancer progression and development.
| S-EPMC5374547 | biostudies-literature
Project description:Gastric cancer (GC), as an epidemic cancer worldwide, has more than 1 million new cases and an estimated 769,000 deaths worldwide in 2020, ranking fifth and fourth in global morbidity and mortality. In mammals, both miRNAs and transcription factors (TFs) play a partial role in gene expression regulation. The mRNA expression profile and miRNA expression profile of GEO database were screened by GEO2R for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Then, DAVID annotated the functions of DEGs to understand the functions played in biological processes. The prediction of potential target genes of miRNA and key TFs of mRNA was performed by mipathDB V2.0 and CHEA3, respectively, and the gene list comparison was performed to look for overlapping genes coregulated by key TFs and DEMs. Finally, the obtained miRNAs, TF, and overlapping genes were used to construct the miRNA-mRNA-TF regulatory network, which was verified by RT-qPCR. 76 upregulated DEGs, 199 downregulated DEGs, and 3 upregulated miRNAs (miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p) were identified from the expression profiles of mRNA (GSE26899, GSE29998, GSE51575, and GSE13911) and miRNA (GSE93415), respectively. Through database prediction and gene list comparison, it was found that among the 199 downregulated DEGs, 61, 71, and 69 genes were the potential targets of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p, respectively. 199 downregulated DEGs were used as the gene list for the prediction of key TFs, and the results showed that RFX6 ranked the highest. The potential target overlap genes of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p were 4 genes (SH3GL2, ATP4B, CTSE, and SORBS2), 7 genes (SLC7A8, RNASE4, ESRRG, PGC, MUC6, Fam3B, and FMO5), and 6 genes (CHGA, PDK4, TMPRSS2, CLIC6, GPX3, and PSCA), respectively. Finally, we constructed a miRNA-mRNA-TF regulatory network based on the above 17 mRNAs, 3 miRNAs, and 1 TF and verified by RT-qPCR and western blot results that the expression of RFX6 was downregulated in GC tissues. These identified miRNAs, mRNAs, and TF have a certain reference value for further exploration of the regulatory mechanism of GC.
| S-EPMC8616677 | biostudies-literature
Project description:Uterine fibroids (UF) are the most common benign gynecologic tumors and lead to heavy menstrual bleeding, severe anemia, abdominal pain, and infertility, which seriously harm a women's health. Unfortunately, the regulatory mechanisms of UF have not been elucidated. Recent studies have demonstrated that miRNAs play a vital role in the development of uterine fibroids. As a high-throughput technology, microarray is utilized to identify differentially expressed genes (DEGs) and miRNAs (DEMs) between UF and myometrium. We identified 373 candidate DEGs and the top 100 DEMs. Function enrichment analysis showed that candidate DEGs were mainly enriched in biological adhesion, locomotion and cell migration, and collagen-containing extracellular matrix. Subsequently, protein-protein interaction (PPI) networks are constructed to analyze the functional interaction between DEGs and screen hub DEGs. Subsequently, the expression levels of hub DEGs were validated by real-time PCR of clinical UF samples. The DGIdb database was used to select candidate drugs for hub DEGs. Molecular docking was applied to test the affinity between proteins and drugs. Furthermore, target genes for 100 candidate DEMs were predicted by miRwalk3.0. After overlapping with 373 candidate DEGs, 28 differentially expressed target genes (DEGTs) were obtained. A miRNA-mRNA network was constructed to investigate the interactions between miRNA and mRNA. Additionally, two miRNAs (hsa-miR-381-3p and hsa-miR-181b-5p) were identified as hub DEMs and validated through RT-PCR. In order to better elucidate the pathogenesis of UF and the synergistic effect between miRNA and transcription factor (TF), we constructed a miRNA-TF-mRNA regulatory network. Meanwhile, in vitro results suggested that dysregulated hub DEMs were associated with the proliferation, migration, and apoptosis of UF cells. Our findings provided a novel horizon to reveal the internal mechanism and novel targets for the diagnosis and treatment of UF.
| S-EPMC8981149 | biostudies-literature
Project description:Cancer metastasis is a disease of extreme clinical relevance, as it is responsible for more than 90% of cancer-associated mortality. The molecular mechanism and critical regulators involved in this complex multi-stage process of metastasis is poorly deciphered in soft tissue sarcomas (STS), a heterogeneous group of rare tumors with high metastatic potential. Therefore, we aimed at identifying miRNA and transcription factor (TF) regulatory networks and paths in STS metastasis. We integrated mRNA and miRNA expression profiles with curated regulations (TF→gene, TF→miRNA, miRNA→gene) from different databases and constructed a potentially active regulatory sub-network in STS metastasis. From functional and topological analysis, we found nine novel regulators of Notch signaling sub-network which are conjectured to play critical role in metastasis of STS. This illustrated that the sub-network is promising for identification of critical regulators. Further analysis deploying our developed tool 'RiNAcyc' and computing coverage ratio of known STS associated genes and miRNAs identified a 15 node active path. This potential path highlights the crucial role of BMP2, hsa-miR-24, AP2 and MYC as the up-stream regulators of the path and hsa-miR-215 and TYMS as potential indicator of chemotherapeutic benefit in STS metastasis.
| S-EPMC4434893 | biostudies-literature
Project description:Breast cancer is the most common cancer among women and the molecular pathways that play main roles in breast cancer regulation are still not completely understood. MicroRNAs (miRNAs) and transcription factors (TFs) are important regulators of gene expression. It is important to unravel the relation of TFs, miRNAs, and their targets within regulatory networks to clarify the processes that cause breast cancer and the progression of it. In this study, mRNA and miRNA expression studies including breast tumors and normal samples were extracted from the GEO microarray database. Two independent mRNA studies and a miRNA study were selected and reanalyzed. Differentially expressed (DE) mRNAs and miRNAs between breast tumor and normal samples were listed by using BRBArray Tools. CircuitsDB2 analysis conducted with DE miRNAs and mRNAs resulted in 3 significant circuits that are SOX10- and hsamiR-301a-dependent. The following significant circuits were characterized and validated bioinformatically by using web-based tools: SOX10→hsa-miR-301a→HOXA3, SOX10→hsa-miR-301a→KIT, and SOX10→hsa-miR-301a→NFIB. It can be concluded that regulatory motifs involving miRNAs and TFs may be useful for understanding breast cancer regulation and for predicting new biomarkers.
| S-EPMC6353269 | biostudies-literature
Project description:Myocardial infarction (MI) is a leading cause of death in the world and many genes are involved in it. Transcription factor (TFs) and microRNAs (miRNAs) are key regulators of gene expression. We hypothesized that miRNAs and TFs might play combinatory regulatory roles in MI. After collecting MI candidate genes and miRNAs from various resources, we constructed a comprehensive MI-specific miRNA-TF co-regulatory network by integrating predicted and experimentally validated TF and miRNA targets. We found some hub nodes (e.g. miR-16 and miR-26) in this network are important regulators, and the network can be severed as a bridge to interpret the associations of previous results, which is shown by the case of miR-29 in this study. We also constructed a regulatory network for MI recurrence and found several important genes (e.g. DAB2, BMP6, miR-320 and miR-103), the abnormal expressions of which may be potential regulatory mechanisms and markers of MI recurrence. At last we proposed a cellular model to discuss major TF and miRNA regulators with signaling pathways in MI. This study provides more details on gene expression regulation and regulators involved in MI progression and recurrence. It also linked up and interpreted many previous results.
| S-EPMC4394890 | biostudies-literature