Dataset Information

Identification of new p53 target microRNAs by bioinformatics and functional analysis.

ABSTRACT:

Background

The tumor suppressor p53 is a sequence-specific transcription factor that regulates an extensive network of coding genes, long non-coding RNAs and microRNAs, that establish intricate gene regulatory circuits influencing many cellular responses beyond the prototypical control of cell cycle, apoptosis and DNA repair.

Methods

Using bioinformatic approaches, we identified an additional group of candidate microRNAs (miRs) under direct p53 transcriptional control. To validate p53 family-mediated responsiveness of the newly predicted target miRs we first evaluated the potential for wild type p53, p63? and p73? to transactivate from p53 response elements (REs) mapped in the miR promoters, using an established yeast-based assay.

Results

The REs found in miR-10b, -23b, -106a, -151a, -191, -198, -202, -221, -320, -1204, -1206 promoters were responsive to p53 and 8 of them were also responsive to p63? or p73?. The potential for germline p53 mutations to drive transactivation at selected miR-associated REs was also examined. Chromatin Immuno-Precipitation (ChIP) assays conducted in doxorubicin-treated MCF7 cells and HCT116 p53+/+ revealed moderate induction of p53 occupancy at the miR-202, -1204, -1206, -10b RE-containing sites, while weak occupancy was observed for the miR-23b-associated RE only in MCF7 cells. RT-qPCR analyses cells showed modest doxorubicin- and/or Nutlin-dependent induction of the levels of mature miR-10b, -23b, -151a in HCT116 p53+/+ and MCF7 cells. The long noncoding RNA PVT1 comprising miR-1204 and -1206 was weakly induced only in HCT116 p53+/+ cells, but the mature miRs were not detected. miR-202 expression was not influenced by p53-activating stimuli in our cell systems.

Conclusions

Our study reveals additional miRs, particularly miR-10b and miR-151a, that could be directly regulated by the p53-family of transcription factors and contribute to the tuning of p53-induced responses.

SUBMITTER: Bisio A

PROVIDER: S-EPMC4225545 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of new p53 target microRNAs by bioinformatics and functional analysis.

Bisio Alessandra A De Sanctis Veronica V Del Vescovo Valerio V Denti Michela A MA Jegga Anil G AG Inga Alberto A Ciribilli Yari Y

BMC cancer 20131121

<h4>Background</h4>The tumor suppressor p53 is a sequence-specific transcription factor that regulates an extensive network of coding genes, long non-coding RNAs and microRNAs, that establish intricate gene regulatory circuits influencing many cellular responses beyond the prototypical control of cell cycle, apoptosis and DNA repair.<h4>Methods</h4>Using bioinformatic approaches, we identified an additional group of candidate microRNAs (miRs) under direct p53 transcriptional control. To validate ...[more]

PMID: 24256616

Similar Datasets

Project description:Purpose: Recurrent implantation failure (RIF) is an enormous challenge for in vitro fertilization (IVF) clinicians. An understanding of the molecular mechanisms of RIF helps to predict prognosis and develop new therapeutic strategies. The study is designed to identify diagnostic biomarkers for RIF as well as the potential mechanisms underlying RIF by utilizing public databases together with experimental validation. Methods: Two microarray datasets of RIF patients and the healthy control endometrium were downloaded from the Gene Expression Omnibus (GEO) database. First, differentially expressed microRNAs (miRNAs) (DEMs) were identified and their target genes were predicted. Then, we identified differentially expressed genes (DEGs) and selected hub genes through protein-protein interaction (PPI) analyses. Functional enrichment analyses of DEGs and DEMs were conducted. Furthermore, the key DEMs which targeted these hub genes were selected to obtain the key miRNA-target gene network. The key genes in the miRNA-target gene network were validated by a single-cell RNA-sequencing dataset of endometrium from GEO. Finally, we selected two miRNA-target gene pairs for further experimental validation using dual-luciferase assay and quantitative polymerase chain reaction (qPCR). Results: We identified 49 DEMs between RIF patients and the fertile group and found 136,678 target genes. Then, 325 DEGs were totally used to construct the PPI network, and 33 hub genes were selected. Also, 25 DEMs targeted 16 key DEGs were obtained to establish a key miRNA-target gene network, and 16 key DEGs were validated by a single-cell RNA-sequencing dataset. Finally, the target relationship of hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 was verified by dual-luciferase assay, and there were significant differences in the expression of those genes between the RIF and fertile group by PCR (p < 0.05). Conclusion: We constructed miRNA-target gene regulatory networks associated with RIF which provide new insights regarding the underlying pathogenesis of RIF; hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 could be further explored as potential biomarkers for RIF, and their detection in the endometrium could be applied in clinics to estimate the probability of successful embryo transfer.

Project description:Purpose: The competing endogenous RNA (ceRNA) network regulatory has been investigated in the occurrence and development of many diseases. This research aimed at identifying the key RNAs of ceRNA network in pterygium and exploring the underlying molecular mechanism.Methods: Differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs were obtained from the Gene Expression Omnibus (GEO) database and analyzed with the R programming language. LncRNA and miRNA expressions were extracted and pooled by the GEO database and compared with those in published literature. The lncRNA-miRNA-mRNA network was constructed of selected lncRNAs, miRNAs, and mRNAs. Metascape was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on mRNAs of the ceRNA network and to perform Protein-Protein Interaction (PPI) Network analysis on the String website to find candidate hub genes. The Comparative Toxicogenomic Database (CTD) was used to find hub genes closely related to pterygium. The differential expressions of hub genes were verified using the reverse transcription-real-time fluorescent quantitative PCR (RT-qPCR).Result: There were 8 lncRNAs, 12 miRNAs, and 94 mRNAs filtered to construct the primary ceRNA network. A key lncRNA LIN00472 ranking the top 1 node degree was selected to reconstruct the LIN00472 network. The GO and KEGG pathway enrichment showed the mRNAs in ceRNA networks mainly involved in homophilic cell adhesion via plasma membrane adhesion molecules, developmental growth, regulation of neuron projection development, cell maturation, synapse assembly, central nervous system neuron differentiation, and PID FOXM1 PATHWAY. According to the Protein-Protein Interaction Network (PPI) analysis on mRNAs in LINC00472 network, 10 candidate hub genes were identified according to node degree ranking. Using the CTD database, we identified 8 hub genes closely related to pterygium; RT-qPCR verified 6 of them were highly expressed in pterygium.Conclusion: Our research found LINC00472 might regulate 8 hub miRNAs (miR-29b-3p, miR-183-5p, miR-138-5p, miR-211-5p, miR-221-3p, miR-218-5p, miR-642a-5p, miR-5000-3p) and 6 hub genes (CDH2, MYC, CCNB1, RELN, ERBB4, RB1) in the ceRNA network through mainly PID FOXM1 PATHWAY and play an important role in the development of pterygium.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the most complicated and fatally pathogenic human malignancies. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of PDAC. The aim of the present study was to identify the key genes and signaling pathways associated with PDAC using bioinformatics analysis. Four transcriptome microarray datasets (GSE15471, GSE55643, GSE62165 and GSE91035) were acquired from Gene Expression Omnibus datasets, which included 226 PDAC samples and 65 normal pancreatic tissue samples. We screened differentially expressed genes (DEGs) with GEO2R and investigated their biological function by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. The overall survival data was obtained from UALCAN, which calculated the data shared with The Cancer Genome Atlas. In addition, a protein-protein interaction (PPI) network of the DEGs was constructed by STRING and Cytoscape software. The four sets of DEGs exhibited an intersection consisting of 205 genes (142 up-regulated and 63 down-regulated), which may be associated with PDAC. GO analysis showed that the 205 DEGs were significantly enriched in the plasma membrane, cell adhesion molecule activity and the Energy pathways, and glycine, serine, threonine metabolism were the most enriched pathways according to KEGG pathway analysis. Kaplan-Meier survival analysis revealed that 22 of 205 common genes were significantly associated with the overall survival of pancreatic cancer patients. In the PPI network and sub-network, DKK1 and HMGA2 were considered as hub genes with high connectivity degrees. DKK1 and HMGA2 are strongly associated with WNT3A and TP53 separately, which indicates that they may play an important role in the Wnt and P53 signaling pathways. Using integrated bioinformatics analysis, we identified DKK1 and HMGA2 as candidate genes in PDAC, which may improve our understanding of the mechanisms of the pathogenesis and integration; the two genes may be therapeutic targets and prognostic markers for PDAC.

Dataset Information

Identification of new p53 target microRNAs by bioinformatics and functional analysis.

Background

Methods

Results

Conclusions

Publications

Identification of new p53 target microRNAs by bioinformatics and functional analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets