Dataset Information

Construction and analyses of the microRNA-target gene differential regulatory network in thyroid carcinoma.

ABSTRACT: Thyroid-carcinoma (THCA) is the most common malignancy with an increasing incidence. Recent evidence has emphasized the role of microRNA (miRNA) in THCA. However, knowledge concerning the roles of miRNAs in THCA is still limited. We therefore use a miRNA-target gene differential regulatory network (MGDRN) to identify key miRNAs and characterize their synergistic regulation in THCA. Both miRNA-target gene interactions from multiple databases and negative expression correlations between miRNA-target genes were used to characterize the interactions. Then, two regulatory networks involving normal and tumor conditions were constructed, respectively. The MGDRN was finally constructed using different interactions between the above two regulatory networks. By analyzing topological features of the MGDRN, four miRNAs (hsa-mir-152-3p, hsa-mir-148a, hsa-mir-130b and hsa-mir-15b) are identified as key miRNAs in THCA. Over-expression of mir-152-3p inhibited proliferation and colony formation of TPC-1 cells. Furthermore, mir-152-3p negatively regulated ERBB3 by binding to the 3'-UTR of ERBB3, and down-regulation of ERBB3 by small interfering (si)RNAs inhibited proliferation and colony formation of TPC-1 cells, indicating that mir-152-3p acted as an anti-tumor miRNA by negatively regulating ERBB3. Finally, two synergistically dysregulated modules were identified which may contribute to the initiation and progression of THCA. Overall, the results provided a better understanding of the molecular basis of THCA, and suggested novel treatment strategies for this cancer.

SUBMITTER: Kang YY

PROVIDER: S-EPMC5453480 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Construction and analyses of the microRNA-target gene differential regulatory network in thyroid carcinoma.

Kang Ying-Ying YY Liu Yang Y Wang Ming-Li ML Guo Min M Wang Yan Y Cheng Zhi-Feng ZF

PloS one 20170601 6

Thyroid-carcinoma (THCA) is the most common malignancy with an increasing incidence. Recent evidence has emphasized the role of microRNA (miRNA) in THCA. However, knowledge concerning the roles of miRNAs in THCA is still limited. We therefore use a miRNA-target gene differential regulatory network (MGDRN) to identify key miRNAs and characterize their synergistic regulation in THCA. Both miRNA-target gene interactions from multiple databases and negative expression correlations between miRNA-targ ...[more]

PMID: 28570571

Similar Datasets

Project description:BackgroundCold is one of the main abiotic stresses that severely affect plant growth and development, and crop productivity as well. Transcriptional changes during cold stress have already been intensively studied in various plant species. However, the gene networks involved in the regulation of differential cold tolerance between tobacco varieties with contrasting cold resistance are quite limited.ResultsHere, we conducted multiple time-point transcriptomic analyses using Tai tobacco (TT, cold susceptibility) and Yan tobacco (YT, cold resistance) with contrasting cold responses. We identified similar DEGs in both cultivars after comparing with the corresponding control (without cold treatment), which were mainly involved in response to abiotic stimuli, metabolic processes, kinase activities. Through comparison of the two cultivars at each time point, in contrast to TT, YT had higher expression levels of the genes responsible for environmental stresses. By applying Weighted Gene Co-Expression Network Analysis (WGCNA), we identified two main modules: the pink module was similar while the brown module was distinct between the two cultivars. Moreover, we obtained 100 hub genes, including 11 important transcription factors (TFs) potentially involved in cold stress, 3 key TFs in the brown module and 8 key TFs in the pink module. More importantly, according to the genetic regulatory networks (GRNs) between TFs and other genes or TFs by using GENIE3, we identified 3 TFs (ABI3/VP1, ARR-B and WRKY) mainly functioning in differential cold responses between two cultivars, and 3 key TFs (GRAS, AP2-EREBP and C2H2) primarily involved in cold responses.ConclusionCollectively, our study provides valuable resources for transcriptome- based gene network studies of cold responses in tobacco. It helps to reveal how key cold responsive TFs or other genes are regulated through network. It also helps to identify the potential key cold responsive genes for the genetic manipulation of tobacco cultivars with enhanced cold tolerance in the future.

Project description:The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

Dataset Information

Construction and analyses of the microRNA-target gene differential regulatory network in thyroid carcinoma.

Publications

Construction and analyses of the microRNA-target gene differential regulatory network in thyroid carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets