ArticleMolecular medicine reports2018
Competing endogenous RNA regulatory network in papillary thyroid carcinoma.
Article in Molecular medicine reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Screening differentially expressed proteins to distinguish thymoma (B1 and B3) from thymic cysts based on tandem mass tag (TMT) technology.Journal of cardiothoracic surgery · 2024Article
- Emerging landscape of circHIPK3 and its role in cancer and other diseases (Review).Molecular medicine reports · 2021Review
- Construction and analysis of an aberrant lncRNA-miRNA-mRNA network associated with papillary thyroid cancer.Medicine · 2020Article
- Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma.Oncology letters · 2020Article
- A susceptibility biomarker identification strategy based on significantly differentially expressed ceRNA triplets for ischemic cardiomyopathy.Bioscience reports · 2020Article
- Proto-oncogene RTL4 promotes tumorigenesis and invasiveness of papillary thyroid cancer.American journal of translational research · 2020Article
- Identification of key miRNAs in papillary thyroid carcinoma based on data mining and bioinformatics methods.Biomedical reports · 2020Article
- The Role of Long Non-Coding RNAs in Thyroid Cancer.Frontiers in oncology · 2020Review
- Construction of a Competitive Endogenous RNA Network in Uterine Corpus Endometrial Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- Gene Expression Patterns Unveil New Insights in Papillary Thyroid Cancer.Medicina (Kaunas, Lithuania) · 2019Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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