ArticleOncology letters2016
Identification of potential therapeutic targets for papillary thyroid carcinoma by bioinformatics analysis.
Article in Oncology letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- S100 proteins in head and neck squamous cell carcinoma (Review).Oncology letters · 2023Review
- Reprotoxic Effect of Tris(2,3-Dibromopropyl) Isocyanurate (TBC) on Spermatogenic Cells In Vitro.Molecules (Basel, Switzerland) · 2023Article
- RAGE Inhibitors for Targeted Therapy of Cancer: A Comprehensive Review.International journal of molecular sciences · 2022Review
- Article
- LncRNA NORAD promotes thyroid carcinoma progression through targeting miR-202-5p.American journal of translational research · 2019Article
- Genetic expression profile-based screening of genes and pathways associated with papillary thyroid carcinoma.Oncology letters · 2018Article
- S100A6 protein: functional roles.Cellular and molecular life sciences : CMLS · 2017Review
- MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search.Nucleic acids research · 2017Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the present study was to identify potential therapeutic targets for papillary thyroid carcinoma (PTC) and to investigate the possible mechanism underlying this disease. The gene expression profile, GSE53157, was downloaded from the Gene Expression Omnibus database. Only 10 chips, including 3 specimens of normal thyroid tissues and 7 specimens of well-differentiated thyroid carcinomas, were analyzed in the present study. Differentially-expressed genes (DEGs) between PTC patients and normal individuals were identified. Next, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of DEGs were performed. Modules in the protein-protein interaction (PPI) network were identified. Significant target genes were selected from the microRNA (miRNA) regulatory network. Furthermore, the integrated network was constructed with the miRNA regulatory and PPI network modules, and key target genes were screened. A total of 668 DEGs were identified. Modules M1, M2 and M3 were identified from the PPI network. From the modules, DEGs of cyclin-dependent kinase inhibitor 1A, S100 calcium binding protein A6 (S100A6), dual specificity phosphatase 5, keratin 19, met proto-oncogene (MET) and lectin galactoside-binding soluble 3 were included in the Malacards database. In the miRNA regulatory and integrated networks, genes of cyclin-dependent kinase inhibitor 1C (CDKN1C), peroxisome proliferator-activated receptor γ, aryl hydrocarbon receptor, basic helix-loop-helix family, member e40 and reticulon 1 were the key target genes. S100A6, MET and CDKN1C may exhibit key roles in the progression and development of PTC, and may be used as specific therapeutic targets in the treatment of PTC. However, further experiments are required to confirm these results.
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