Title |
High-throughput sequencing of small RNA transcriptomes reveals critical biological features targeted by microRNAs in cell models used for squamous cell cancer research
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Published in |
BMC Genomics, October 2013
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DOI | 10.1186/1471-2164-14-735 |
Pubmed ID | |
Authors |
Patricia Severino, Liliane Santana Oliveira, Natalia Torres, Flavia Maziero Andreghetto, Maria de Fatima Guarizo Klingbeil, Raquel Moyses, Victor Wünsch-Filho, Fabio Daumas Nunes, Monica Beatriz Mathor, Alexandre Rossi Paschoal, Alan Mitchell Durham |
Abstract |
The implication of post-transcriptional regulation by microRNAs in molecular mechanisms underlying cancer disease is well documented. However, their interference at the cellular level is not fully explored. Functional in vitro studies are fundamental for the comprehension of their role; nevertheless results are highly dependable on the adopted cellular model. Next generation small RNA transcriptomic sequencing data of a tumor cell line and keratinocytes derived from primary culture was generated in order to characterize the microRNA content of these systems, thus helping in their understanding. Both constitute cell models for functional studies of microRNAs in head and neck squamous cell carcinoma (HNSCC), a smoking-related cancer. Known microRNAs were quantified and analyzed in the context of gene regulation. New microRNAs were investigated using similarity and structural search, ab initio classification, and prediction of the location of mature microRNAs within would-be precursor sequences. Results were compared with small RNA transcriptomic sequences from HNSCC samples in order to access the applicability of these cell models for cancer phenotype comprehension and for novel molecule discovery. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 50% |
France | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 50% |
Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
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Chile | 1 | 2% |
India | 1 | 2% |
Unknown | 42 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 10 | 23% |
Student > Ph. D. Student | 8 | 18% |
Student > Master | 7 | 16% |
Student > Bachelor | 4 | 9% |
Other | 4 | 9% |
Other | 6 | 14% |
Unknown | 5 | 11% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 13 | 30% |
Agricultural and Biological Sciences | 11 | 25% |
Biochemistry, Genetics and Molecular Biology | 5 | 11% |
Engineering | 3 | 7% |
Computer Science | 3 | 7% |
Other | 3 | 7% |
Unknown | 6 | 14% |