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Characterization of Wild-Type and Mutated RET Proto-Oncogene Associated with Familial Medullary Thyroid Cancer
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  • Characterization of Wild-Type and Mutated RET Proto-Oncogene Associated with Familial Medullary Thyroid Cancer
  • Characterization of Wild-Type and Mutated RET Proto-Oncogene Associated with Familial Medullary Thyroid Cancer
저자명
Masbi. Mohammad Hosein,Mohammadiasl. Javad,Galehdari. Hamid,Ahmadzadeh. Ahmad,Tabatabaiefar. Mohammad Amin,Golchin. Neda,Haghpan
간행물명
Asian Pacific journal of cancer prevention : APJCP
권/호정보
2014년|15권 5호|pp.2027-2033 (7 pages)
발행정보
아시아태평양암예방학회
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정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

Background: We aimed to assess RET proto-oncogene polymorphisms in three different Iranian families with medullary thyroid cancer (MTC), and performed molecular dynamics simulations and free energy stability analysis of these mutations. Materials and Methods: This study consisted of 48 patients and their first-degree relatives with MTC confirmed by pathologic diagnosis and surgery. We performed molecular dynamics simulations and free energy stability analysis of mutations, and docking evaluation of known RET proto-oncogene inhibitors, including ZD-6474 and ponatinib, with wild-type and mutant forms. Results: The first family consisted of 27 people from four generations, in which nine had the C.G2901A (P.C634Y) mutation; the second family consisted of six people, of whom three had the C.G2901T (P.C634F) mutation, and the third family, who included 12 individuals from three generations, three having the C.G2251A (P.G691S) mutation. The automated 3D structure of RET protein was predicted using I-TASSER, and validated by various protein model verification programs that showed more than 96.3% of the residues in favored and allowed regions. The predicted instability indices of the mutated structures were greater than 40, which reveals that mutated RET protein is less thermo-stable compared to the wild-type form (35.4). Conclusions: Simultaneous study of the cancer mutations using both in silico and medical genetic procedures, as well as onco-protein inhibitor binding considering mutation-induced drug resistance, may help in better overcoming chemotherapy resistance and designing innovative drugs.