Computational analysis of the mutations in BAP1, PBRM1 and SETD2 genes reveals the impaired molecular processes in renal cell carcinoma
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Francesco Piva1,*, Matteo Giulietti1,*, Giulia Occhipinti1, Matteo Santoni2, Francesco Massari3, Valeria Sotte2, Roberto Iacovelli4, Luciano Burattini2, Daniele Santini5, Rodolfo Montironi6, Stefano Cascinu2, Giovanni Principato1
1Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche Region, Ancona, Italy
2Department of Medical Oncology, AOU Ospedali Riuniti – Polytechnic University of the Marche Region, Ancona, Italy
3Department of Medical Oncology, University of Verona, Verona, Italy
4Medical Oncology Unit of Urogenital and Head & Neck Tumors, European Institute of Oncology, Milan, Italy
5Department of Medical Oncology, Campus Bio-Medico University of Rome, Rome, Italy
6Pathological Anatomy, Polytechnic University of the Marche Region School of Medicine United Hospitals, Ancona, Italy
*These authors have contributed equally to this work
Francesco Piva, e-mail: firstname.lastname@example.org
Keywords: mutations, polymorphisms, predictions, RCC, computational
Received: July 15, 2015 Accepted: August 12, 2015 Published: October 07, 2015
Clear cell Renal Cell Carcinoma (ccRCC) is due to loss of von Hippel–Lindau (VHL) gene and at least one out of three chromatin regulating genes BRCA1-associated protein-1 (BAP1), Polybromo-1 (PBRM1) and Set domain-containing 2 (SETD2). More than 350, 700 and 500 mutations are known respectively for BAP1, PBRM1 and SETD2 genes. Each variation damages these genes with different severity levels. Unfortunately for most of these mutations the molecular effect is unknown, so precluding a severity classification. Moreover, the huge number of these gene mutations does not allow to perform experimental assays for each of them. By bioinformatic tools, we performed predictions of the molecular effects of all mutations lying in BAP1, PBRM1 and SETD2 genes. Our results allow to distinguish whether a mutation alters protein function directly or by splicing pattern destruction and how much severely. This classification could be useful to reveal correlation with patients’ outcome, to guide experiments, to select the variations that are worth to be included in translational/association studies, and to direct gene therapies.
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