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.
Piva F., Giulietti M., Occhipinti G., Santoni M., Massari F., Sotte V., et al. (2015). Computational analysis of the mutations in BAP1, PBRM1 and SETD2 genes reveals the impaired molecular processes in Renal Cell Carcinoma. ONCOTARGET, 6(31), 32161-32168 [10.18632/oncotarget.5147].
Computational analysis of the mutations in BAP1, PBRM1 and SETD2 genes reveals the impaired molecular processes in Renal Cell Carcinoma
Massari F.;
2015
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
