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Untouchable genes in the human genome: Identifying ideal targets for cancer treatment.
Cancer Genet ; 231-232: 67-79, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30803560
ABSTRACT

BACKGROUND:

Usually, genes with a higher-than-expected number of somatic mutations in tumor samples are assumed to be cancer related. We identified genes with a fewer-than-expected number of somatic mutations - "untouchable genes".

METHODS:

To predict the expected number of somatic mutations, we used a linear regression model with the number of mutations in the gene as an outcome, and gene characteristics, including gene size, nucleotide composition, level of evolutionary conservation, expression level and others, as predictors. Analysis of residuals from the regression model was used to compare the observed and predicted number of mutations.

RESULTS:

We have identified 19 genes with a less-than-expected number of loss-off-function (nonsense, frameshift or pathogenic missense) mutations - i.e., untouchable genes. The number of silent or neutral missense mutations in untouchable genes was equal or higher than the expected number. Many mucins, including MUC16, MUC17, MUC6, MUC5AC, MUC5B, and MUC12, are untouchable. We hypothesized that untouchable mucins help tumor cells to avoid immune response by providing a protective coat that prevents direct contact between effector immune cells, e.g., cytotoxic T-cells, and tumor cells. Survival analysis of available TCGA data demonstrated that overall survival of patients with low (below the median) expression of untouchable mucins was better compared to patients with high expression of untouchable mucins. Aside from mucins, we have identified a number of other untouchable genes.

CONCLUSIONS:

Untouchable genes may be ideal targets for cancer treatment since suppression of untouchable genes is expected to inhibit survival of tumor cells.

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Aspecto clínico: Predição / Prognóstico Idioma: Inglês Revista: Cancer Genet Ano de publicação: 2019 Tipo de documento: Artigo