@entity2531 mutation accumulation is a major cause of abnormal cell growth. However, some mutations in @entity5 cells may be deleterious to the survival and proliferation of the @entity5 cells, thus offering a protective effect to the @entity1 . We investigated this hypothesis via a unique analysis of the clinical and somatic mutation datasets of @entity43 published by the @entity5 . We defined and screened 562 macro mutation signatures (MMSs) for their associations with the overall survival of 320 @entity43 @entity1 . Each MMS measures the number of mutations present on the member genes (except for @entity876 ) covered by a specific Gene Ontology (GO) term in each @entity5 . We found that somatic mutations favorable to the @entity1 survival are predominant in @entity43 compared to those indicating poor clinical outcomes. Specially, we identified 19 (3) predictive MMSs that are, usually by a nonlinear dose-dependent effect, associated with good (poor) @entity1 survival. The false discovery rate for the 19 "positive" predictors is at the level of 0.15. The GO terms corresponding to these MMSs include "lysosomal membrane" and "response to @entity519 ", each of which is relevant to the progression and therapy of @entity5 . Using these MMSs as features, we established a classification tree model which can effectively partition the training samples into three prognosis groups regarding the survival time. We validated this model on an independent dataset of the same disease (Log-rank p-value < 2.3 * 10(-4)) and a dataset of @entity0 (Log-rank p-value < 9.3 * 10(-3)). We compared the GO terms corresponding to these MMSs and those enriched with expression-based predictive genes. The analysis showed that the GO term pairs with large similarity are mainly pertinent to the proteins located on the cell organelles responsible for material transport and waste disposal, suggesting the crucial role of these proteins in @entity5 mortality.

XXXX mutations favorable to @entity1 survival are predominant in @entity43 .

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity43 :: ('MESH:D010051', 'Disease') :: ['ovarian carcinomas', 'ovarian cancer'] @entity0 :: ('MESH:D001943', 'Disease') :: ['breast cancer'] @entity5 :: ('MESH:D009369', 'Disease') :: ['Cancer Genome Atlas', 'tumor', 'cancer'] @entity876 :: ('7157', 'Gene') :: ['TP53'] @entity519 :: ('MESH:D000860', 'Disease') :: ['hypoxia'] @entity2531 :: ('MESH:D013001', 'Disease') :: ['Somatic']
@entity2531 :: (MESH:D013001,Disease) :: ['Somatic']
