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- W2900418488 abstract "Abstract Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored." @default.
- W2900418488 created "2018-11-16" @default.
- W2900418488 creator A5006332127 @default.
- W2900418488 creator A5013497733 @default.
- W2900418488 creator A5020444039 @default.
- W2900418488 creator A5029383178 @default.
- W2900418488 creator A5040406602 @default.
- W2900418488 creator A5058919066 @default.
- W2900418488 creator A5062687794 @default.
- W2900418488 creator A5076771459 @default.
- W2900418488 creator A5091734590 @default.
- W2900418488 date "2018-11-12" @default.
- W2900418488 modified "2023-10-18" @default.
- W2900418488 title "Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis" @default.
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