Cancer Target Gene Screening: a web application for breast cancer target gene screening using multi-omics data analysis.
Brief Bioinform
; 21(2): 663-675, 2020 03 23.
Article
en En
| MEDLINE
| ID: mdl-30698638
ABSTRACT
Breast cancer comprises several molecular subtypes with distinct clinical features and treatment responses, and a substantial portion of each subtype remains incurable. A comprehensive analysis of multi-omics data and clinical profiles is required in order to better understand the biological complexity of this cancer type and to identify new prognostic and therapeutic markers. Thus, there arises a need for useful analytical tools to assist in the investigation and clinical management of the disease. We developed Cancer Target Gene Screening (CTGS), a web application that provides rapid and user-friendly analysis of multi-omics data sets from a large number of primary breast tumors. It allows the investigation of genomic and epigenomic aberrations, evaluation of transcriptomic profiles and performance of survival analyses and of bivariate correlations between layers of omics data. Notably, the genome-wide screening function of CTGS prioritizes candidate genes of clinical and biological significance among genes with copy number alteration, DNA methylation and dysregulated expression by the integrative analysis of different types of omics data in customized subgroups of breast cancer patients. These features may help in the identification of druggable cancer driver genes in a specific subtype or the clinical condition of human breast cancer. CTGS is available at http//ctgs.biohackers.net.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
2_ODS3
Problema de salud:
2_muertes_prematuras_enfermedades_notrasmisibles
Asunto principal:
Neoplasias de la Mama
/
Pruebas Genéticas
/
Internet
/
Genómica
/
Proteómica
/
Transcriptoma
Tipo de estudio:
Diagnostic_studies
/
Screening_studies
Límite:
Female
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Humans
Idioma:
En
Revista:
Brief Bioinform
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article