Survival marker genes of colorectal cancer derived from consistent transcriptomic profiling

[EN]Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumo...

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Detalles Bibliográficos
Autores: Martinez-Romero, Jorge, Bueno Fortes, Santiago, Martín-Merino Acera, Manuel, Ramírez de Molina, Ana, Rivas Sanz, Javier de las
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/146462
Acceso en línea:http://hdl.handle.net/10366/146462
Access Level:acceso abierto
Palabra clave:Cancer
Colorectal cancer
Colon
Survival
Kaplan-Meier analysis
Gene marker
Bioinformatics
Transcriptomics
Gene Expression
Kaplan-Meier Estimate
Colorectal Neoplasms
3201.01 Oncología
estimación de Kaplan-Meier
colon
neoplasias colorrectales
síntomas de cáncer
Descripción
Sumario:[EN]Identification of biomarkers associated with the prognosis of different cancer subtypes is critical to achieve better therapeutic assistance. In colorectal cancer (CRC) the discovery of stable and consistent survival markers remains a challenge due to the high heterogeneity of this class of tumors.We built an integrated dataset with 1273 human colorectal samples, which provides a homogeneous robust framework to analyse genome-wide expression and survival data. Using this dataset we identified two sets of genes that are candidate prognostic markers for CRC in stages III and IV, showing either up-regulation correlated with poor prognosis or up-regulation correlated with good prognosis.Finally, the set of top 100 genes that showed overexpression correlated with low survival was used to build a CRC risk predictor applying a multivariate Cox proportional hazards regression analysis.