RESUMEN
Objetivo: Analizar los perfiles de expresión génica del cáncer de próstata (CaP) e identificar los genes diferencialmente expresados. Determinar si la expresión diferencial en tejido se mantiene en muestras de orina-posmasaje prostático (PMP). Material y métodos: Un total de 46 muestras de tejido prostático (36 de pacientes con CaP y 10 controles) y 158 orinas-PMP (113 de pacientes con CaP y 45 controles) se recogieron entre diciembre de 2003 y mayo de 2007. Se utilizaron microarrays de ADN para identificar los genes diferencialmente expresados entre las muestras de tejido tumorales y las controles. Diez genes fueron seleccionados para la validación técnica de los microarrays en las mismas muestras tisulares mediante PCR cuantitativa (RT-qPCR). Se seleccionaron 42 genes para ser validados en muestras de orina-PMP mediante RT-qPCR. Resultados: El gráfico de escalado multidimensional mostró una clara separación entre las muestras de tejido tumorales y las controles. Se han identificado 1.047 genes diferencialmente expresados (FDR ≤ 0,1) entre los 2 grupos. La correlación entre los datos de microarrays y RT-qPCR fue alta (r = 0,928, p < 0,001). Trece genes mantuvieron el mismo sentido de expresión diferencial al ser analizados en orinas-PMP y 4 de ellos (HOXC6, PCA3, PDK4 y TMPRSS2-ERG) mostraron diferencias de expresión estadísticamente significativas entre orinas-PMP tumorales y controles (p < 0,05). Conclusión: Existe un perfil de expresión génica diferencial en el CaP. Aunque la extrapolación de la expresión génica obtenida en tejido prostático a orina-PMP se debe realizar con precaución, el análisis del tejido prostático permite la identificación de nuevos biomarcadores para diagnóstico no invasivo del CaP
Objective: To analyze gene expression profiles of prostate cancer (PCa) with the aim of determining the relevant differentially expressed genes and subsequently ascertain whether this differential expression is maintained in post-prostatic massage (PPM) urine samples. Material and methods: Forty-six tissue specimens (36 from PCa patients and 10 controls) and158 urine PPM-urines (113 from PCa patients and 45 controls) were collected between December 2003 and May 2007. DNA microarrays were used to identify genes differentially expressed between tumour and control samples. Ten genes were technically validated in the same tissue samples by quantitative RT-PCR (RT-qPCR). Forty two selected differentially expressed genes were validated in an independent set of PPM-urines by qRT-PCR. Results: Multidimensional scaling plot according to the expression of all the microarray genes showed a clear distinction between control and tumour samples. A total of 1047 differentially expressed genes (FDR≤0.1) were indentified between both groups of samples. We found a high correlation in the comparison of microarray and RT-qPCR gene expression levels (r = 0.928,P < 0.001). Thirteen genes maintained the same fold change direction when analyzed in PPM urine samples and in four of them (HOXC6, PCA3, PDK4 and TMPRSS2-ERG), these differences were statistically significant (P < 0.05). Conclusion: The analysis of PCa by DNA microarrays provides new putative mRNA markers for PCa diagnosis that, with caution, can be extrapolated to PPM-urines
Asunto(s)
Humanos , Masculino , Expresión Génica , Neoplasias de la Próstata/genética , Análisis de Secuencia por Matrices de Oligonucleótidos/métodos , Marcadores Genéticos , Predisposición Genética a la Enfermedad , Estudios de Casos y Controles , Reacción en Cadena en Tiempo Real de la PolimerasaRESUMEN
OBJECTIVE: To analyze gene expression profiles of prostate cancer (PCa) with the aim of determining the relevant differentially expressed genes and subsequently ascertain whether this differential expression is maintained in post-prostatic massage (PPM) urine samples. MATERIAL AND METHODS: Forty-six tissue specimens (36 from PCa patients and 10 controls) and 158 urine PPM-urines (113 from PCa patients and 45 controls) were collected between December 2003 and May 2007. DNA microarrays were used to identify genes differentially expressed between tumour and control samples. Ten genes were technically validated in the same tissue samples by quantitative RT-PCR (RT-qPCR). Forty two selected differentially expressed genes were validated in an independent set of PPM-urines by qRT-PCR. RESULTS: Multidimensional scaling plot according to the expression of all the microarray genes showed a clear distinction between control and tumour samples. A total of 1047 differentially expressed genes (FDR≤.1) were indentified between both groups of samples. We found a high correlation in the comparison of microarray and RT-qPCR gene expression levels (r=.928, P<.001). Thirteen genes maintained the same fold change direction when analyzed in PPM-urine samples and in four of them (HOXC6, PCA3, PDK4 and TMPRSS2-ERG), these differences were statistically significant (P<.05). CONCLUSION: The analysis of PCa by DNA microarrays provides new putative mRNA markers for PCa diagnosis that, with caution, can be extrapolated to PPM-urines.
