BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy, characterized by largely unsatisfactory responses to the currently available therapeutic strategies. In this study we evaluated the expression of genes involved in gemcitabine uptake in a selected cohort of patients with PDAC, with well-defined clinical-pathological features. METHODS: mRNA levels of hENT1, CHOP, MRP1 and DCK were evaluated by means of qRT-PCR in matched pairs of tumor and adjacent normal tissue samples collected from PDAC patients treated with gemcitabine after surgical tumor resection. To detect possible interaction between gene expression levels and to identify subgroups of patients at different mortality/progression risk, the RECursive Partitioning and Amalgamation (RECPAM) method was used. RESULTS: RECPAM analysis showed that DCK and CHOP were most relevant variables for the identification of patients with different mortality risk, while hENT1 and CHOP were able to identify subgroups of patients with different disease progression risk.

Modeling interactions between Human Equilibrative Nucleoside Transporter-1 and other factors involved in the response to gemcitabine treatment to predict clinical outcomes in pancreatic ductal adenocarcinoma patients

Pellegrini F.;Copetti M.;Lombardi L.;di Mola F. F.;di Sebastiano P.;
2014

Abstract

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy, characterized by largely unsatisfactory responses to the currently available therapeutic strategies. In this study we evaluated the expression of genes involved in gemcitabine uptake in a selected cohort of patients with PDAC, with well-defined clinical-pathological features. METHODS: mRNA levels of hENT1, CHOP, MRP1 and DCK were evaluated by means of qRT-PCR in matched pairs of tumor and adjacent normal tissue samples collected from PDAC patients treated with gemcitabine after surgical tumor resection. To detect possible interaction between gene expression levels and to identify subgroups of patients at different mortality/progression risk, the RECursive Partitioning and Amalgamation (RECPAM) method was used. RESULTS: RECPAM analysis showed that DCK and CHOP were most relevant variables for the identification of patients with different mortality risk, while hENT1 and CHOP were able to identify subgroups of patients with different disease progression risk.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11564/709722
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