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It has been reported that the expression of thymidylate synthase (TS), dihydropyrimidine dehydrogenase (DPD) and orotate phosphoribosyltransferase (OPRT) may predict the clinical efficacy of 5-fluorouracil (5-FU)-based therapy in cancer patients. We investigated the differences in the mRNA and protein expression of these enzymes in various tumor tissues. A total of 17,613 specimens of head and neck, gastric, colorectal, breast, lung and pancreatic cancer were collected from multiple facilities in Japan, and the mRNA and protein expression levels of the above enzymes were examined in 4,830 and 12,783 of these specimens, respectively. The mRNA levels were analyzed using RT-PCR in laser-captured microdissected formalin-fixed paraffin-embedded specimens, while the protein levels were analyzed by enzyme-linked immunosorbent assays. The median values of the relative TS, DPD and OPRT mRNA levels were 2.06, 0.803 and 1.17, respectively, while the median protein levels were 22.1, 134.8 and 3.81 ng enzyme/mg protein, respectively. The carcinomas were classified into two sets of four groups each using the overall median levels of TS and DPD or TS and OPRT as cutoff values. Approximately 60% of the gastric cancers exhibited elevated mRNA and protein expression levels of DPD, while >65% of the colorectal cancers showed low levels of DPD expression. Overall, 75% of the head and neck cancers exhibited high expression levels of DPD. Among the lung and pancreatic cancers, 50-74% showed low TS/high DPD expression. In conclusion, the mRNA expression and protein levels of TS, DPD and OPRT differed according to the type of cancer. The results of this large-scale population analysis are expected to be useful as reference data for predicting the relationship between the respective enzyme levels and the efficacy of 5-FU-based chemotherapy.

international journal of molecular medicine

Thymidylate synthase, dihydropyrimidine dehydrogenase, orotate phosphoribosyltransferase mRNA and protein expression levels in solid tumors in large scale population analysis