Background: Breast cancer is a hormone-dependent tumor, and 70-80% of breast cancer patients are estrogen receptor (ER) positive. ZR-75-1 cell lines are more consistent with human breast cancer, which is mostly ER positive and PR positive. To better study the biological characteristics of 18 F-fluoroestradiol ( 18 F-FES) in breast cancer patients, ZR-75-1 breast cancer models were selected to provide a basis for further clinical application.Methods: 18 F-FES uptake in vivo was evaluated in ZR-75-1 tumor-bearing mice, using MCF-7 tumorbearing mice as a positive control. Competitive inhibition experiment was also performed, using ER downregulator fulvestrant. Biodistribution of 18 F-FES was observed in ZR-75-1 breast tumor-bearing mice scanning by 18 F-FES-PET/CT in vivo and γ counter ex vivo. The expression of ER was also determined by immunohistochemistry. An abnormal toxicity test was performed in ICR male mice whose behavior and vital signs were observed within 48 hours of 18 F-FES injection. OLINDA/EXM 2.0 software was used to calculate the absorbed doses of adult female body phantoms.Results: There was no significant difference in FES uptake between ZR-75-1 and MCF-7 tumor-bearing mice. Intervention with fulvestrant decreased the uptake of 18 F-FES. Biodistribution studies demonstrated that the uptake of 18 F-FES was high in the liver and kidneys but low in the brain. Other than excretory organs, the uptake of 18 F-FES in ER-positive breast tumors was significantly higher than in ER-negative tissues. The estimated human effective dose was 0.016 mSv/MBq for the adult female body model. Conclusions:The 18 F-FES tracer could have suitable properties for imaging ER-positive breast tumors. It may provide an important evidence for individualized treatment of patients with breast cancer.
Background: More and more evidence confirms that there are many metabolic disorders in the tumor. The occurrence and development of breast cancer (BC) is closely related to metabolism. Methods: A metabolic related genes table was obtained by the Kyoto Encyclopedia of Genes and Genomes (KEGG) related metabolic pathway. The edgeR package was used to identify differentially expressed genes (DEGs) of The Cancer Genome Atlas (TCGA) breast cancer. We established a prognostic model by univariate Cox regression analysis and lasso-penalized Cox regression. The validation prognostic model was built through the Group on Earth Observations (GEO) database. Use the nomogram and Receiver Operating Characteristic (ROC) curve to verify the accuracy of models. Result: We identified 178 DEGs and 14 prognostic-related genes to construct a prognostic model. In the TCGA prognostic model and the GEO validation prognostic model, patients were divided into high riskscore group and low riskscore group, the high riskscore group had worse prognosis.Conclusion: We constructed a prognostic model of metabolic related genes and verified the feasibility and accuracy of the model. It is hoped that the model can provide a basis and biomarker for breast cancer related metabolic therapy and prognosis.
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