Castration-resistant prostate cancer (CRPC) is the most common malignant tumor of the male urinary system. Nanodrug delivery systems (NDDS) have been widely applied in drug delivery for tumor therapy; however, nanotherapeutics encounter various biological barriers that prevent successful accumulation of drugs, specifically at diseased sites. Therefore, there is an urgent need to develop a CRPC-targeting nanocomposite with fine biocompatibility for penetrating various biological barriers, delivering sufficient drugs to the targeting site and improving therapeutic efficiency. In this work, CRPC cell membranes were firstly adapted as biomimetic vectors for the encapsulating PEG−PLGA polymer containing the chemotherapy drug docetaxel (DTX). The CRPC membrane-camouflaged nanoparticles can easily escape early recognition by the immune system, penetrate the extracellular barrier, and evade clearance by the circulatory system. In addition to the characteristics of traditional nanoparticles, the CRPC cell membrane contains an arsenal of highly specific homotypic moieties that can be used to recognize the same cancer cell types and increase the targeted drug delivery of DTX. In vivo fluorescence and radionuclide dual-model imaging were fulfilled by decorating the biomimetic nanosystem with near-infrared dye and isotope, which validated the homotypic targeting property offered by the CRPC cell membrane coating. Importantly, remarkably improved therapeutic efficacy was achieved in a mice model bearing CRPC tumors. This homologous cell membrane enabled an efficient drug delivery strategy and enlightened a new pathway for the clinical application of tumor chemotherapy drugs in the future.

Purpose: To develop and validate a nomogram for preoperative predicting the pathological upgrading of prostate cancer (PCa).Methods: The prediction model was developed in a primary cohort that consisted of 208 PCa patients. All patients included in the study possessed both biopsy pathology specimens and radical prostatectomy pathology specimens, and completed the ( 68 Ga-prostate-specific membrane antigen [PSMA]) positron emission tomography/ computed tomography (PET/CT) detection. The R function "createDataPartition" was used in a 7:3 ratio to randomly divide the patients into training and validation cohorts. In the training cohort, the independent predictors of pathological upgrading of PCa were determined by univariate analysis, univariate regression analysis and multivariate regression analysis. Based on these independent predictors, a nomogram was developed, and its performance was evaluated by receiver operating characteristic (ROC) curve, area under the curve (AUC) and calibration curve of training cohort and validation cohort. Results: The nomogram incorporated five independent predictors including prostate volume (PV), SUV max of the 68 Ga-PSMA PET/CT examination on prostate lesions (SUV max ), body mass index (BMI); percentage of cancer positive biopsy cores (PPC) and biopsy International Society of Urological Pathology (ISUP) grade. The nomogram showed good diagnostic accuracy for the pathological upgrading of both the training cohort and the validation cohort (AUC = 0.818 and 0.806, respectively).The calibration curves for the two cohorts both showed optimal agreement between nomogram prediction and actual observation. Conclusions:We developed and validated a nomogram to accurately predict the risk of pathological upgrading after radical PCa surgery, which can provide accurate basis for therapeutic schedule and prognostic data of PCa patients.