Novel Gold Nanoparticles For Tumour Therapy And Prediction Models For Prognoses In Colorectal Liver Metastasis Patients

Objective:Neuroendocrine tumours(NETs)are rare cancers with positive somatostatin receptor type 2(SSTR2)expression.Currently,treatment effect for NETs is not satisfying.Nanomaterial-mediated therapy targeting SSTR2 in NETs is very promising.This study firstly combined mesoporous silica-coated gold nanorods(AuNRs@mSiO2)and targeting-SSTR2 DOTA-TATE into AuNRs@mSiO2@DOTA-TATE to investigate NETs inhibition under near-infrared light.Methods:808 nm laser(0.5 W/cm2)and thermocouple probe were used to detect the photostability and photothermal conversion ability of drugs;in vitro,cell experiments(MTT and Calcein-AM/PI)and organoid experiments investigated the anti-tumor ability of drugs;vivo experiments were performed by tumour-bearing mouse.Animal nuclide imaging explored the targeting-SSTR2 ability and biological distribution of drugs.Tumor temperature,tumor volume,tumor weight,and immunohistochemical staining were used to assess anti-tumor ability of drugs.Results:This study firstly combined mesoporous silica-coated gold nanorods(AuNRs@mSiO2)and targeting-SSTR2 DOTA-TATE into AuNRs@mSiO2@DOTA-TATE.AuNRs@mSiO2@DOTA-TATE showed good photothermal conversion efficiency(40.72%)under 808 nm laser.In vitro,under light irradiation,the cell viability significantly decreased with increasing AuNR@mSiO2@DOTA-TATE concentration;in two successfully established NET organoids with SSTR2 expression,AuNRs@mSiO2@DOTA-TATE with light inhibited tumours significantly.In vivo,the SSTR2-targeting ability and biodistribution of AuNRs@mSiO2@DOTA-TATE were confirmed with AuNRs@mSiO2@64Cu-DOTA-TATE under micro-positron emission tomography/computed tomography(micro-PET/CT);Activated by 808 nm laser,AuNRs@mSiO2@DOTA-TATE showed pretty anti-tumor ability.No significant inflammatory lesions or cell necrosis was found in the main organs,which revealed that the drugs did not have significant toxicity.Conclusion:In summary,we presented a feasible strategy to construct AuNRs@mSiO2@DOTA-TATE with good photothermal conversion efficiency,targeting-SSTR2 ability,good biocompatibility and significant antitumour effects activated by laser.AuNRs@mSiO2@DOTA-TATE is expected to become a new approach for the treatment of NETs.Objective:The aim of this study is to establish predictive models combining preoperative hematology markers to predict postoperative complications, progression-free survival (PFS) and overall survival (OS) in colorectal liver metastasis (CRLM) patients undergoing surgical resection.Methods:This study retrospectively included 380 CRLM patients undergoing CRLM resection. Multivariate regression analyses explored the independent factors affecting postoperative complications, PFS, and OS. The prediction models were constructedbased on the independent factors (P < 0.05), and the differentiation ability and calibrationability were evaluated. Survival was estimated using the Kaplan-Meier method andcompared by log-rank test.Results:A predictive model for predicting postoperative complications was constructed based on preoperative GGT ≥ 34.5 U / L(P = 0.007), RDW-CV ≥ 14.1% (P = 0.049), and intraopyative blood loss ≥ 200.0 ml (P = 0.015). The calibration curve and Hosmer-Lemeshow test (chi-square: 3.99, P = 0.91) showed the ideal calibration of the model. The predictive model for PFS was constructed based on preoperative GGT ≥ 31.0 U / L (P = 0.011), D-dimer ≥ 0.251 mg/L (P = 0.012), RDW-CV < 16.0% (P < 0.001), RDW-SD > 43.9 fl (P = 0.002), positive lymph node metastasis (P = 0.012), billobular liver metastasis distribution (P = 0.015) and R0 resection (P = 0.001). The time-dependent ROC curve showed that the AUC value was continuously greater than 0.7, which was higher than that of CRS score (P < 0.001). The predictive model for OS was constructed based on preoperative GGT ≥ 31.0 U/L (P = 0.044), positive lymph-node metastasis (P = 0.007), R0 resection (P = 0.002), major liver resection (P =0.019), postoperative complications (P = 0.003) and postoperative chemotherapy (P =0.002). The C-index was 0.700 ± 0.021, better than the CRS score (P < 0.001). According to the total risk score of the models, the population was divided into low risk, median risk and high risk groups, and PFS and OS were significantly different in the pair comparisons (P < 0.001).Conclusions:This study firstly established predictive models by including preoperative hematology markers to predict postoperative complications, PFS and OS in CRLM patients receiving resection. The predictive model showed good abilities of differentiation and calibration, providing a reference for clinicians to develop individualized CRLM management and treatment strategies.