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Background: Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods: A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results: Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions: The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.
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INTRODUCTION: Breast cancer is a malignant tumor that seriously threatens women's life and health. METHODS: In this study, we proposed to use graphene nanoparticles loaded with siRNA that can silence Rictor molecules essential for the mammalian target of rapamycin (mTOR) complex 2 (mTORC2) complex to enhance gene delivery to tumor cells through modification of cell-penetrating peptide (CPP) for the treatment of breast cancer. RESULTS: Remarkably, we successfully synthesized graphene oxide (GO)/polyethyleneimine (PEI)/polyethylene glycol (PEG)/CPP/small interfering RNA (siRNA) system, and the results were observed by atomic force microscopy (AFM) and ultraviolet visible (UV-Vis) absorption spectra. The optimum mass ratio of siRNA to GO-PEI-PEG-CPP was 1:0.5. We screened out Rictor siRNA-2 from 9 candidates, which presented the highest inhibition rate, and this siRNA was selected for the subsequent experiments. We validated that Rictor siRNA-2 significantly reduced the Rictor expression in triple negative breast cancer (TNBC) cells. Confocal fluorescence microscope and flow cytometry analysis showed that GO-PEI-PEG-CPP/siRNA was able to be effectively uptake by TNBC cells. GO-PEI-PEG-CPP/siRNA improved the effect of siRNA on the inhibition of TNBC cell viability and the induction of TNBC cell apoptosis. The expression of Rictor and the phosphorylation of Akt and p70s6k were inhibited by GO-PEI-PEG-CPP/siRNA. Tumorigenicity analysis in nude mice showed that GO-PEI-PEG-CPP/siRNA significantly repressed the tumor growth of TNBC cells in vivo. The levels of ki-67 were repressed by GO-PEI-PEG-CPP/siRNA, and the apoptosis was induced by GO-PEI-PEG-CPP/siRNA in the system. DISCUSSION: Therefore, we concluded that CPP-modified GO nanoparticles loaded with Rictor siRNA significantly repressed TNBC progression by the inhibition of PI3K/Akt/mTOR signaling. Our finding provides a promising therapeutic strategy for the treatment of TNBC.
