1.The mechanism of MLN4924 for anticancer therapy
Mi YANG ; Hongren CEN ; Cheng LONG ; Jiejun FU
Journal of International Oncology 2017;44(5):373-375
MLN4924 can inhibit the proliferation,invasion and metastasis of tumor by inducing tumor cells apoptosis,senescence and autophagy,which can inhibit tumor angiogenesis and enhance the sensitivity of radiotherapy and chemotherapy.Therefore,MLN4924 plays a good anti-tumor effect.
2.Effects of FBXW7α on Expression and Localization of Heat Shock Transcription Factor 1 in Colorectal Cancer Cells
Cancer Research on Prevention and Treatment 2021;48(5):457-463
Objective To investigate the effect of FBXW7 on the expression and localization of HSF1 in colorectal cancer cells. Methods The expression levels of HSF1 and pHSF1Ser326 protein in FBXW7 deletion (KO) and wild-type (WT) FBXW7-expressing counterpart colorectal cancer cells were detected by Western blot. The nucleoprotein expression and localization of pHSF1Ser326 in heat-shocked or recovery stage cells were observed by Western blot and immunofluorescence method. Results The HSF1 expression level in DLD1 cells transfected with FBXW7α was decreased significantly (
3.Value of dual-layer spectral detector CT in differentiating the diagnosis of lung cancer and inflammatory nodules
Yicheng FU ; Ye YU ; Xingbiao CHEN ; Ying ZHANG ; Xiaoqian LI ; Yibo SUN ; Jiejun CHENG ; Huawei WU
Chinese Journal of Radiology 2021;55(12):1264-1269
Objective:To explore the value of dual-layer spectral detector CT in differentiating the diagnosis of lung cancer and inflammatory nodules.Methods:A total of 92 patients undergoing enhanced chest scan from March 2019 to September 2020 at Renji Hospital, School of Medicine, Shanghai Jiaotong University, were retrospectively enrolled in the study. The conventional CT parameters, spectral CT parameters were measured and the nodules′ morphological characteristics were analyzed. Later the factors with statistical significance were identified as independent variables in a logistic regression model to establish models for predicting malignant nodules. ROC curve was used to assess the diagnostic performance for the conventional CT model, spectral CT parameters and combined model, respectively. Differences in the area under the ROC curve (AUC) were analyzed by the DeLong test.Results:Lobulated sign (42 and 8, respectively, χ2=10.779, P=0.001), short burr sign (41 and 7, respectively, χ2=11.911, P=0.001), pleural indentation sign (45 and 9 respectively, χ2=11.705, P=0.001), vascular convergence sign (35 and 8, respectively, χ2=5.337, P=0.021) and the venous phase iodine concentrations (IC) value [(2.1±0.5) mg/ml, (2.3±0.5) mg/ml, t=-2.464, P=0.016], normalized iodine concentrations (NIC) value (0.40±0.06, 0.45±0.08, t=-6.943, P<0.001), and Z-effective (Z eff) values (8.38±0.21, 8.49±0.19, t=-2.122, P=0.037) were significantly different between the lung cancer group and the inflammatory group, while other CT signs and CT indicators were not significantly different between the lung cancer group and the inflammatory group ( P>0.05). The conventional CT model was established with lobulated sign, short burr sign, pleural indentation sign, vascular convergence sign, and the AUC for differential diagnosis of lung cancer and inflammatory nodules was 0.827. The spectral CT parameter model was established with venous phase IC, venous phase NIC, and venous phase Z eff value, and the AUC for differential diagnosis of lung cancer and inflammatory nodules was 0.899. The conventional CT model combined spectral CT parameter model was established with the significant factors in the univariate analysis, and the AUC for differential diagnosis of lung cancer and inflammatory nodules was 0.925. The AUC of the combined model showed no significant difference from that of the spectral CT parameter model ( Z=1.794, P=0.073). However, AUC of the combined model was significantly higher than that of evaluation based on conventional CT alone ( Z=2.156, P=0.031). Conclusion:Spectral CT parameters combined with conventional CT signs can improve the differential diagnosis efficiency between lung cancer and inflammatory nodules.