Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

OBJECTIVE: To investigate the effects of down-regulating of c-Met expression to the proliferation, invasiveness and apoptosis of human multiple myeloma RPMI 8226 cells. METHODS: According to transfection the RPMI8226 cells were dividide into RPMI 8226 (untreated RPMI 8226), RPMI 8226 /shRNA-Met and RPMI8226/shRNA-control group, respectively. Protein expression level of c-Met was detected by Western blot so as to evaluate transfection condition; the proliferation of the cells was detected by MTT; apoptosis and cycle of the cells were detected by flow cytometry; effect of c-Met/shRNA on RPMI 8226 cell adhesion was detected by RPMI 8226 cell adherence to ECM (Fn and Matrigel) and ECV304 cells. Invasiveness of RPMI 8226 cell was detected by Transwell assay. RESULTS: The c-Met short hairpin RNA (shRNA) was successfully transfected into RPMI 8226 cells, and could inhibit the expression of c-Met significantly. The down-regulation of c-Met could inhibit the proliferation of RPMI 8226 cells significantly. The percentage of cells in the G/G phase and apoptotic rate (sub-G) in the RPMI 8226/shRNA-Met group were higher than those in the control group, the adhesion rate and the number of migrated RPMI 8226/shRNA-Met cells were decreased significantly as compared with control group. There were no significant differences in each indexes between RPMI 8226/shRNA-control and control group. CONCLUSION Knockdown of c-Met can affect the proliferation, adherence, invasiveness and apoptosis of human multiple myeloma RPMI 8226 cells.
Apoptosis ; Cell Line, Tumor ; Cell Proliferation ; Humans ; Multiple Myeloma ; RNA, Small Interfering

OBJECTIVETo investigate if NS-398 could enhance the chemosensitivity of Bortezomib (BOR) on human multiple myeloma RPMI 8226 cells.

METHODSAfter the treatment of NS-398 combined with BOR, MTT assay was used to detect the proliferation inhibition effect on human multiply myeloma RPMI 8226 cells in vitro, Flow cytometry was used to analyze their effect of apoptosis and cell cycle; the caspase-3 activity of different treatment group was detected by using ELISA and the activity of Cox-2 was measured by using Cox-2 activity assay kit.

RESULTSThe inhibitory rate of NS-398 combined with BOR was higher than that of NS-398 or BOR alone(Q>0.85). After treatment of NS-398 combined with BOR, the percentage of cells arrested in the G/Gphase and apoptotic rate were both higher than that of treatment with each drug alone(Q>1.15). The caspase-3 activity in cells treated with combined of NS-398 and BOR was significantly higher than that of treatment of each drug alone(Q>1.15).

CONCLUSIONNS-398 combined with BOR shows a synergistic effect on the growth inhibition of RPMI 8226 cells in vitro.

OBJECTIVETo establish bortezomib (BOR)-resistant human multiple myeloma U266 cell line U266/BOR and to detect its biological characteristics.

METHODSU266 cells were constantly exposed at low dose and progressively increasing dose of BOR to establish U266/BOR, the cell morphology was observed by inverted microscopy, ICand resistant index were determined by MTT assay, cell growth curve was drawed and the doubling time was calculated; cell cycle distribution were analyzed by flow cytometry, and RT-PCR was used to detect the mRNA expression of resistance-related genes.

RESULTSThe MM U266/BOR cell line was successfully constructed and its resistance index was up to 19.8. The both cell morphologies were not different. Compared with U266 cells, the multiplication time was postponed with the increase of G/Gcell ratio, and S phase was reduced. The mRNA expression of PTPROt, Beclin 1 and PTEN were reduced, and the mRNA expression of c-Maf was enhanced in U266/BOR cells; as compared with U266 cells, but the MDR1 mRNA expression was not different between U266 cells and U266 BOR cells.

CONCLUSIONThe BOR-resistant U266 cell line has been establiseed successfully. It provides an ideal cell model for further exploration of the mechanism for BOR resistance.

OBJECTIVETo investigate if Ad-NK4 can enhance the chemosensitivity of human multiple myeloma RPMI 8226 cells to bortezomib(BOR).

METHODSThe cell-matrix adhesion test and PRMI 8226 cell-ECV 304 cell adhesion test were used to analyze the effect of Ad-NK4 on adhesion of RPMI 8226 cells; Western blot was used to detect the expression changes of adhesion and invasion-associated proteins MMP2, MMP3, MMP7 and VEGF; MTT assay was used to detect the effect of Ad-NK4 on proliferation of RPMI 8226 cells; the flow cytometry with PI staining was used to detect the effect of Ad-NK4 on cell apoptosis; the expression of cleaved caspase-3, BAX and BCL-2 was assayed by Western blot.

RESULTSThese 2 adhesion assays indicated that Ad-NK4 significantly inhibited the adhesion of human multiple myeloma RPMI 8226 cells. In addition, Erk and JAK/STAT pathway may be involved in the process. The expression level of MMP-2, MMP-3 and VEGF were decreased in Ad-NK4 group, compared with untreated or Ad-GFP group (P<0.05). However, the expression of MMP-7 protein in Ad-NK4 group was not significantly different from untreated or Ad-GFP group (P>0.05). The inhibitory rates of the proliferation in cells treatedly Ad-NK4 combined with BOR was significantly higher than that with BOR or Ad-NK4 alone. Similarly, Western blot indicated that the level of cleaved caspase-3 and BAX in cells treated with Ad-NK4 combined with BOR was significantly higher than BOR or Ad-NK4 alone, but BCL-2 protein expression was significantly lower. Meanwhile, the ratio of BAX/BCL-2 was increased.

CONCLUSIONAd-NK4 can enhance the chemosensitivity of human multiple myeloma RPMI 8226 cells to BOR,which is associated with increasing of both BAX/BCL-2 ratio and Caspase-3 activation.

Apoptosis ; Bortezomib ; Caspase 3 ; Cell Line, Tumor ; Humans ; Multiple Myeloma