Abstract: Plumbagin (PLB), an active naphthoquinone compound, has shown potent anticancer effects in preclinical studies; however, the effect and underlying mechanism of PLB for the treatment of pancreatic cancer is unclear. This study aimed to examine the pancreatic cancer cell killing effect of PLB and investigate the underlying mechanism in human pancreatic cancer PANC-1 and BxPC-3 cells. The results showed that PLB exhibited potent inducing effects on cell cycle arrest in PANC-1 and BxPC-3 cells via the modulation of cell cycle regulators including CDK1/CDC2, cyclin B1, cyclin D1, p21 Waf1/Cip1, p27 Kip1, and p53. PLB treatment concentration- and time-dependently increased the percentage of autophagic cells and significantly increased the expression level of phosphatase and tensin homolog, beclin 1, and the ratio of LC3-II over LC3-I in both PANC-1 and BxPC-3 cells. PLB induced inhibition of phosphatidylinositol 3-kinase (PI3K)/protein kinase B/mammalian target of rapamycin and p38 mitogen-activated protein kinase (p38 MAPK) pathways and activation of 5'-AMP-dependent kinase as indicated by their altered phosphorylation, contributing to the proautophagic activities of PLB in both cell lines. Furthermore, SB202190, a selective inhibitor of p38 MAPK, and wortmannin, a potent, irreversible, and selective PI3K inhibitor, remarkably enhanced PLB-induced autophagy in PANC-1 and BxPC-3 cells, indicating the roles of PI3K and p38 MAPK mediated signaling pathways in PLB-induced autophagic cell death in both cell lines. In addition, PLB significantly inhibited epithelial to mesenchymal transition phenotype in both cell lines with an increase in the expression level of E-cadherin and a decrease in N-cadherin. Moreover, PLB treatment significantly suppressed the expression of Sirt1 in both cell lines. These findings show that PLB promotes cell cycle arrest and autophagy but inhibits epithelial to mesenchymal transition phenotype in pancreatic cancer cells with the involvement of PI3K/protein kinase B/ mammalian target of rapamycin and p38 MAPK mediated pathways. Keywords: Plumbagin, pancreatic cancer, cell cycle, autophagy, EMT, Sirt1
Objective: To explore the value of differential subsampling with cartesian ordering (DISCO) and multiplexed sensitivity-encoding diffusion weighted-imaging (MUSE-DWI) combined with prostate specific antigen density (PSAD) in the diagnosis and risk stratification of prostate cancer (PCa). Methods: The data of 183 patients [aged from 48 to 86 (68±8) years] with prostate diseases in the General Hospital of Ningxia Medical University from July 2020 to August 2021 were retrospectively collected. Those patients were divided into non-PCa group (n=115) and PCa group (n=68) based on the disease condition. According to the risk degree, PCa group was subdivided into low risk PCa group (n=14) and medium-to-high risk PCa group (n=54). The differences of volume transfer constant (Ktrans), rate constant (Kep), extracellular volume fraction (Ve), apparent diffusion coefficient (ADC) and PSAD between groups were analyzed. Receiver operating characteristic (ROC) curves analysis were conducted for evaluating the diagnostic efficacy of quantitative parameters and PSAD in distinguishing non-PCa and PCa, low-risk PCa and medium-high risk PCa. Multivariate logistic regression model was used for screening out the predictors, which was statistically significant differences between non-PCa group and PCa group, for PCa prediction. Results: Ktrans, Kep, Ve and PSAD of PCa group all were higher than those of non-PCa group, and ADC value was lower than that of non-PCa group, and the differences all were statistically significant (all P<0.001). Ktrans, Kep and PSAD of medium-to-high risk PCa group all were higher than those of low risk PCa group, and ADC value was lower than that of low risk PCa group, and the differences were all statistically significant (all P<0.001). When distinguishing non-PCa from PCa, the area under ROC curve (AUC) of the combined model (Ktrans+Kep+Ve+ADC+PSAD) was higher than that of any single index [0.958 (95%CI: 0.918-0.982) vs 0.881 (95%CI: 0.825-0.924), 0.836 (95%CI: 0.775-0.887), 0.672 (95%CI: 0.599-0.740), 0.940(95%CI: 0.895-0.969), 0.816(95%CI:0.752-0.869), all P<0.05]. When distinguishing low-risk PCa and medium-to-high risk PCa, the AUC of the combined model (Ktrans+Kep+ADC+PSAD) were higher than those of Ktrans, Kep and PSAD[0.933 (95%CI: 0.845-0.979) vs 0.846 (95%CI:0.738-0.922), 0.782 (95%CI:0.665-0.873), 0.84 8(95%CI: 0.740-0.923), all P<0.05]. The multivariate logistic regression analysis showed that Ktrans (OR=1.005, 95%CI:1.001-1.010) and ADC values (OR=0.992, 95%CI:0.989-0.995) were predictors of PCa (P<0.05). Conclusions: DISCO and MUSE-DWI combined with PSAD can distinguish benign and malignant prostate lesions. Ktrans and ADC values were predictors of PCa; Ktrans, Kep, ADC values and PSAD are helpful in predicting the biological behavior of PCa.目的： 探究基于笛卡尔采集的K空间共享三维容积快速动态成像（DISCO）和复合灵敏度编码的高分辨率扩散成像（MUSE-DWI）联合前列腺特异性抗原密度（PSAD）在前列腺癌（PCa）的诊断及危险分层中的价值。 方法： 回顾性收集2020年7月至2021年8月宁夏医科大学总医院183例［年龄：48~86（68±8）岁］前列腺疾病患者的资料。根据疾病情况分为非PCa组115例，PCa组68例，其中PCa组又根据危险程度分为低危PCa组14例，中高危PCa组54例。分析组间容积转移常数（Ktrans）、速率常数（Kep）、血管外细胞外体积分数（Ve）、表观扩散系数（ADC）和PSAD的差异。采用受试者工作特征（ROC）曲线评估各定量参数值及PSAD鉴别非PCa和PCa、低危PCa和中高危PCa的诊断效能。采用多因素logistic回归模型对非PCa组和PCa组间差异有统计学意义的指标进行分析，筛选出PCa的预测因子。 结果： PCa组的Ktrans、Kep、Ve值和PSAD均高于非PCa组，ADC值低于非PCa组，差异均有统计学意义（均P<0.001）；中高危PCa组的Ktrans、Kep值和PSAD均高于低危PCa组，ADC值低于低危PCa组，差异均有统计学意义（均P<0.001）。鉴别非PCa和PCa时，联合模型（Ktrans+Kep+Ve+ADC+PSAD）的ROC曲线下面积（AUC）高于单一指标［0.958（95%CI：0.918~0.982）比0.881（95%CI：0.825~0.924）、0.836（95%CI：0.775~0.887）、0.672（95%CI：0.599~0.740）、0.940（95%CI：0.895~0.969）、0.816（95%CI：0.752~0.869），均P<0.05］；鉴别低危PCa和中高危PCa时，联合模型（Ktrans+Kep+ADC+PSAD）的AUC高于Ktrans、Kep和PSAD［0.933（95%CI：0.845~0.979）比0.846（95%CI：0.738~0.922）、0.782（95%CI：0.665~0.873）、0.848（95%CI：0.740~0.923），均P<0.05］。多因素logistic回归分析显示Ktrans（OR=1.005，95%CI：1.001~1.010）和ADC值（OR=0.992，95%CI：0.989~0.995）是PCa的预测因子（P<0.05）。 结论： DISCO和MUSE-DWI联合PSAD可以鉴别前列腺良恶性病变，Ktrans和ADC值是PCa的预测因子；Ktrans、Kep、ADC值和PSAD有助于预测PCa的生物学行为。.
Objective: To investigate the application value of relaxation time quantitative technique from synthetic magnetic resonance imaging (MRI) in the diagnosis and invasion assessment of prostate cancer. Methods: A total of 119 patients with prostate diseases [122 regions of interest(ROI)] who underwent routine MRI scan and magnetic resonance image compilation (MAGiC) sequence of prostate from March 2020 to March 2021 in General Hospital of Ningxia Medical University were retrospectively collected, they were divided into prostate cancer group(58 cases, 61 ROI) and non-prostate cancer group(61 cases, 61 ROI) according to the pathological results. In the prostate cancer group, those patients with an age of 48 to 85(69.8±5.9) years, and further divided into two subgroups according to the location of occurrence: peripheral zone cancer group (43 cases, 45 ROI) and transitional zone cancer group (15 cases, 16 ROI). The non-prostate cancer group consisted of patients with benign prostatic hyperplasia or complicated with chronic prostatitis, with an age of 41 to 81(68.6±7.0) years, and they were further divided into two subgroups according to the location of occurrence: non-cancerous peripheral zone group (45 cases, 45 ROI) and transitional zone benign prostatic hyperplasia group(16 cases, 16 ROI). Prostate cancer lesions were classified as low risk (Gleason score ≤6) or intermediate/high risk (Gleason score ≥7). After the post-processing of MAGiC images, T1, T2 and proton density(PD) values of prostate cancer group and non-prostate cancer group were obtained. At the same time, relevant software were used for image post-processing to generate apparent diffusion coefficient (ADC) value, the data between the two groups were analyzed by the Independent sample t-test or Mann-Whitney U-test, and the diagnostic effectiveness of each quantitative parameter in diagnosing prostate cancer and discriminating low risk prostate cancer from intermediate/high risk prostate cancer was analyzed by using receiver operating characteristic curve (ROC) analysis, the correlation between each quantitative parameter and Gleason score were assessed by Spearman correlation analysis. Results: The T1 value and T2 value of the peripheral zone cancer group were lower than those in non-cancerous peripheral zone group [1 201.3 (1 103.5, 1 298.2) ms vs 2 274.0 (1 620.9, 2 776.5) ms; 78.0 (74.0, 83.8) ms vs (160.6±54.9) ms] (all P<0.001), there was no statistically significant in PD value between the two groups (P>0.05). The T1 value and T2 value of the transitional zone cancer group were lower than those in transitional zone benign prostatic hyperplasia group [1 073.3 (1 003.9, 1 164.9) ms vs 1 340.8 (1 208.5, 1 502.8) ms; 76.9 (74.8, 82.8) ms vs 95.1(82.8, 103.4) ms] (all P<0.001), there was no statistically significant in PD value between the two groups (P>0.05). The area under the curve (AUC) of T2 value was similar with the ADC value in discriminating peripheral zone cancer group from non-cancerous peripheral zone group(0.963 vs 0.991, P=0.105), while in discriminating transitional zone cancer group from transitional zone benign prostatic hyperplasia group, the AUC of T2 value、T1 value and ADC value were similar(0.867, 0.930 vs 0.938, all P>0.05). ADC value, T2 value all were negatively correlated with Gleason score (r=-0.747,-0.453, all P<0.001). T2 value and ADC value demonstrated equivalent diagnostic performance in discriminating low risk from intermediate/high risk prostate cancer, and there were no statistically significant (AUC: 0.787 vs 0.943, P=0.069). Conclusions: Quantitative relaxation time T1 and T2 values derived from synthetic MRI can discriminate prostate cancer from other benign pathologies, and T2 value have the equivalent diagnostic performance compared to ADC value. Synthetic MRI has high clinical application value, and T2 value can distinguish low risk prostate cancer from intermediate/high risk prostate cancer.目的： 探讨集成MRI弛豫时间定量技术在前列腺癌诊断及侵袭性评估中的应用价值。 方法： 回顾性搜集2020年3月至2021年3月宁夏医科大学总医院行前列腺MRI常规序列和磁共振图像编译（MAGiC）序列扫描的前列腺疾病患者119例［共122个感兴趣区（ROI）］，依据病理结果分为前列腺癌组（58例，61个ROI）和非前列腺癌组（61例，61个ROI）。前列腺癌组为前列腺癌患者，年龄48~85（69.8±5.9）岁，依据发生部位分为两个亚组：外周带癌组（43例，45个ROI）和中央腺体癌组（15例，16个ROI）。非前列腺癌组为良性前列腺增生或合并慢性前列腺炎患者，年龄41~81（68.6±7.0）岁，依据发生部位分为两个亚组：外周带非癌组（45例，45个ROI）和中央腺体良性前列腺增生组（16例，16个ROI）。前列腺癌病灶分为低危（GS≤6分）和中/高危（GS≥7分）。前列腺癌组和非前列腺癌组 MAGiC图像经过后处理后得到T1、T2、质子密度（PD）值，同时用相关软件进行后处理生成表观扩散系数（ADC）值，并采用独立样本t检验或Mann-Whitney U检验分析两组间数据的比较，采用受试者工作特征（ROC）曲线分析各定量参数诊断前列腺癌和鉴别低危、中高危前列腺癌的诊断效能，采用Spearman相关分析评估各定量参数与Gleason评分的相关性。 结果： 外周带癌组的T1值和T2值［M（Q1，Q3）］均低于外周带非癌组［1 201.3（1 103.5，1 298.2）ms比2 274.0（1 620.9，2 776.5）ms；78.0（74.0，83.8）ms比（160.6±54.9）ms］（均P<0.001），两组PD 值差异无统计学意义（P>0.05）。中央腺体癌组的T1值和T2值均低于中央腺体良性前列腺增生组［1 073.3（1 003.9，1 164.9）ms比1 340.8（1 208.5，1 502.8）ms；76.9（74.8，82.8）ms比95.1（82.8，103.4）ms］（均P<0.001），两组PD值差异无统计学意义（P>0.05）。区分外周带癌组与外周带非癌组时，T2值显示出与ADC值相似的曲线下面积（AUC）（0.963比0.991，P=0.105），而区分中央腺体癌组与中央腺体良性前列腺增生组时，T2值、T1值与ADC值的AUC均相似（0.867、0.930 比0.938，两两之间均P>0.05）。ADC值、T2值均与 Gleason评分呈负相关（r=-0.747、-0.453，均P<0.001）。T2值和ADC值在区分低危和中高危前列腺癌时的诊断效能相当，差异无统计学意义（AUC：0.787比0.943，P=0.069）。 结论： 集成MRI获得的定量T1和T2值能够鉴别前列腺癌和其他良性病变，T2值与ADC值诊断效能相当。集成MRI具有较高的临床应用价值，T2值能够区分低危与中高危前列腺癌。.
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