Qiu Jia-Xuan, Zhou Zhi-Wei, He Zhi-Xu, Zhao Ruan Jin, Zhang Xueji, Yang Lun, Zhou Shu-Feng, Mao Zong-Fu
School of Public Health, Wuhan University, Wuhan, Hubei, People's Republic of China ; Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People's Republic of China.
Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL, USA ; Guizhou Provincial Key Laboratory for Regenerative Medicine, Stem Cell and Tissue Engineering Research Center and Sino-US Joint Laboratory for Medical Sciences, Guiyang Medical University, Guiyang, Guizhou, People's Republic of China.
Drug Des Devel Ther. 2015 Jan 7;9:349-417. doi: 10.2147/DDDT.S71677. eCollection 2015.
Plumbagin (PLB) has exhibited a potent anticancer effect in preclinical studies, but the molecular interactome remains elusive. This study aimed to compare the quantitative proteomic responses to PLB treatment in human prostate cancer PC-3 and DU145 cells using the approach of stable-isotope labeling by amino acids in cell culture (SILAC). The data were finally validated using Western blot assay. First, the bioinformatic analysis predicted that PLB could interact with 78 proteins that were involved in cell proliferation and apoptosis, immunity, and signal transduction. Our quantitative proteomic study using SILAC revealed that there were at least 1,225 and 267 proteins interacting with PLB and there were 341 and 107 signaling pathways and cellular functions potentially regulated by PLB in PC-3 and DU145 cells, respectively. These proteins and pathways played a critical role in the regulation of cell cycle, apoptosis, autophagy, epithelial to mesenchymal transition (EMT), and reactive oxygen species generation. The proteomic study showed substantial differences in response to PLB treatment between PC-3 and DU145 cells. PLB treatment significantly modulated the expression of critical proteins that regulate cell cycle, apoptosis, and EMT signaling pathways in PC-3 cells but not in DU145 cells. Consistently, our Western blotting analysis validated the bioinformatic and proteomic data and confirmed the modulating effects of PLB on important proteins that regulated cell cycle, apoptosis, autophagy, and EMT in PC-3 and DU145 cells. The data from the Western blot assay could not display significant differences between PC-3 and DU145 cells. These findings indicate that PLB elicits different proteomic responses in PC-3 and DU145 cells involving proteins and pathways that regulate cell cycle, apoptosis, autophagy, reactive oxygen species production, and antioxidation/oxidation homeostasis. This is the first systematic study with integrated computational, proteomic, and functional analyses revealing the networks of signaling pathways and differential proteomic responses to PLB treatment in prostate cancer cells. Quantitative proteomic analysis using SILAC represents an efficient and highly sensitive approach to identify the target networks of anticancer drugs like PLB, and the data may be used to discriminate the molecular and clinical subtypes, and to identify new therapeutic targets and biomarkers, for prostate cancer. Further studies are warranted to explore the potential of quantitative proteomic analysis in the identification of new targets and biomarkers for prostate cancer.
白花丹素(PLB)在临床前研究中已显示出强大的抗癌作用,但其分子相互作用组仍不清楚。本研究旨在使用细胞培养中氨基酸稳定同位素标记(SILAC)方法,比较人前列腺癌PC-3和DU145细胞对PLB治疗的定量蛋白质组学反应。最终通过蛋白质印迹分析对数据进行验证。首先,生物信息学分析预测PLB可与78种参与细胞增殖和凋亡、免疫及信号转导的蛋白质相互作用。我们使用SILAC进行的定量蛋白质组学研究表明,在PC-3和DU145细胞中,分别至少有1225种和267种蛋白质与PLB相互作用,且分别有341条和107条信号通路及细胞功能可能受PLB调控。这些蛋白质和通路在细胞周期、凋亡、自噬、上皮-间质转化(EMT)及活性氧生成的调控中起关键作用。蛋白质组学研究显示PC-3和DU145细胞对PLB治疗的反应存在显著差异。PLB处理显著调节了PC-3细胞中调控细胞周期、凋亡及EMT信号通路的关键蛋白表达,但在DU145细胞中未出现这种情况。同样,我们的蛋白质印迹分析验证了生物信息学和蛋白质组学数据,并证实了PLB对PC-3和DU145细胞中调控细胞周期、凋亡、自噬及EMT的重要蛋白的调节作用。蛋白质印迹分析的数据未显示PC-3和DU145细胞之间存在显著差异。这些发现表明,PLB在PC-3和DU145细胞中引发了不同的蛋白质组学反应,涉及调控细胞周期、凋亡、自噬、活性氧产生及抗氧化/氧化稳态的蛋白质和通路。这是第一项结合计算、蛋白质组学和功能分析的系统性研究,揭示了前列腺癌细胞中信号通路网络及对PLB治疗的差异蛋白质组学反应。使用SILAC进行的定量蛋白质组学分析是一种有效且高度灵敏的方法,可用于识别像PLB这样的抗癌药物的靶标网络,这些数据可用于区分前列腺癌的分子和临床亚型,并识别新的治疗靶点和生物标志物。有必要进一步研究探索定量蛋白质组学分析在识别前列腺癌新靶点和生物标志物方面的潜力。
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