肝癌索拉非尼耐药标志物筛选及PPARγ拮抗剂抗耐药作用研究

发布时间：2018-04-29 01:34

本文选题：肝癌 + 索拉非尼　；参考：《浙江大学》2016年博士论文

【摘要】：背景:肝癌为我国高发恶性肿瘤之一,每年约有38.3万人死于肝癌,约占全球肝癌死亡总数的51%。目前肝癌治疗手段有限,疗效不理想,病死率极高,是困扰我国群众健康的重大问题。肝癌的常见治疗手段包括外科手术、肝动脉栓塞化疗术和消融术等,对于无法手术患者,化疗也是重要手段之一。近年来新型分子靶向治疗技术的发展为肝癌治疗带来了全新视野。靶向药物索拉非尼可以通过抑制酪氨酸激酶阻止肿瘤新生血管的生成,还能经Raf/MEK/ERK信号传导通路直接抑制肿瘤生长。临床试验表明索拉非尼在肝癌晚期患者中展现了良好的疗效和耐受性,但存在耐药现象,其耐药机制尚不明确,一般认为与肿瘤细胞、肿瘤基质和个体遗传耐药性有关。相关研究表明肝癌耐药相关分子的表达变化能指示患者疗效敏感性,因此通过探索潜在生物标志物,建立索拉非尼治疗疗效的分子生物标志物谱,将有利于针对性给药,实现精准医疗;此外对于关键分子的机制研究,将解决耐药发生,并推动新药研发。方法:1、外科(肝切除/肝移植)术后复发患者接受索拉非尼治疗,根据影像学表现结合血清学指标(AFP)将入组患者分为耐药型和敏感型。取各组肝癌及癌旁组织,同时以其他肝硬化患者及健康供肝组织作为基准对照进行iTRAQ实验,比较耐药组与敏感组之间组织蛋白表达差异,进而开展GO、KEGG等生物信息学分析,发现特征标志物谱和所涉及的生物学通路。2、建立肝癌细胞体外索拉非尼耐药模型,在模型中采用Western blot技术对特征标志物的表达差异进一步验证。3、利用耐药模型,结合PPARy拮抗剂及激动剂,采用CCK8法检测PPARγ信号通路外在干预下肝癌细胞增殖情况的变化,细胞流式技术研究细胞周期阻滞和凋亡对肝癌细胞增殖抑制的作用,探索PPARγ拮抗剂潜在的抗索拉非尼耐药价值。结果:1、利用iTRAQ技术,发现索拉非尼敏感组和耐药组表达差异达到1.5倍以上的蛋白有222个,而表达差异达到2倍以上的蛋白57个,其中表达上调蛋白有26个,表达下调蛋白有31个;2、GO分析表明差异蛋白多涉及小分子代谢过程、分解代谢过程、羧酸代谢过程及含氧酸的代谢过程,而KEGG的信号通路分析同样表明,差异蛋白富集在糖代谢、脂肪酸代谢和糖酵解通路;3、利用体外耐药模型对2倍以上差异蛋白的验证发现包括ITGA6、FN1、ICAM1、ITPA、BAX、TXNDC17和MSH2等蛋白差异与体内结果一致,可作为机制研究的候选蛋白;4、基于生物信息学分析结果,发现PPARs特别是PPARγ在耐药模型中表达发生显著变化;5、发现PPARγ的拮抗剂T0070907能够增强肝癌细胞对索拉非尼的敏感性,另一个拮抗剂GW9662未能达到同样效果;6、,.拮抗剂T0070907及GW9662的联用未能进一步增强细胞对索拉非尼的敏感性;7、肝癌细胞对索拉非尼敏感性的增强是细胞周期阻滞所致。结论:1、通过比较索拉非尼耐药和敏感患者组织蛋白表达谱,发现多个差异蛋白,涉及脂肪酸代谢等生物学过程及信号通路;2、PPARγ可能是肝癌索拉非尼耐药发生的关键因子,其拮抗剂T0070907可增强肝癌细胞的敏感性,具有潜在的临床应用价值。
[Abstract]:Background: liver cancer is one of the high incidence of malignant tumors in China. About 383 thousand people die of liver cancer every year, accounting for about 51%. of the total number of cancer deaths in the world. At present, the treatment of liver cancer is limited, the curative effect is not ideal and the fatality rate is very high. It is a major problem perplexing the health of the masses in our country. The common treatment means of liver cancer include surgery, hepatic arterial chemoembolization and chemotherapy. In recent years, the development of new molecular targeting therapy has brought new field of vision for the treatment of liver cancer. The target drug Sola Fini can inhibit the formation of neovascularization of tumor by inhibiting tyrosine kinase, and can also suppress the swelling directly through the Raf/MEK/ERK signal transduction pathway. Tumor growth. Clinical trials show that Sola Fini has shown good efficacy and tolerance in advanced liver cancer patients, but there is a drug resistance phenomenon. The mechanism of drug resistance is not clear. It is generally considered to be related to tumor cells, tumor matrix and individual genetic resistance. Therefore, by exploring potential biomarkers and establishing molecular biomarker spectra of sorafenib treatment, it will be beneficial to targeted drug delivery and accurate medical treatment. In addition, the mechanism of key molecules will be studied to solve the drug resistance and promote new drug development. Methods: 1, surgery (hepatectomy / liver transplantation) for recurrent patients after surgery. In the treatment of sorafeni, the patients were divided into drug-resistant and sensitive types according to the imaging findings combined with serological index (AFP). The liver and paracancerous tissues were taken in each group, and the iTRAQ experiment was carried out with other liver cirrhosis patients and healthy donor liver tissue as the reference control, and the difference of tissue protein expression between the drug resistant and the sensitive groups was compared. GO, KEGG and other bioinformatics analysis, found the characteristic marker spectrum and the biological pathway involved.2, established the sorafeni drug resistance model of liver cancer cells in vitro. In the model, the expression difference of the characteristic markers was further verified by Western blot technology, and.3 was verified by the drug resistance model, PPARy antagonist and agonist, and the CCK8 method was used. The proliferation of hepatoma cells under the external intervention of PPAR gamma signaling pathway. Cell flow cytometry was used to study the effect of cell cycle arrest and apoptosis on the proliferation inhibition of hepatoma cells, and to explore the potential anti sorafeni resistance value of PPAR gamma antagonist. Results: 1, the expression difference between the current sorafenib sensitive group and the drug resistant group was 1. by using iTRAQ technique. More than 5 times more protein than 222, and the expression difference reached more than 2 times of protein 57, of which there were 26 expression up-regulated proteins and 31 down-regulated proteins; 2, GO analysis showed that the differential proteins involved small molecular metabolic processes, metabolic processes, carboxylic acid metabolism and metabolic processes of oxyacid, and KEGG signaling pathway analysis also indicated that The difference protein was enriched in sugar metabolism, fatty acid metabolism and glycolysis pathway; 3, the identification of 2 times more than the protein in vitro drug resistance model found that the differences of ITGA6, FN1, ICAM1, ITPA, BAX, TXNDC17 and MSH2 were consistent with the results of the body, and could be used as the candidate proteins for the mechanism study; 4, based on the bioinformatics analysis, the discovery of PPARs In particular, the expression of PPAR gamma was significantly changed in the drug resistance model. 5, PPAR gamma antagonist T0070907 was found to enhance the sensitivity of liver cancer cells to sorafenib, and the other antagonist GW9662 failed to achieve the same effect; 6, the combination of antagonist T0070907 and GW9662 failed to step up the cell sensitivity to sorafenib; 7, liver cancer was fine. Conclusion: 1, by comparing the tissue protein expression profiles of sorafenib and sensitive patients, a number of differential proteins are found to be involved in biological processes such as fatty acid metabolism and signaling pathways. 2, PPAR gamma may be a key factor in the occurrence of Sola Fini resistance in liver cancer, and its antagonist T007 0907, it can enhance the sensitivity of liver cancer cells and has potential clinical application value.

【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：R735.7

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