新疆地区肾癌血清肿瘤标志物的筛查及其维、汉民族差异的研究
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摘要
目的：通过探索新疆地区肾癌患者与健康者之间血清蛋白表达谱的变化，汉族与维吾尔族肾癌患者之间血清蛋白表达谱的变化以及肾癌患者手术治疗前后的血清蛋白表达谱变化，①筛选并建立新疆地区肾癌血清标志物诊断模型；②建立维吾尔族、汉族不同民族肾癌血清诊断模型并筛查两个不同民族肾癌患者之间的差异蛋白；③筛选肾癌术后复发、转移及预后监测指标。方法：采用表面增强激光解吸离子化飞行时间质谱技术和弱阳离子交换蛋白芯片（CM10）检测血清标本，所得到的数据采用浙江大学蛋白质质谱数据分析系统软件包进行分析研究。蛋白质指纹图诊断模型建立是应用支持向量机方法来完成的，模型判别效果的评估采用留一法交叉验证。采用Wilconxon秩和检验进行两组间的蛋白质峰的比较，P＜0.05有统计学意义。1）对新疆地区44例健康对照与40例肾癌患者血清蛋白表达谱进行检测分析；2）对新疆地区24例维吾尔族肾癌患者与24例维吾尔族健康对照血清蛋白表达谱进行检测分析；对36例汉族肾癌患者与36例汉族健康对照血清蛋白质指纹图谱进行检测分析；3）对新疆地区40例肾癌患者手术治疗前以及手术后血清蛋白表达谱进行检测分析。结果：1）相比于健康对照组血清蛋白质荷比峰值图谱，肾癌组表现出明显差异。其中10个差异性最大的蛋白（P＜0.05），均在健康对照组中低表达，而在肾癌组呈高表达，质荷比分别为5914、5940、8087、8155、5949、8067、5986、5346、15946、6116。建立的诊断模型是由10个蛋白（质荷比分别为：5914、5940、8087、8155、5949、8067、5986、5346、15946、6116）构成。应用十倍交叉留一法进行验证，结果此诊断模型的敏感度为92.50%（37/40），特异度为93.18%（41/44）；2）维吾尔族肾癌组与维吾尔族健康对照组组间得到的有显著性差异的6个蛋白质峰，差异具有统计学意义（P＜0.05），质荷比（M/Z）分别为5935、5945、5911、13766、13965、6887，六个蛋白峰均在维吾尔族肾癌组中高表达，在维吾尔族健康对照组中低表达。建立的诊断模型是由这6个蛋白构成。应用十倍交叉留一法进行验证，结果此诊断模型的敏感度为100.00%（24/24），特异度为91.67%（22/24）；汉族肾癌组与汉族健康对照组组间得到的有显著性差异的4个蛋白质峰，差异具有统计学意义（P＜0.05），质荷比（M/Z）分别为5937、5345、5947、5912，4个蛋白峰均在汉族肾癌组中高表达，在汉族健康对照组中低表达。建立的诊断模型是由这4个蛋白构成。应用十倍交叉留一法进行验证，结果此诊断模型的敏感度为91.67%（33/36），特异度为94.44%（34/36）；维吾尔族与汉族两个不同民族肾癌之间的血清蛋白质指纹图谱存在着较明显的差异。质荷比为6887、13766、13965的3个蛋白峰为维吾尔族肾癌诊断模型所独有，而质荷比为5345的蛋白峰为汉族肾癌诊断模型所独有；3）新疆地区肾癌患者手术治疗前与手术治疗后的血清蛋白质指纹图谱存在着较明显的差异，表达差异最大的19个蛋白质峰（P＜0.05）中质荷比为8777、2748、8646、8734、8701、8087、7982、8155、8072、15946、16141、16306、16116、8135、8017、2760、6672、4301、6471手术后表达低于手术前。其中3个蛋白质峰（质荷比为8155，8087，15946）与此前筛选到的肾癌患者与正常对照之间差异蛋白质峰相同。结论：1）将表面增强激光解吸电离飞行时间质谱技术以及生物信息学分析软件的联合运用是找到肾癌肿瘤标志物的一种有效的方法，建立的诊断模型能够较准确灵敏的区分健康对照与肾癌人群。其中M/Z5914、5940、8067、6116对应的蛋白质可能为β-淀粉样蛋白、人体β-防御素、胃泌素前体以及金属硫蛋白。2）构建的维吾尔族肾癌诊断模型能够较准确灵敏的区分维吾尔族健康对照与维吾尔族肾癌人群，构建的汉族肾癌诊断模型能够较准确灵敏的区分汉族健康对照与汉族肾癌人群；新疆地区维吾尔族肾癌与汉族肾癌患者之间血清蛋白质指纹图谱存在较明显的差异，质荷比为6887、13766、13965、5345的蛋白峰可能是维汉不同民族肾癌的差异蛋白。其中M/Z为6887、13766、13965蛋白峰对应的可能蛋白质分别为为角蛋白、甲状腺素转运蛋白及干扰素诱导的跨膜蛋白-1。3）新疆肾癌患者手术前后血清蛋白质组学变化研究筛选到的3个蛋白质峰（M/Z8155，8087，15946，）可能就是肿瘤分泌的特异性标记物。其对应的可能蛋白质分别为唾液酸转移酶I、CCR4趋化因子受体以及细胞凋亡调节蛋白。
Objective:To explore serum protein change between renal cell carcinoma patients,and the healthy person, and between Uygur with RCC and Han with RCC, and theperioperative dynamic variation of patients with RCC in XinJiang,①To screen andconstruct diagnostic model of RCC in XinJiang;②To construct differentethnic(including Han and Uygur)diagnostic model of RCC in XinJiang and screen serumdifferentially expressed protein between Han with renal cancer and Uygur with renalcancer in Xinjiang;③To screen the perioperative dynamic variation of serumdifferentially expressed protein for prognostic monitoring of recurrence and metastasisafter surgery in patients with RCC. Methods: To use weak cation exchange andhydrophobic surface protein chip (CM10) and Surface-enhanced laser desorptionionization time of flight mass spectrometry (SELDI-TOF-MS) to detect the serum protein.The data was analyzed and the diagnostic model was established by using ZhejiangUniversity Protein Chip Data Analyze System software package (ZUCI-PDAS). Supportvector machine (SVM) analyze the data of spectra and establisha diagnostic model,which it was evaluated and validated by leave one cross validation. Wilconxon rank sumtest was used to compare two groups and it will has statistical significance when p valuewas less than0.05.1)To analyze serum specimens from40RCC patients and44healthycontrols in order to screen out the serum differentially expressed proteins of RCC andconstruct RCC diagnostic model;2) To detected serum specimens from patients (36Hanand24Uygur)with RCC and60healthy controls so as to construct different ethnic RCCdiagnostic model and screen out disparate serum proteins between Han with RCC andUygur with RCC.3) To screen serum protein change between preoperative andpostoperative serum specimens from40patients so as to screen out monitering dynamicvariation of biomaker. Results:1) Significantly difference existed between healthycontrols and RCC patients (P＜0.05). total ten proteins peaks(5914,5940,8087,8155, 5949,8067,5986,5346,15946,6116) were all high-expressed in RCC group and all ofthem were low-expressed in healthy controls. After cross validation, diagnostic modelwas composed and established by ten proteins. The specificity and sensitivity were93.18%(41/44)and92.50%(37/40);2) Obvious disparity existed between healthy controlsand RCC patients in Uygur (P＜0.05). All of them (5935,5945,5911,13766,13965,6887) were high-expressed in RCC group and all of them were low-expressed in healthycontrols. After cross validation, diagnostic model was composed and established by sixproteins. The specificity and sensitivity were91.67%(22/24) and100.00%(24/24);Obvious disparity existed between healthy controls and RCC patients in Han (P＜0.05).Total four proteins peaks (5937,5345,5947,5912) were all high-expressed in RCCgroup and all of them were low-expressed in healthy controls. After cross validation,diagnostic model was composed and established by four proteins. The specificity andsensitivity were94.44%(34/36)and91.67%(33/36); disparity existed between UygurRCC patients and Han RCC patients in terms of serum protein finger prints. Threeproteins peaks M/Z6887,13766,13965existed diagnostic model of Uygur patients groupwhile lacked in Han patients group, meanwhile, one proteins peaks M/Z5345existeddiagnostic model of Han patients group while lacked in Uygur patients group;3) It hadconspicuous distinction of serum protein profiling between postoperative andpreoperative patients (P＜0.05). Among them,19proteins peaks (8777,2748,8646,8734,8701,8087,7982,8155,8072,15946,16141,16306,16116,8135,8017,2760,6672,4301,6471) were all high-expressed in preoperative group and all of them werelow-expressed in postoperative group. Three(8155,8087,15946)of protein peaks weresame as preoperative sample, which was higher in preoperative patients than healthcontrol, was higher postoperatively. Conclusion:1) It is a effective method for theaffiliation of bioinformatics software analysis and SELDI-TOF-MS to look forbiomarkers of renal cell carcinoma (RCC), RCC and healthy controls in Xinjaing can beaccurately distinguished by constitutive sensitive diagnostic model; Four (M/Z5914,5940,8067,6116) of protein peaks may be Amyloid betaA4protein, HumanBeta-defensin, ProGRP and Metallothionein;2)RCC and healthy controls in Uygur andHan in Xinjaing can be accurately distinguished by constitutive sensitive diagnosticmodel respectively; It has difference for protein finger prints between Han with RCC andUygur with RCC in Xinjing. M/Z6887,13766,13965,5345may be differential markersbetween Uygur with RCC and Han with RCC and M/Z6887,13766,13965may beKeratin, transthyretin and interferon-induced transmembrane protein-1;3) The three screened protein peaks (M/Z8155,8087,15946) between preoperative and postoperativepatients may be specific markers secreted by RCC, which may be ST6Gal I, CCchemokine receptor4and GW112.
Objective:To explore serum protein change between renal cell carcinoma patients,and the healthy person, and between Uygur with RCC and Han with RCC, and theperioperative dynamic variation of patients with RCC in XinJiang,①To screen andconstruct diagnostic model of RCC in XinJiang;②To construct differentethnic(including Han and Uygur)diagnostic model of RCC in XinJiang and screen serumdifferentially expressed protein between Han with renal cancer and Uygur with renalcancer in Xinjiang;③To screen the perioperative dynamic variation of serumdifferentially expressed protein for prognostic monitoring of recurrence and metastasisafter surgery in patients with RCC. Methods: To use weak cation exchange andhydrophobic surface protein chip (CM10) and Surface-enhanced laser desorptionionization time of flight mass spectrometry (SELDI-TOF-MS) to detect the serum protein.The data was analyzed and the diagnostic model was established by using ZhejiangUniversity Protein Chip Data Analyze System software package (ZUCI-PDAS). Supportvector machine (SVM) analyze the data of spectra and establisha diagnostic model,which it was evaluated and validated by leave one cross validation. Wilconxon rank sumtest was used to compare two groups and it will has statistical significance when p valuewas less than0.05.1)To analyze serum specimens from40RCC patients and44healthycontrols in order to screen out the serum differentially expressed proteins of RCC andconstruct RCC diagnostic model;2) To detected serum specimens from patients (36Hanand24Uygur)with RCC and60healthy controls so as to construct different ethnic RCCdiagnostic model and screen out disparate serum proteins between Han with RCC andUygur with RCC.3) To screen serum protein change between preoperative andpostoperative serum specimens from40patients so as to screen out monitering dynamicvariation of biomaker. Results:1) Significantly difference existed between healthycontrols and RCC patients (P＜0.05). total ten proteins peaks(5914,5940,8087,8155, 5949,8067,5986,5346,15946,6116) were all high-expressed in RCC group and all ofthem were low-expressed in healthy controls. After cross validation, diagnostic modelwas composed and established by ten proteins. The specificity and sensitivity were93.18%(41/44)and92.50%(37/40);2) Obvious disparity existed between healthy controlsand RCC patients in Uygur (P＜0.05). All of them (5935,5945,5911,13766,13965,6887) were high-expressed in RCC group and all of them were low-expressed in healthycontrols. After cross validation, diagnostic model was composed and established by sixproteins. The specificity and sensitivity were91.67%(22/24) and100.00%(24/24);Obvious disparity existed between healthy controls and RCC patients in Han (P＜0.05).Total four proteins peaks (5937,5345,5947,5912) were all high-expressed in RCCgroup and all of them were low-expressed in healthy controls. After cross validation,diagnostic model was composed and established by four proteins. The specificity andsensitivity were94.44%(34/36)and91.67%(33/36); disparity existed between UygurRCC patients and Han RCC patients in terms of serum protein finger prints. Threeproteins peaks M/Z6887,13766,13965existed diagnostic model of Uygur patients groupwhile lacked in Han patients group, meanwhile, one proteins peaks M/Z5345existeddiagnostic model of Han patients group while lacked in Uygur patients group;3) It hadconspicuous distinction of serum protein profiling between postoperative andpreoperative patients (P＜0.05). Among them,19proteins peaks (8777,2748,8646,8734,8701,8087,7982,8155,8072,15946,16141,16306,16116,8135,8017,2760,6672,4301,6471) were all high-expressed in preoperative group and all of them werelow-expressed in postoperative group. Three(8155,8087,15946)of protein peaks weresame as preoperative sample, which was higher in preoperative patients than healthcontrol, was higher postoperatively. Conclusion:1) It is a effective method for theaffiliation of bioinformatics software analysis and SELDI-TOF-MS to look forbiomarkers of renal cell carcinoma (RCC), RCC and healthy controls in Xinjaing can beaccurately distinguished by constitutive sensitive diagnostic model; Four (M/Z5914,5940,8067,6116) of protein peaks may be Amyloid betaA4protein, HumanBeta-defensin, ProGRP and Metallothionein;2)RCC and healthy controls in Uygur andHan in Xinjaing can be accurately distinguished by constitutive sensitive diagnosticmodel respectively; It has difference for protein finger prints between Han with RCC andUygur with RCC in Xinjing. M/Z6887,13766,13965,5345may be differential markersbetween Uygur with RCC and Han with RCC and M/Z6887,13766,13965may beKeratin, transthyretin and interferon-induced transmembrane protein-1;3) The three screened protein peaks (M/Z8155,8087,15946) between preoperative and postoperativepatients may be specific markers secreted by RCC, which may be ST6Gal I, CCchemokine receptor4and GW112.
引文
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