基于外周血多参数联合分析作为女性恶性肿瘤
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摘要
目的：探索基于血清多参数联合诊断用于鉴别女性恶性肿瘤与健康对照组的诊断价值,为临床诊断提供一种分子辅助诊断方法。按照生物信息学方向流动,从不同的视角研究探索女性恶性肿瘤尤其是卵巢癌及乳腺癌的诊断模式,试图筛选高灵敏、高特异的新的肿瘤生物标志物。
方法：收集卵巢肿瘤(Ovarian tumors)158例、卵巢癌(ovarian cancer)137例,卵巢良性肿瘤13例、卵巢交界性肿瘤8例及健康对照组40例,乳腺癌60例,乳腺良性肿瘤20例,建立临床数据库。①流行病学调查及统计学分析卵巢癌临床实验室指标的差异；对生化指标及肿瘤标记物CA125及其余肿瘤标志物进行生物信息学分析。②采用旷博三步法的免疫荧光定量PCR方法测量乳腺癌中血清miRNA水平,分析了不同组间miRNA表达的差异。首先应用标准的miRNA提取方法。设定miRNA的内参、质控,在第一步,加多聚A尾,在miRNA和其他非编码RNA分子的3’端。第二步,反转录miRNA为cDNA的并在5端加特异性标签。最后,在一个特异性的miRNA的正向引物和反向通用引物组合的进行qPCR反应,并测量；③用基质辅助激光解离时间飞行质谱(MALDI-TOF-MS)绘制卵巢癌蛋白多肽图谱,用MB-WCX磁珠优化处理外周血清标本,用Clinprot软件分析卵巢癌与健康对照的蛋白多肽差异,结合生物信息学技术分析,分别分析比较了卵巢癌不同分期、分级及病理类型的多肽表达差异；④Luminex液态芯片检测血清中炎性因子GM-CSF、IFN-γ、GRO、 IL-1β、IL-2、IL-6、IL-8、MCP-1、TNF-α、VEGF、EGF、RANTES、CCL21/6Ckine和SDF-1/CXCL12等指标,比较其在卵巢浆液性上皮癌与健康对照组的差异,并探讨其作为肿瘤标志物的预测价值。数据资料用SPSS19.0计算机统计软件及ClinPro Tools软件分析。
结果：①建立临床数据库；回顾分析了卵巢癌患者的病史资料特点,女性肿瘤标志物CEA, AFP, CA125, CA199, CA153, CA724, SCC, NSE, HCG, CYFRA21-1, Ferr在卵巢癌组显著上调,CA125表达最显著,但仅有54%的患者CA125表达阳性,CA125表达阳性率随期别升高而增高。CA153在诊断卵巢与健康对照组时的曲线下面积达到0.827,敏感性80.3%,但是,特异性为72.6%。CA125敏感性仅为64.3%,但特异性好,特异度为90.4%。CA125与CA153联合诊断的曲线下面积可以达到0.877,敏感度上升73.5%,而仍然保持良好的特异度90.5%。一些与代谢有关的生化指标显著升高。如GGT、HDL、CK-MB、CO2、ALP、TG、CK-MB、均高于健康对照组。②miR-451, miR-148a, miR-27a,和miR-30b在乳腺癌患者血清中表达下调(miR-451:P=0.000; miR-148a:P=0.021; miR-27a:P=0.013and miR-30b:P=0.001)。miR-451, miR-27a和miR-30b在乳腺良性疾病中也下调miR-451:P=0.002; miR-27a:P=0.012和miR-30b:P=0.046。其中miR-451在这些指标中被认为敏感度最高,表达差异最大,可以被用来进行乳腺疾病与健康对照组的初筛,miR-148a在区别卵巢良性肿瘤和恶性肿瘤时有一定的意义。miR-451, miR-148a, miR-27a口miR-30b作为标志物集用于诊断乳腺癌,ROC工作曲线下面积可达到0.953,敏感性94.7%,特异性82.8%。单一的miR-148在区分卵巢良恶性肿瘤时,能够达到56.1%的敏感性,78.9%的特异性,曲线下面积达69.8%。③卵巢癌不同病理类型、不同分期及分级的差异蛋白。4个差异多肽与卵巢癌的分级有关,分别为2884.4Da,2899.9Da,3542.9Da,2864.6Da。其中2884.4Da峰差异最显著,在高分化的卵巢癌中表达较高,在中分化、低分化组中表达明显下调。8个多肽峰与卵巢癌的分期有关,在晚期癌症中表达上调的是2683.5Da,2661.3Da,2670.6Da,2952.0Da,表达下调的是1532.7Da,1980.3Da,2884.4Da,1804.7Dao同源的肿瘤,差异表达的多肽也少。建立回归方程Y=0.691+0.398X2487-0.638X1867,诊断价值最高,ROC曲线下面积为0.855,敏感度可达100%,特异度达到60.9%,Youden指数0.609。④卵巢癌组EGF, IL-6, MCP-1,6Ckine的血清中值水平明显升高,与健康对照组比较(p<0.001),而IL-2的血清水平在卵巢癌中下降。根据差异表达的各项因子多因素回归后建立诊断方程Y=-3.707+0.009X EGF+0.001XMCP+0.014XCkine,诊断模型的敏感度为72.7%,特异度为83.8%, ROC曲线下面积为0.845。
结论：以女性肿瘤卵巢癌、乳腺癌临床数据资料为基础,采用目前比较前沿的分子诊断技术,整合基因组学、蛋白组学技术的生物信息学分析,可建立更准确的临床诊断模型,为癌症早期诊断提供有利工具,结果有待进一步验证。
Objective:Based on the serum indicators, we aimed to identify the diagnostic value of healthy controls and disease groups, which may provide a complementary diagnostic method for clinical diagnosis. We investigated the difference of genomics and serological proteomics in female cancer compared to health controls, try to explore the high sensitive and specific diagnostic model and find new biomarkers forovarian cancer and breast cancer.
Methods:The serum and clinical parameter of female ovarian tumors (n=158), ovarian cancer (n=137), benign ovarian tumor (n=13), borderline ovarian tumors (n=8) breast cancer (n=60), benign breast tumor (n=20) and healthy controls (n=40) were collected for clinical data set.①The epidemiology and statistics analyzed the difference of laboratory parameters between cancer group and healthcontrol.②All the samples were analyzed using three steps based quantitative PCR method. Total RNA was isolated from serum samples using the standard Quantobio microRNA isolation procedures. All samples passed quality control. The synthesized miRNA internal control, QuantoIC1, was extrinsically supplied as normalization controls for miRNA quantification. The expression levels of miRNAs in different groups were analyzed.③All the serum samples were treated by MB-WCX beads firstly and the proteomic profilings were attained by Clinprot/Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS).④A set of candidate cytokines and chemokines (GM-CSF,IFN-y,GRO,IL-1β,IL-2,IL-6,IL-8,MCP-1,TNF-α,VEGF,EGF,RANTES,C CL21/6Ckine,SDF-1/CXCL12) were measured by using Luminex liquid chip technique. Data were analysed by SPSS19.0and ClinPro Tools.
Results:①After establishing the clinical data base, most of laboratory parameters in ovarian cancer groups were distinct with controls groups, Median level of CA125in ovarian cancer was higher than in healthcontrols (P=0.001), but only54%of the patients with CA125positive expression, positive rate of CA125increased with late stages. CA153has a sensitivity of80.3%, a specificity of72.6%in predicting ovarian cancer, while CA125has a sensitivity of64.3%, a specificity of90.4%. The combination of CA125and CA153may yield an increase sensitivity of73.5%, while still maintaining the specificity of90.5%, with the area under the curve of0.877;②Four out of the six candidate microRNAs tended to be differentially expressed in serum samples between patients with breast cancer and healthy controls: miR-451:P<0.001; miR-148a:P=0.021; miR-27a:P=0.013and miR-30b:P=0.001. Three out of the six miRNAs (miR-451, miR-27a, and miR-30b) showed different expression levels between benign breast tumor groups and healthy controls:miR-451: P=0.002; miR-27a:P=0.012and miR-30b:P=0.046. A panel of miRNAs consisting of the four down-regulated miRNAs can distinguish breast cancer from healthy controls well, with an area under the receiver operating characteristic curve (ROC) of95.3%, a sensitivity of94.7%, and a specificity of82.8%. miR-148a levels differentiated between malignant and benign breast masses, with an AUC of69.8%, a sensitivity of56.1%, and a specificity of78.9%.③The specific model Y=0.691+0.398X2487-0.638Xi867comprised of two peptides2847Da,1867Da could distinguish ovarian cancer from healthy group showed100%of sensitivity and60.9%of specificity by ROC analysis, the areaunder ROC curve attain to85.5%. Little disparity of peptidomic profiling was scantly found between homologous tumors. m/z2884.4Da showed a higher expression level in well-differentiated ovarian cancer. m/z2681.5Da,2659.8Da,2670.6Da,2952.0Da had a relatively high level in advanced ovarian cancer.④EGF,IL-6,MCP-1,6Ckine,RANTES, IL-10were significantly over-expressed in the tumor group compared with those in normal controls, while IL-2reduced. The model of binary logistic regression equationY=-3.707+0.009X EGF+0.001X MCP+0.014X Ckine, have a sensitivity of72.7%and a specificity of83.8%for predicting ovarian cancer, the receiver operating characteristic (ROC) curve analysis using five combined markers yielded an AUC of84.5%.
Conclusions:Based on the clinical data base of ovarian cancer, the combination of bioinformatics and clinical data could set up simpler clinical diagnostic model; meanwhile, peptides and microRNA may be considered to be the ideal biomarker. Cytokines and chemokines have diagnostic value, but not reach the desired level.
方法：收集卵巢肿瘤(Ovarian tumors)158例、卵巢癌(ovarian cancer)137例,卵巢良性肿瘤13例、卵巢交界性肿瘤8例及健康对照组40例,乳腺癌60例,乳腺良性肿瘤20例,建立临床数据库。①流行病学调查及统计学分析卵巢癌临床实验室指标的差异；对生化指标及肿瘤标记物CA125及其余肿瘤标志物进行生物信息学分析。②采用旷博三步法的免疫荧光定量PCR方法测量乳腺癌中血清miRNA水平,分析了不同组间miRNA表达的差异。首先应用标准的miRNA提取方法。设定miRNA的内参、质控,在第一步,加多聚A尾,在miRNA和其他非编码RNA分子的3’端。第二步,反转录miRNA为cDNA的并在5端加特异性标签。最后,在一个特异性的miRNA的正向引物和反向通用引物组合的进行qPCR反应,并测量；③用基质辅助激光解离时间飞行质谱(MALDI-TOF-MS)绘制卵巢癌蛋白多肽图谱,用MB-WCX磁珠优化处理外周血清标本,用Clinprot软件分析卵巢癌与健康对照的蛋白多肽差异,结合生物信息学技术分析,分别分析比较了卵巢癌不同分期、分级及病理类型的多肽表达差异；④Luminex液态芯片检测血清中炎性因子GM-CSF、IFN-γ、GRO、 IL-1β、IL-2、IL-6、IL-8、MCP-1、TNF-α、VEGF、EGF、RANTES、CCL21/6Ckine和SDF-1/CXCL12等指标,比较其在卵巢浆液性上皮癌与健康对照组的差异,并探讨其作为肿瘤标志物的预测价值。数据资料用SPSS19.0计算机统计软件及ClinPro Tools软件分析。
结果：①建立临床数据库；回顾分析了卵巢癌患者的病史资料特点,女性肿瘤标志物CEA, AFP, CA125, CA199, CA153, CA724, SCC, NSE, HCG, CYFRA21-1, Ferr在卵巢癌组显著上调,CA125表达最显著,但仅有54%的患者CA125表达阳性,CA125表达阳性率随期别升高而增高。CA153在诊断卵巢与健康对照组时的曲线下面积达到0.827,敏感性80.3%,但是,特异性为72.6%。CA125敏感性仅为64.3%,但特异性好,特异度为90.4%。CA125与CA153联合诊断的曲线下面积可以达到0.877,敏感度上升73.5%,而仍然保持良好的特异度90.5%。一些与代谢有关的生化指标显著升高。如GGT、HDL、CK-MB、CO2、ALP、TG、CK-MB、均高于健康对照组。②miR-451, miR-148a, miR-27a,和miR-30b在乳腺癌患者血清中表达下调(miR-451:P=0.000; miR-148a:P=0.021; miR-27a:P=0.013and miR-30b:P=0.001)。miR-451, miR-27a和miR-30b在乳腺良性疾病中也下调miR-451:P=0.002; miR-27a:P=0.012和miR-30b:P=0.046。其中miR-451在这些指标中被认为敏感度最高,表达差异最大,可以被用来进行乳腺疾病与健康对照组的初筛,miR-148a在区别卵巢良性肿瘤和恶性肿瘤时有一定的意义。miR-451, miR-148a, miR-27a口miR-30b作为标志物集用于诊断乳腺癌,ROC工作曲线下面积可达到0.953,敏感性94.7%,特异性82.8%。单一的miR-148在区分卵巢良恶性肿瘤时,能够达到56.1%的敏感性,78.9%的特异性,曲线下面积达69.8%。③卵巢癌不同病理类型、不同分期及分级的差异蛋白。4个差异多肽与卵巢癌的分级有关,分别为2884.4Da,2899.9Da,3542.9Da,2864.6Da。其中2884.4Da峰差异最显著,在高分化的卵巢癌中表达较高,在中分化、低分化组中表达明显下调。8个多肽峰与卵巢癌的分期有关,在晚期癌症中表达上调的是2683.5Da,2661.3Da,2670.6Da,2952.0Da,表达下调的是1532.7Da,1980.3Da,2884.4Da,1804.7Dao同源的肿瘤,差异表达的多肽也少。建立回归方程Y=0.691+0.398X2487-0.638X1867,诊断价值最高,ROC曲线下面积为0.855,敏感度可达100%,特异度达到60.9%,Youden指数0.609。④卵巢癌组EGF, IL-6, MCP-1,6Ckine的血清中值水平明显升高,与健康对照组比较(p<0.001),而IL-2的血清水平在卵巢癌中下降。根据差异表达的各项因子多因素回归后建立诊断方程Y=-3.707+0.009X EGF+0.001XMCP+0.014XCkine,诊断模型的敏感度为72.7%,特异度为83.8%, ROC曲线下面积为0.845。
结论：以女性肿瘤卵巢癌、乳腺癌临床数据资料为基础,采用目前比较前沿的分子诊断技术,整合基因组学、蛋白组学技术的生物信息学分析,可建立更准确的临床诊断模型,为癌症早期诊断提供有利工具,结果有待进一步验证。
Objective:Based on the serum indicators, we aimed to identify the diagnostic value of healthy controls and disease groups, which may provide a complementary diagnostic method for clinical diagnosis. We investigated the difference of genomics and serological proteomics in female cancer compared to health controls, try to explore the high sensitive and specific diagnostic model and find new biomarkers forovarian cancer and breast cancer.
Methods:The serum and clinical parameter of female ovarian tumors (n=158), ovarian cancer (n=137), benign ovarian tumor (n=13), borderline ovarian tumors (n=8) breast cancer (n=60), benign breast tumor (n=20) and healthy controls (n=40) were collected for clinical data set.①The epidemiology and statistics analyzed the difference of laboratory parameters between cancer group and healthcontrol.②All the samples were analyzed using three steps based quantitative PCR method. Total RNA was isolated from serum samples using the standard Quantobio microRNA isolation procedures. All samples passed quality control. The synthesized miRNA internal control, QuantoIC1, was extrinsically supplied as normalization controls for miRNA quantification. The expression levels of miRNAs in different groups were analyzed.③All the serum samples were treated by MB-WCX beads firstly and the proteomic profilings were attained by Clinprot/Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS).④A set of candidate cytokines and chemokines (GM-CSF,IFN-y,GRO,IL-1β,IL-2,IL-6,IL-8,MCP-1,TNF-α,VEGF,EGF,RANTES,C CL21/6Ckine,SDF-1/CXCL12) were measured by using Luminex liquid chip technique. Data were analysed by SPSS19.0and ClinPro Tools.
Results:①After establishing the clinical data base, most of laboratory parameters in ovarian cancer groups were distinct with controls groups, Median level of CA125in ovarian cancer was higher than in healthcontrols (P=0.001), but only54%of the patients with CA125positive expression, positive rate of CA125increased with late stages. CA153has a sensitivity of80.3%, a specificity of72.6%in predicting ovarian cancer, while CA125has a sensitivity of64.3%, a specificity of90.4%. The combination of CA125and CA153may yield an increase sensitivity of73.5%, while still maintaining the specificity of90.5%, with the area under the curve of0.877;②Four out of the six candidate microRNAs tended to be differentially expressed in serum samples between patients with breast cancer and healthy controls: miR-451:P<0.001; miR-148a:P=0.021; miR-27a:P=0.013and miR-30b:P=0.001. Three out of the six miRNAs (miR-451, miR-27a, and miR-30b) showed different expression levels between benign breast tumor groups and healthy controls:miR-451: P=0.002; miR-27a:P=0.012and miR-30b:P=0.046. A panel of miRNAs consisting of the four down-regulated miRNAs can distinguish breast cancer from healthy controls well, with an area under the receiver operating characteristic curve (ROC) of95.3%, a sensitivity of94.7%, and a specificity of82.8%. miR-148a levels differentiated between malignant and benign breast masses, with an AUC of69.8%, a sensitivity of56.1%, and a specificity of78.9%.③The specific model Y=0.691+0.398X2487-0.638Xi867comprised of two peptides2847Da,1867Da could distinguish ovarian cancer from healthy group showed100%of sensitivity and60.9%of specificity by ROC analysis, the areaunder ROC curve attain to85.5%. Little disparity of peptidomic profiling was scantly found between homologous tumors. m/z2884.4Da showed a higher expression level in well-differentiated ovarian cancer. m/z2681.5Da,2659.8Da,2670.6Da,2952.0Da had a relatively high level in advanced ovarian cancer.④EGF,IL-6,MCP-1,6Ckine,RANTES, IL-10were significantly over-expressed in the tumor group compared with those in normal controls, while IL-2reduced. The model of binary logistic regression equationY=-3.707+0.009X EGF+0.001X MCP+0.014X Ckine, have a sensitivity of72.7%and a specificity of83.8%for predicting ovarian cancer, the receiver operating characteristic (ROC) curve analysis using five combined markers yielded an AUC of84.5%.
Conclusions:Based on the clinical data base of ovarian cancer, the combination of bioinformatics and clinical data could set up simpler clinical diagnostic model; meanwhile, peptides and microRNA may be considered to be the ideal biomarker. Cytokines and chemokines have diagnostic value, but not reach the desired level.
引文
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