肿瘤血液标志物的研发及临床评价课件

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单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,2016/12/17,#,肿瘤血液标志物的研发及临床评价,Zhang Peng-jun,肿瘤血液标志物的研发及临床评价Zhang Peng-ju,Ten Leading Cancer Types for the Estimated New Cancer Cases and Deaths by Sex,United States,2013,CA Cancer J Clin.2013,63(1):11-30,Ten Leading Cancer Types for t,Nature Reviews,Cancer,2011,426-437,Cell free nucleic acids,Nature Reviews Cancer,2011,4,Outline of strategies for biomarker discovery through utilization of emerging technologies.,Nat Clin Pract Oncol.2008,5(10):588-99,Outline of strategies for biom,Cancer biomarkers that are currently in clinical use,Nat Clin Pract Oncol.2008,5(10):588-99,Cancer biomarkers that are cur,肿瘤血液标志物的研发及临床评价课件,Multi-parameter panels can significantly improve the diagnostic value,compared to the conventional biomarker,Multi-parameter panels can sig,8,Methylation profiling of serum DNA from hepatocellular,carcinoma patients using an Infinium Human Methylation,450 BeadChip,8Methylation profiling of seru,肿瘤血液标志物的研发及临床评价课件,肿瘤血液标志物的研发及临床评价课件,A,.,6,例早期原发性肝癌和,6,例健康对照的全基因组甲基化水平,。,B,.,早期原发性肝癌组和健康对照组全基因组甲基化水平比较,A.6例早期原发性肝癌和6例健康对,与,健康对照组相比,,,早期原发性肝癌组,差异,甲基化位点的个数及倍数关系,与,健康对照组相比,,,早期原发性肝癌组,差异甲基化位点,的,基因分布,与健康对照组相比,早期原发性肝癌组差异甲基化位点的基因分布,值大于,0.5,为,超甲基,化,值小于,0.2,为,超低甲基,化,我们,筛选出,453,个健康对照组和,37,个原发性肝细胞癌组的超甲基化位点。,超,甲基化位点的聚类分析,(,欧氏距离,),值大于0.5为超甲基化超甲基化位点的聚类分析(欧氏距离),超,甲基化位点,的,基因本体论和基因功能富集分析,超甲基化位点的基因本体论和基因功能富集分析,原发性,肝细胞癌,组,中超甲基化位点,的,GO,及,功能,富集分析,原发性肝细胞癌组中超甲基化位点的GO及功能富集分析,健康,对照,组中超甲基化位点,的,GO,及,功能,富集分析,健康对照组中超甲基化位点的GO及功能富集分析,基因,功能富集分析后健康对照组中超甲基化位点的基因相互作用分析,基因功能富集分析后健康对照组中超甲基化位点的基因相互作,DBX2,和,THY1,甲基化位点通过亚硫酸盐测序法验证,DBX2和THY1甲基化位点通过亚硫酸盐测序法验证,DBX2,和,THY1,甲基化位点通过亚硫酸盐测序法,进行,扩,大,样本验证,A.,DBX2,和,THY1,甲基化位点在原发性肝癌组和健康对照组的甲基化水平比较。,B.,DBX2,和,THY1,甲基化位点用于区分原发性肝癌组和健康对照组的诊断价值,DBX2和THY1甲基化位点通过亚硫酸盐测序法进行扩大样本验,Multi-parameter model for diagnosis of cancer,21,Multi-parameter model for diag,29,例慢性,胰腺炎,82,例急性,胰腺炎,162,例胰腺,癌,33,例,结肠炎,62,例,结肠息肉,101,例,结肠癌,20,例结肠癌术,后,40,例胃,增生,139,例,胃癌,31,例慢性,肺炎,28,例小细胞,肺癌,32,例肺鳞,癌,72,例肺,腺癌,22,例,乳腺增生,101,例,乳腺癌,65,例宫颈良性,病变,72,例宫颈,癌,18,例宫颈癌术,后,84,例,前列腺增生,108,例,前列腺癌,12,例前列腺癌术,后,200,例健康,对照,胰腺,疾病,结肠疾病,胃疾病,肺疾病,乳腺疾病,宫颈疾病,前列腺疾病,正常对照,ALB,、,ALP,、,ALT,、,ApoA1,、,ApoB,、,AST,、,Ca,、,CHO,、,CK,、,CKMB,、,CO,2,、,Cl,、,CYS,、,CR,、,DB,、,GGT,、,GLU,、,HDL,、,HCY,、,K,、,LDL,、,LP(a),、,Mg,、,Na,、,P,、,SA,、,TP,、,TB,、,TBA,、,TG,、,UN,和,UA,GM-CSF,、,IFN-,、,IL-10,、,IL-1,、,IL-2,、,IL-4,、,IL-6,、,IL-8,、,MCP-1,和,TNF,PG1,、,PG2,和,SCC,AFP,、,CA125,、,CA153,、,CA199,、,CA724,、,CY211,、,CEA,、,FERR,和,NSE,1513,例,29例慢性胰腺炎胰腺疾病结肠疾病胃疾病肺疾病乳腺疾病宫颈疾病,For,differentiating between the colorectal adenoma and colorectal cancer groups.The AUC of multivariate logistic regression was 0.945(95%CI:0.9090.981).Compared with the conventional biomarkers CEA and CA199,the AUC of multivariate logistic regression showed significant improvements(p 0.05).AUC:Area under the curve.,Future Oncology,2013,For differentiating between th,肿瘤血液标志物的研发及临床评价课件,肿瘤血液标志物的研发及临床评价课件,26,The red line was the leave-one-out cross-validation(LOOCV)accuracy curve varying,with k,and each asterisk represent the value of accuracy with a fixd k,ROC curves for the top five features and two panels of biomarkers selected by our model.,26 The red line was the leav,肿瘤血液标志物的研发及临床评价课件,Figure 1.,Flowchart of our experimental design,Figure 1.Flowchart of our exp,Table 1.,Clinical characteristic of the studied subjects,Table 1.Clinical characterist,Table 2,.List of the serum parameters in the 61-plex panel,Table 2.List of the serum par,Table 3.,The sensitivity of top 20 performing 4 serum parameter panels for discriminating PDAC,Vs,Control and PDAC,Vs,Benign group identified by MMC algorithm applied to the training set at 90%specificity,Table 3.The sensitivity of to,Figure 2,.ROC curves analysis for discriminating between the PDAC versus Control and PDAC versus Benign groups in the training group.ROC curves of CA19-9,ALB,CRP and IL-8 panel(solid line)and CA19-9(dotted line)for discriminating between PDAC and Ctrl in the training group(a)and validation group(c).ROC curves of the panel consisting of CA19-9,CO,2,CRP and IL-6(solid line)and CA19-9(dotted line)for discriminating between PDAC and Benign in the training group(b)and validation group(d).,Figure 2.ROC curves analysis,33,Thanks for your attention,33Thanks for your attention,
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