CAJ | 학술논문

目的通过胃癌差异基因表达谱提供的信息鉴定用于识别胃癌分化程度的分子标志物.方法从本实验室前期所建立的胃癌Oligo基因芯片数据库中选取15份胃癌全基因组表达谱芯片数据,包括9份低分化胃癌和6份高分化胃癌标本数据,采用生物信息学分析方法BAGEL和k-TSP筛选配对的分类器,用于识别肿瘤的分化程度.随后采用ROC曲线对所筛选的特征分子标志物的分类敏感度和特异度进行判断,鉴定具有最强分类能力的分类器.选取北京肿瘤医院30份胃癌组织标本,包括22份低分化胃癌和8份高分化胃癌标本,采用实时荧光定量PCR对所鉴定的分类器进行验证.结果利用胃癌分化差异基因表达谱数据,采用BAGEL分析方法筛选出了121个表达变化大于2倍的差异表达基因(FC＞2.0,P＜0.001),并在此基础上进一步采用k-TSP分析方法获得了3组用于区分胃癌高低分化程度的胃癌特征基因,包括MYLIP和TMPRSS3,ZNF266和TM4SF1以及SNAI2和CNFN.ROC曲线结果显示,SNAI2和CNFN组合基因对胃癌标本的分化程度进行判断具有最高的分类敏感度(100%)和特异度(100%),其AUC达到1,其他两组分类器则分别为0.981和0.963.实时荧光定量PCR结果显示,在22份低分化胃癌标本中,18份标本(82%)的SNAI2的表达水平高于CNFN;在8份高分化胃癌标本中,6份标本(75%)的SNAI2的表达水平低于CNFN.结论 SNAI2和CNFN在不同分化程度胃癌中具有特定的表达模式,并且两者的表达水平呈现负相关趋势,提示SNAI2和CNFN组合可能作为判断胃癌分化程度的分子标志物.
목적 통과위암차이기인표체보제공적신식감정용우식별위암분화정도적분자표지물.방법 종본실험실전기소건립적위암Oligo기인심편수거고중선취15빈위암전기인조표체보심편수거,포괄9빈저분화위암화6빈고분화위암표본수거,채용생물신식학분석방법BAGEL화k-TSP사선배대적분류기,용우식별종류적분화정도.수후채용ROC곡선대소사선적특정분자표지물적분류민감도화특이도진행판단,감정구유최강분류능력적분류기.선취북경종류의원30빈위암조직표본,포괄22빈저분화위암화8빈고분화위암표본,채용실시형광정량PCR대소감정적분류기진행험증.결과 이용위암분화차이기인표체보수거,채용BAGEL분석방법사선출료121개표체변화대우2배적차이표체기인(FC＞2.0,P＜0.001),병재차기출상진일보채용k-TSP분석방법획득료3조용우구분위암고저분화정도적위암특정기인,포괄MYLIP화TMPRSS3,ZNF266화TM4SF1이급SNAI2화CNFN.ROC곡선결과현시,SNAI2화CNFN조합기인대위암표본적분화정도진행판단구유최고적분류민감도(100%)화특이도(100%),기AUC체도1,기타량조분류기칙분별위0.981화0.963.실시형광정량PCR결과현시,재22빈저분화위암표본중,18빈표본(82%)적SNAI2적표체수평고우CNFN;재8빈고분화위암표본중,6빈표본(75%)적SNAI2적표체수평저우CNFN.결론 SNAI2화CNFN재불동분화정도위암중구유특정적표체모식,병차량자적표체수평정현부상관추세,제시SNAI2화CNFN조합가능작위판단위암분화정도적분자표지물.
Objective To identify biomarkers associated with the differentiated phenotype based on gene expression profiling of gastric cancer. Methods Two bioinformatic methods, BAGEL and k-TSP, were used to identify featured genes associated with differentiation in gastric cancer samples based on the Oligo gene chip data, and ROC curves were used to verify the classification sensitivity and specificity of the identified genes. Finally, a total of 30 gastric cancer samples with different differentiation levels were collected for laboratory validation using real-time PCR analyses. Results A total of 121 differentially expressed genes were identified using the BAGEL algorithm, the criterion were FC ＞ 2. 0 and P ＜ 0. 001.Then, the k-TSP algorithm for feature selection based on this differential expression data were used, and 3 groups of featured genes which had potential to classify poor and well differentiation gastric cancer samples were identified, including MYLIP and TMPRSS3, ZNF266 and TM4SF1, SNAI2 and CNFN. To define the featured gene groups that had the highest classification capability, ROC curves to calculate the classification sensitivity and specificity of each gene group were used. The results showed that the combination of SNAI2and CNFN as a classifier had the highest classification sensitivity and specificity. Real-time PCR results showed that 18 of 22 poor differentiation samples were found with high expression of SNAI2 and low expression of CNFN (82%); 6 of 8 well differentiation samples were of low expression of SNAI2 and high expression of CNFN (75%). Conclusion The results indicate that SNAI2 and CNFN are constantly expressed in poor or well differentiation gastric cancer samples, and the expression pattern of these two genes is opposite. These results indicate that SNAI2 and CNFN have the potential for the identification of the differentiation level of gastric cancer.