肺癌晚期患者不同基因突变、融合或扩增的相关性
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摘要
目的基于数据挖掘技术,对肺癌晚期患者不同基因突变、融合或扩增相关性进行探讨。方法收集我院630份病例,利用Excel 2016建立药物数据库。采用频数分析与关联规则进行统计分析,统计分析在SPSS 22.0和SPSS Moder统计软件上进行。结果高频率基因有26位,EGFR-19突变、ALK融合、RET突变、EGFR-L858R-21突变、ALK突变、T790M突变位列前6;前26位中NRAS-G12D突变与BRAF-G466V突变、MYC-R450W突变与CYP2D6突变、GATA3M423fs突变与ESR1突变、SRC突变与GATA3M423fs突变、SRC突变与ESR1突变分类值之间相关联。基因关联分析最小值50%,支持度10%,经Apriori模块分析,EGFR-19突变与14基因未见(置信度46.667%)、EGFR-19突变与T790M突变(置信度40.000%)、EGFR-19突变与TP53突变(置信度13.333%)、ROS1融合与MET扩增(置信度47.619%)、MET扩增与ROS1融合(置信度50.000%)、T790M突变与EGFR扩增(置信度57.895%)、EGFR-19突变与EGFR扩增(置信度42.105%)。聚类分析BRAF-G466V与NRAS-G12D,MYC-R450W与CYP2D6,GATA3M423fs与SRC,PIK3CA扩增与PIK3CA,Pten与EGFE-L861Q-21,KRAS G12A与KRAS G12D,MET扩增与ROS1融合,EGFR扩增与BRAF-G466V,ERBB2 S310F与BRAFG464V。结论肺癌晚期不同基因之间以及基因的突变、融合、缺少与扩增等具有关联性。
Objective Based on data mining technology, we discussed the correlation of different gene mutation, fusion or amplification in advanced lung cancer patients. Methods 630 cases in our hospital were collected and the drug database was established by Excel 2016. Frequency analysis and association rules were used for statistical analysis, and statistical analysis was performed on SPSS22.0 and SPSSModerler statistical software.Results There were 26 high frequency genes, such as EGFR-19 mutation, ALK fusion, RET mutation, EGFRL858 R-21 mutation, ALK mutation, and the first 6 cases of T790M mutation. The first 26 were NRAS-G12 D mutation and BRAF-G466 V mutation, MYC-R450 W mutation and CYP2D6 mutation, GATA3 M423 fs mutation and ESR1 mutation, abrupt mutation, mutation and sudden process. The variable classification values are associated with each other. The minimum value of gene association analysis was 50% and support was 10%. After Apriori module analysis, EGFR-19 mutation and 14 genes were not found(confidence degree 46.667%), EGFR-19 mutation and T790 M mutation(confidence degree 40%), EGFR-19 mutation and TP53 mutation(confidence degree 13.333%), ROS1 fusion and MET amplification(confidence 47.619%), MET amplification and ROS1 melting(confidence level 50%), T790 M mutation and EGFR amplification(confidence level 57.895%), EGFR-19 mutation and EGFR amplification(confidence 42.105%). Cluster analysis BRAF-G466 V and NRAS-G12D,MYC-R450 W and CYP2 D6, GATA3 M423 fs and SRC, PIK3 CA amplification and PIK3CA, Pten and EGFEL861 Q-21, KRAS G12A and blending. Conclusion There are correlations between different genes and mutation,fusion, lack and amplification of lung cancer.
Objective Based on data mining technology, we discussed the correlation of different gene mutation, fusion or amplification in advanced lung cancer patients. Methods 630 cases in our hospital were collected and the drug database was established by Excel 2016. Frequency analysis and association rules were used for statistical analysis, and statistical analysis was performed on SPSS22.0 and SPSSModerler statistical software.Results There were 26 high frequency genes, such as EGFR-19 mutation, ALK fusion, RET mutation, EGFRL858 R-21 mutation, ALK mutation, and the first 6 cases of T790M mutation. The first 26 were NRAS-G12 D mutation and BRAF-G466 V mutation, MYC-R450 W mutation and CYP2D6 mutation, GATA3 M423 fs mutation and ESR1 mutation, abrupt mutation, mutation and sudden process. The variable classification values are associated with each other. The minimum value of gene association analysis was 50% and support was 10%. After Apriori module analysis, EGFR-19 mutation and 14 genes were not found(confidence degree 46.667%), EGFR-19 mutation and T790 M mutation(confidence degree 40%), EGFR-19 mutation and TP53 mutation(confidence degree 13.333%), ROS1 fusion and MET amplification(confidence 47.619%), MET amplification and ROS1 melting(confidence level 50%), T790 M mutation and EGFR amplification(confidence level 57.895%), EGFR-19 mutation and EGFR amplification(confidence 42.105%). Cluster analysis BRAF-G466 V and NRAS-G12D,MYC-R450 W and CYP2 D6, GATA3 M423 fs and SRC, PIK3 CA amplification and PIK3CA, Pten and EGFEL861 Q-21, KRAS G12A and blending. Conclusion There are correlations between different genes and mutation,fusion, lack and amplification of lung cancer.
引文
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[14]周启明,赵艳,陈倩琪,等.BRAF和NRAS基因表达与黑色素瘤细胞增殖的相关性分析[J].海南医学院学报,2013,19(8):1023-1026,1030.
[15]朱卫东,石晨曦,李三恩,等.晚期非小细胞肺癌中c-MET、ALK、ROS1基因突变的相关性及临床意义[J].临床与实验病理学杂志,2017,33(9):997-1000.
[2]RECK M,RABE K F.Precision diagnosis and treatment for advanced non-small-cell lung cancer[J].N Engl J Med,2017,377(9):849-861.
[3]靳智勇.非小细胞肺癌基因多态性在患者疗效预测及预后判断中的研究[D].北京市结核病胸部肿瘤研究所,2014:6.
[4]李继华,何俊,唐锐,等.宣威肺癌分子流行病学研究:煤种、基因型与肺癌风险[J].中国肺癌杂志,2015,18(1):16-22.
[5]刘平,吴羽华,周立娟,等.不同基因分型晚期非小细胞肺癌患者的预后分析[J].中国肺癌杂志,2017,20(11):741-750.
[6]赵冠华.非小细胞肺癌患者与健康人群血清及尿液特异性差异多肽及肺鳞癌患者血清多肽与化疗疗效相关性的探索性研究[D].中国人民解放军军事医学科学院,2017:8.
[7]SHAW A T,FORCIONE D G,DIGUMARTHY S R,et al.Case records of the massachusetts general hospital.case 21-2011.A31-year-old man with ALK-positive adenocarcinoma of the lung[J].N Engl J Med,2011,365(2):158-167.
[8]张静,樊旼.非小细胞肺癌BRAF突变及靶向治疗的研究进展[J].临床肿瘤学杂志,2015,20(3):271-275.
[9]杨露璐.非小细胞肺癌新的治疗性靶点的探索[D].南方医科大学,2014:4.
[10]赵钊,周永列.杭州地区89例肺腺癌患者EGFR、KRAS与BRAF突变状态分析[J].中国卫生检验杂志,2016,26(22):3201-3203.
[11]孔令琦.周围型肺腺癌和鳞癌CT、病理表现与c-erbB-2蛋白表达的相关性研究[D].青岛大学,2001:2.
[12]于芹,姜达,李颖.T790M突变的非小细胞肺癌研究现状与前景[J].中国肺癌杂志,2017,20(3):199-204.
[13]杜亚茜,马煜辉,杨长绍,等.非小细胞肺癌患者外周血ctD-NA样本中EGFR-T790M基因突变分析[J].实用医学杂志,2017,33(19):3228-3232.
[14]周启明,赵艳,陈倩琪,等.BRAF和NRAS基因表达与黑色素瘤细胞增殖的相关性分析[J].海南医学院学报,2013,19(8):1023-1026,1030.
[15]朱卫东,石晨曦,李三恩,等.晚期非小细胞肺癌中c-MET、ALK、ROS1基因突变的相关性及临床意义[J].临床与实验病理学杂志,2017,33(9):997-1000.