膀胱癌分子分型和分子网络的系统生物学研究
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摘要
在手术前对非浸润性和浸润性膀胱尿路上皮癌进行准确的区分对于制定正确的治疗方案至关重要；然而,目前临床尚缺乏有效的鉴别诊断方法。本研究利用微阵列比较基因组杂交(array comparative genome hybridization, array CGH)技术研究膀胱癌的基因组DNA拷贝数变化,旨在获得膀胱癌特征性DNA拷贝数变异谱型,通过构建分子模型来对非浸润性和浸润性膀胱癌进行分子分型。在获得膀胱癌的基因组DNA拷贝数变异谱型的基础上,对同一组膀胱癌组织样品进行mRNA表达谱和miRNA表达谱的并行分析,从这三组分析数据中挖掘三种分子层面的相关性改变,构建分子网络并初步探讨其在膀胱癌发生发展中的意义。
本课题第一部分工作是应用Agilent公司的4x44K array CGH芯片分析了45例膀胱癌组织基因组DNA。结果显示,染色体变化主要发生在1p、1q、2q、4q、5q、6q、8p、8q、9p、9q、10q、11p、11q、13q、14q、17p、20q等染色体臂。将这批数据与本实验室前期工作中的膀胱癌1x44K array CGH芯片数据整合,运用R Project分析软件,从共计65例膀胱癌(非浸润性膀胱癌29例,浸润性膀胱癌36例)的array CGH数据中鉴定出一组在膀胱癌基因组中有较高频率拷贝数变异的染色体区域,并且得到29个在Ta-T3期膀胱癌组间有显著差异的DNA拷贝数变异片段(p<0.05)。采用回归分割决策树的方法,构建了一个由5个DNA片段(分别定位于染色体lp36.13、lq32.1、5q11.2、9q21.31-q21.33和9q33.2)组成的树状模型,该模型对非浸润性膀胱癌和浸润性膀胱癌的分型准确率达90.8%。随后用leave one out算法对上述29个差异片段进行交叉验证。用real-timePCR的方法分别在array CGH分析所用起始膀胱癌样本(57例)和独立样本组织(49例)中验证了分子分型模型中包含的9个基因(A_l4_P 103301、FAM131C CH1T1 CDC20B、RASEF、RMI1、DAPK1、TRAF1和C5)的拷贝数变异情况,从而明确了array CGH结果的可靠性,以及分型模型的可行性。
本课题第二部分工作应用Agilent公司mRNA表达谱芯片和niRNA表达谱芯片分别对35例和32例膀胱癌组织进行了分析。结果显示,非浸润性和浸润性膀胱癌的mRNA表达谱之间存在772个差异表达基因,GO注释发现这些差异表达基因均与细胞外基质有关；非浸润性和浸润性膀胱癌的miRNA表达谱之间存在146个差异表达的miRNA,对差异最显著的5个miRNA进行靶基因预测,并对这些靶基因进行GO分析,发现靶基因主要与代谢相关。本研究中有30例膀胱癌样品并行完成了array CGH芯片、mRNA表达谱和miRNA表达谱芯片分析。通过WGCNA (Weighted correlation network analysis)算法鉴定出11个与肿瘤浸润程度相关的“基因模块’'(module),基于GSEA (Gene Set Enrichment Analysis)方法得到信息以及eQTL (expression Quantitative Trait Locus)分析方法研究膀胱癌基因组DNA拷贝数变异与转录组改变之间的关系,针对每个module构建整合有DNA、mRNA、miRNA三个层面信息的分子网络,并初步探讨其内在的生物学意义。
本研究结果表明,非浸润性和浸润性膀胱癌基因组DNA拷贝数的改变确实存在差异,据此通过生物信息学方法构建的分子模型能够比较准确地区分两型膀胱癌,并且在独立样本中得到了证实。而运用系统生物学方法构建的与膀胱癌浸润程度相关的分子网络,则为深入研究膀胱癌的发生发展机制提供了大量信息和宝贵的线索。
The biological characteristics of the two subtypes of bladder urothelial carcinoma, non-invasive superficial tumor and invasive tumor, are closely associated with the clinical performance, the therapeutical plan. However there is a lack of effective approaches to the preoperative discrimination for the two subtypes of bladder cancer. In this study, array comparative genome hybridization (array CGH) analysis was performed first, to obtain the genome DNA copy number alteration profiles that specifically related to the non-invasive or invasive bladder cancer, respectively. From these, a set of gain or loss genes/regions could be identified to generate a mathematical model for molecular classification of the two subtypes of tumor.
The tumor tissue samples derived from 45 patients of bladder cancer were analyzed by array CGH on the Agilent human genome 4×44K microarrays. The genome-wide patterns of copy number alteration were obtained after analyzing by the Agilent software CGH Analytics 4.0. In euchromosomes, the alterations were mainly occurred in 1p, 1q, 2q,4q,5q,6q,8p,8q,9p,9q, 10q,11p, 11q,13q，14q, 17p and 20q. Combining the data with that of 20 bladder cancer samples previously analyzed by Agilent human genome 1×44K microarrays, a genomic profile was identified for the 65 cases of bladder cancer (29 non-invasive and 36 invasive). Besides,29 significantly variable fragments among the stage Ta, T1, T2 and T3 tumors were detected. Using them, a recursive partitioning decision tree model was constructed with 5 fragments located in 1p36.13, 1q32.1,5q11.2,9q21.31-q21.33 and 9q33.2, respectively. The accuracy of the model was 90.8%(59/65) for discriminating non-invasive and invasive bladder cancer. The reliability of the tree model was demonstrated by cross validation with the 'leave-one-out' algorithm. The 9 genes involved in the tree model (A_14_P103301, FAM131C, CHIT1, CDC20B, RASEF, RMI1, DAPK1, TRAF1 and C5) were then validated by real time polymerase chain reaction (RT-PCR) in both the tumor tissue samples used for the array CGH analysis (n=57) and that derived from an independent group of bladder cancers (n=49). There was a considerable correlation between the results of the array CGH and real time PCR. profile and a microRNA (miRNA) expression profile were obtained from 35 and 32 tissue samples of bladder cancer, respectively, using the Agilent microarrays. There were 772 differentially expressed genes between non-invasive and invasive bladder cancer; and all these genes were associated with extracellular matrix by Gene Ontology (GO) searching. There were 146 differentially expressed miRNAs between the two types of bladder cancer. The target genes of the 5 most significantly different miRNAs were predicted, and subsequently analyzed by the GO.
In this study,30 of the 65 cases were parallelly screened by array CGH. mRNA microarrays and miRNA microarrays; and the data mining was performed with bioinformatic approaches. Grounded on the mRNA expression profile,11 gene-modules correlated with tumor invasion were identified, using weighted correlation network analysis (WGCNA). Based on the data derived from GSEA (gene set enrichment analysis) and eQTL (expression Quantitative Trait Locus), the networks for each module were built up. which integrated the information from the molecular levels of DNA, mRNA and miRNA, and the biological significance of the molecular networks was discussed.
In conclusion, data obtained from the present study indicated that there were differences in the genomic profiles between the non-invasive and invasive bladder cancers. The two types of bladder urothelial carcinomas could be classified with the decision tree model with 5 fragments, which was certified in an independent sample set. This integrative study on molecular classification and molecular networks provided considerable information for better understanding bladder urothelial carcinoma.
本课题第一部分工作是应用Agilent公司的4x44K array CGH芯片分析了45例膀胱癌组织基因组DNA。结果显示,染色体变化主要发生在1p、1q、2q、4q、5q、6q、8p、8q、9p、9q、10q、11p、11q、13q、14q、17p、20q等染色体臂。将这批数据与本实验室前期工作中的膀胱癌1x44K array CGH芯片数据整合,运用R Project分析软件,从共计65例膀胱癌(非浸润性膀胱癌29例,浸润性膀胱癌36例)的array CGH数据中鉴定出一组在膀胱癌基因组中有较高频率拷贝数变异的染色体区域,并且得到29个在Ta-T3期膀胱癌组间有显著差异的DNA拷贝数变异片段(p<0.05)。采用回归分割决策树的方法,构建了一个由5个DNA片段(分别定位于染色体lp36.13、lq32.1、5q11.2、9q21.31-q21.33和9q33.2)组成的树状模型,该模型对非浸润性膀胱癌和浸润性膀胱癌的分型准确率达90.8%。随后用leave one out算法对上述29个差异片段进行交叉验证。用real-timePCR的方法分别在array CGH分析所用起始膀胱癌样本(57例)和独立样本组织(49例)中验证了分子分型模型中包含的9个基因(A_l4_P 103301、FAM131C CH1T1 CDC20B、RASEF、RMI1、DAPK1、TRAF1和C5)的拷贝数变异情况,从而明确了array CGH结果的可靠性,以及分型模型的可行性。
本课题第二部分工作应用Agilent公司mRNA表达谱芯片和niRNA表达谱芯片分别对35例和32例膀胱癌组织进行了分析。结果显示,非浸润性和浸润性膀胱癌的mRNA表达谱之间存在772个差异表达基因,GO注释发现这些差异表达基因均与细胞外基质有关；非浸润性和浸润性膀胱癌的miRNA表达谱之间存在146个差异表达的miRNA,对差异最显著的5个miRNA进行靶基因预测,并对这些靶基因进行GO分析,发现靶基因主要与代谢相关。本研究中有30例膀胱癌样品并行完成了array CGH芯片、mRNA表达谱和miRNA表达谱芯片分析。通过WGCNA (Weighted correlation network analysis)算法鉴定出11个与肿瘤浸润程度相关的“基因模块’'(module),基于GSEA (Gene Set Enrichment Analysis)方法得到信息以及eQTL (expression Quantitative Trait Locus)分析方法研究膀胱癌基因组DNA拷贝数变异与转录组改变之间的关系,针对每个module构建整合有DNA、mRNA、miRNA三个层面信息的分子网络,并初步探讨其内在的生物学意义。
本研究结果表明,非浸润性和浸润性膀胱癌基因组DNA拷贝数的改变确实存在差异,据此通过生物信息学方法构建的分子模型能够比较准确地区分两型膀胱癌,并且在独立样本中得到了证实。而运用系统生物学方法构建的与膀胱癌浸润程度相关的分子网络,则为深入研究膀胱癌的发生发展机制提供了大量信息和宝贵的线索。
The biological characteristics of the two subtypes of bladder urothelial carcinoma, non-invasive superficial tumor and invasive tumor, are closely associated with the clinical performance, the therapeutical plan. However there is a lack of effective approaches to the preoperative discrimination for the two subtypes of bladder cancer. In this study, array comparative genome hybridization (array CGH) analysis was performed first, to obtain the genome DNA copy number alteration profiles that specifically related to the non-invasive or invasive bladder cancer, respectively. From these, a set of gain or loss genes/regions could be identified to generate a mathematical model for molecular classification of the two subtypes of tumor.
The tumor tissue samples derived from 45 patients of bladder cancer were analyzed by array CGH on the Agilent human genome 4×44K microarrays. The genome-wide patterns of copy number alteration were obtained after analyzing by the Agilent software CGH Analytics 4.0. In euchromosomes, the alterations were mainly occurred in 1p, 1q, 2q,4q,5q,6q,8p,8q,9p,9q, 10q,11p, 11q,13q，14q, 17p and 20q. Combining the data with that of 20 bladder cancer samples previously analyzed by Agilent human genome 1×44K microarrays, a genomic profile was identified for the 65 cases of bladder cancer (29 non-invasive and 36 invasive). Besides,29 significantly variable fragments among the stage Ta, T1, T2 and T3 tumors were detected. Using them, a recursive partitioning decision tree model was constructed with 5 fragments located in 1p36.13, 1q32.1,5q11.2,9q21.31-q21.33 and 9q33.2, respectively. The accuracy of the model was 90.8%(59/65) for discriminating non-invasive and invasive bladder cancer. The reliability of the tree model was demonstrated by cross validation with the 'leave-one-out' algorithm. The 9 genes involved in the tree model (A_14_P103301, FAM131C, CHIT1, CDC20B, RASEF, RMI1, DAPK1, TRAF1 and C5) were then validated by real time polymerase chain reaction (RT-PCR) in both the tumor tissue samples used for the array CGH analysis (n=57) and that derived from an independent group of bladder cancers (n=49). There was a considerable correlation between the results of the array CGH and real time PCR. profile and a microRNA (miRNA) expression profile were obtained from 35 and 32 tissue samples of bladder cancer, respectively, using the Agilent microarrays. There were 772 differentially expressed genes between non-invasive and invasive bladder cancer; and all these genes were associated with extracellular matrix by Gene Ontology (GO) searching. There were 146 differentially expressed miRNAs between the two types of bladder cancer. The target genes of the 5 most significantly different miRNAs were predicted, and subsequently analyzed by the GO.
In this study,30 of the 65 cases were parallelly screened by array CGH. mRNA microarrays and miRNA microarrays; and the data mining was performed with bioinformatic approaches. Grounded on the mRNA expression profile,11 gene-modules correlated with tumor invasion were identified, using weighted correlation network analysis (WGCNA). Based on the data derived from GSEA (gene set enrichment analysis) and eQTL (expression Quantitative Trait Locus), the networks for each module were built up. which integrated the information from the molecular levels of DNA, mRNA and miRNA, and the biological significance of the molecular networks was discussed.
In conclusion, data obtained from the present study indicated that there were differences in the genomic profiles between the non-invasive and invasive bladder cancers. The two types of bladder urothelial carcinomas could be classified with the decision tree model with 5 fragments, which was certified in an independent sample set. This integrative study on molecular classification and molecular networks provided considerable information for better understanding bladder urothelial carcinoma.
引文
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