复杂疾病的核心基因筛选及分子机制研究
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摘要
肿瘤、代谢疾病、心脑血管疾病等绝大多数复杂性疾病的的流行和发展不仅给人类带来了漫长的痛苦和折磨，也给世界各国带来了沉重的负担，广泛且深入地研究复杂疾病的发病机理成为现下最迫切的医学难题。然而复杂疾病的诊断与研究与传统的单基因遗传疾病不同，需要综合考虑疾病发生的环境因素和遗传因素，从而为其诊断、药物设计、个性化治疗以及预防提供坚实的基础和突破性思路。
转录组数据对于揭示疾病相关基因及其分子机制有着重要的作用，发现了许多在癌症发生发展过程中起到重要作用的靶点，为后续的基因表达调控、代谢通路和信号转导等研究提供了有意义的启示。然而对如此庞大数据进行信息挖掘并不容易，我们在现有方法的基础上创建了一套完整的分析流程，注重各基因表达之间的共同合作关系，很好地弥补了传统方法筛选核心基因时的不足。对于每一个基因给予一个分值用以评判它在整个生物学过程的各个阶段下和全过程中是否稳定地与其他基因共表达调控，并以此参数筛选参与生物学功能的核心基因。在分析前列腺癌发生发展的分子机制中，运用这套分析流程筛选核心基因，随机检测证实全部与人类胚胎癌干细胞、增生性疾病、慢性炎症和DNA损伤等肿瘤发生机制相关，证实了该分析流程的有效和可靠，也为进一步的深入研究提供了分析基础和数据积累。在对三种人工多能干细胞全能性的机制研究中，针对全部miRNA表达情况，创新性地将miRNA靶基因和印记基因信息整合在一起，为细胞全能性研究提供思路。在抗糖尿病药物的分子机制分析中，主要关注Rosi、Pio和Met抗糖尿病药物在肝脏、心脏和脂肪组织等五种不同组织细胞中的表达差异，并且结合转录因子和蛋白质相互作用数据库，阐述了三种药物之间存在的相同或不同的作用机制，以及不同脂肪组织在功能与起源分化上的差异，为深入研究糖尿病的发生和抗糖尿病药物的研制提供思路。
虽然我们的工作还不能完全揭示这些复杂疾病相关的基因群关系，但对整个基因调控网络图而言是很好的补充和完善。进一步的研究需要我们利用更多的数据和手段整合分析才能逐渐理清多基因通过蛋白质复合物、调控网络以及互作的通路网络调控。我们相信随着时间的推移和相关知识的积累，总有一天能够将这张调控网络完全呈现在人们面前。
Prevalence and development of complex diseases like cancer, metabolic diseases, heart andcardiovascular diseases, not only bring human beings a long period of pain and suffering, but alsobe a heavy burden to the world. Thus, it becomes a most pressing medical problem to study thepathogenesis of complex diseases more extensively and deeply. However, the diagnosis ofcomplex diseases are different from those of traditional single-gene genetic diseases, asenvironmental factors and genetic factors are needed to be considered to provide solid foundationand breakthrough ideas for diagnosis, drug design, personalized treatment and prevention.
Transcriptome data plays an important role in revealing disease-related genes and molecularmechanism. In recent years, some researches based on time series expression data reveal muchcrucial targets in the process of cancer development, which provide meaningful insights in furtherstudy on regulation of gene expression, metabolic pathways and signal transduction. However, it’sdifficult to analyze these hidden changes of gene expression with commonly used statisticalmethods, so we created a complete set of expression profiling data analysis process used to screenout those core regulatory genes closely related with the disease to describe its specific molecularmechanisms in detail on the basis of existing algorithms. Conservative and dynamic cooperativeproperties were firstly introduced to mine gene expression profiling data for finding functionalgene signatures. TiCoGE is just a strategy to find those gene clusters with strong cooperativedynamic conservative properties across cancer progression stages in dealing with gene expressionprofiling data, and each gene will be ranked with a conservative score to evaluate its importance incancer progression and development. Our methodology was presented in an effort to breakthrough the limitations of pure statistical models and looks reasonable sensitive for further studyof gene function and regulation of networks.
We chose a set of time series gene expression data on prostate cancer, including the fivestages for analyzing the co-expression of genes in the process of tumor development. TiCoGEmethod eventually screened out492high score genes, and several genes were randomly selectedand all annotated to well known oncogenesis mechanisms or disease models, such as humanembryonal cancer stem cells, proliferative disorders, human immunodeficiency, chronicinflammation, out of control of remodeling balance, DNA-damaging, etc. These results not onlyconfirmed the reliability and validity of TiCoGE method, but also explore the relationship ofmultiple genes in cancer development from a global perspective. It is valuable to elucidate thelisted core genes for further research as an analytic basis and data accumulation.
Our work also included analyzing whole expressed miRNA of three artificial pluripotent cells,by an innovative use of focusing on the region of differentially expressed miRNAs, the adjacentmiRNA target genes and imprinted genes. Besides, another part of our work is about the molecularmechanism of anti-diabetic drugs, including rosiglitazone, pioglitazone and metformin. in fivedifferent tissue cells, that is liver, heart and3adipose tissues. We explained the potential same ordifferent mechanism of drugs and search the diverse function and differentiation between threeadipose tissues, which may provide new idea for anti-diabetic drug design.
Thus, to some extent, our evolutionary conservation analyzing based methodologycompensates for the inherent weaknesses of current statistical methods and provides a new wayfor gene expression profile analysis. However, complex disease is caused by multiple genesthrough protein complexes, regulatory networks, as well as the interaction path network control,we need to use multiple types of data and tools for integration analysis in order to clarify theassociation between the combination of multiple genes and complex diseases gradually.
转录组数据对于揭示疾病相关基因及其分子机制有着重要的作用，发现了许多在癌症发生发展过程中起到重要作用的靶点，为后续的基因表达调控、代谢通路和信号转导等研究提供了有意义的启示。然而对如此庞大数据进行信息挖掘并不容易，我们在现有方法的基础上创建了一套完整的分析流程，注重各基因表达之间的共同合作关系，很好地弥补了传统方法筛选核心基因时的不足。对于每一个基因给予一个分值用以评判它在整个生物学过程的各个阶段下和全过程中是否稳定地与其他基因共表达调控，并以此参数筛选参与生物学功能的核心基因。在分析前列腺癌发生发展的分子机制中，运用这套分析流程筛选核心基因，随机检测证实全部与人类胚胎癌干细胞、增生性疾病、慢性炎症和DNA损伤等肿瘤发生机制相关，证实了该分析流程的有效和可靠，也为进一步的深入研究提供了分析基础和数据积累。在对三种人工多能干细胞全能性的机制研究中，针对全部miRNA表达情况，创新性地将miRNA靶基因和印记基因信息整合在一起，为细胞全能性研究提供思路。在抗糖尿病药物的分子机制分析中，主要关注Rosi、Pio和Met抗糖尿病药物在肝脏、心脏和脂肪组织等五种不同组织细胞中的表达差异，并且结合转录因子和蛋白质相互作用数据库，阐述了三种药物之间存在的相同或不同的作用机制，以及不同脂肪组织在功能与起源分化上的差异，为深入研究糖尿病的发生和抗糖尿病药物的研制提供思路。
虽然我们的工作还不能完全揭示这些复杂疾病相关的基因群关系，但对整个基因调控网络图而言是很好的补充和完善。进一步的研究需要我们利用更多的数据和手段整合分析才能逐渐理清多基因通过蛋白质复合物、调控网络以及互作的通路网络调控。我们相信随着时间的推移和相关知识的积累，总有一天能够将这张调控网络完全呈现在人们面前。
Prevalence and development of complex diseases like cancer, metabolic diseases, heart andcardiovascular diseases, not only bring human beings a long period of pain and suffering, but alsobe a heavy burden to the world. Thus, it becomes a most pressing medical problem to study thepathogenesis of complex diseases more extensively and deeply. However, the diagnosis ofcomplex diseases are different from those of traditional single-gene genetic diseases, asenvironmental factors and genetic factors are needed to be considered to provide solid foundationand breakthrough ideas for diagnosis, drug design, personalized treatment and prevention.
Transcriptome data plays an important role in revealing disease-related genes and molecularmechanism. In recent years, some researches based on time series expression data reveal muchcrucial targets in the process of cancer development, which provide meaningful insights in furtherstudy on regulation of gene expression, metabolic pathways and signal transduction. However, it’sdifficult to analyze these hidden changes of gene expression with commonly used statisticalmethods, so we created a complete set of expression profiling data analysis process used to screenout those core regulatory genes closely related with the disease to describe its specific molecularmechanisms in detail on the basis of existing algorithms. Conservative and dynamic cooperativeproperties were firstly introduced to mine gene expression profiling data for finding functionalgene signatures. TiCoGE is just a strategy to find those gene clusters with strong cooperativedynamic conservative properties across cancer progression stages in dealing with gene expressionprofiling data, and each gene will be ranked with a conservative score to evaluate its importance incancer progression and development. Our methodology was presented in an effort to breakthrough the limitations of pure statistical models and looks reasonable sensitive for further studyof gene function and regulation of networks.
We chose a set of time series gene expression data on prostate cancer, including the fivestages for analyzing the co-expression of genes in the process of tumor development. TiCoGEmethod eventually screened out492high score genes, and several genes were randomly selectedand all annotated to well known oncogenesis mechanisms or disease models, such as humanembryonal cancer stem cells, proliferative disorders, human immunodeficiency, chronicinflammation, out of control of remodeling balance, DNA-damaging, etc. These results not onlyconfirmed the reliability and validity of TiCoGE method, but also explore the relationship ofmultiple genes in cancer development from a global perspective. It is valuable to elucidate thelisted core genes for further research as an analytic basis and data accumulation.
Our work also included analyzing whole expressed miRNA of three artificial pluripotent cells,by an innovative use of focusing on the region of differentially expressed miRNAs, the adjacentmiRNA target genes and imprinted genes. Besides, another part of our work is about the molecularmechanism of anti-diabetic drugs, including rosiglitazone, pioglitazone and metformin. in fivedifferent tissue cells, that is liver, heart and3adipose tissues. We explained the potential same ordifferent mechanism of drugs and search the diverse function and differentiation between threeadipose tissues, which may provide new idea for anti-diabetic drug design.
Thus, to some extent, our evolutionary conservation analyzing based methodologycompensates for the inherent weaknesses of current statistical methods and provides a new wayfor gene expression profile analysis. However, complex disease is caused by multiple genesthrough protein complexes, regulatory networks, as well as the interaction path network control,we need to use multiple types of data and tools for integration analysis in order to clarify theassociation between the combination of multiple genes and complex diseases gradually.
引文
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