基于数据挖掘分析POLR2A基因在脑胶质瘤的表达及意义

目的:基于数据挖掘分析POLR2A基因在低级别脑胶质瘤及正常脑组织中的表达情况,进一步探讨POLR2A基因对低级别脑胶质瘤患者的预后意义。方法:利用Oncomine和GEPIA数据库对POLR2A基因mRNA在正常脑组织和低级别脑胶质瘤组织中的表达进行分析;通过cBioportal分析POLR2A基因在低级别脑胶质瘤组织中的突变情况;利用Onco Lnc数据库对POLR2A基因的表达水平与低级别脑胶质瘤患者生存率做Kaplan-Meier生存分析;使用String-DB数据库探索真核生物表达调控过程中的POLR2A相关蛋白。结果:与正常脑组织相比,低级别脑胶质瘤组织中的POLR2A基因mRNA水平呈显著高表达(P≤0.05);POLR2A基因的表达水平与低级别脑胶质瘤患者的总生存时间无明显相关性;POLR2A基因在低级别脑胶质瘤组织中存在高突变率;真核生物RNA聚合酶POLR2E、POLR2F、POLR2G、POLR2K、POLR2L等与POLR2A有明显的相互作用。结论:数据库中荟萃了POLR2A基因在低级别脑胶质瘤组织中表达的相关信息,证实POLR2A基因在低级别脑胶质瘤组织中呈高表达。     

人脑胶质瘤细胞中GDNF 基因启动子1 区甲基化状态的研究

目的:观察GDNF启动子1区在人脑胶质瘤细胞中的甲基化修饰状态,以期探讨其对于GDNF在胶质瘤中高表达的影响。方法:基因测序检测10例胶质瘤与5例正常脑组织中GDNF基因序列,比较其基因是否有突变发生;重亚硫酸盐修饰后基因测序检测20例胶质瘤(10例低级别和10例高级别)与5例正常脑组织中GDNF启动子1区甲基化修饰状态。结果:GDNF启动子1区基因在胶质瘤中没有发生突变;GDNF启动子1区甲基化修饰在正常脑组织、低级别、高级别中发生率分别为72.25%、86.25%、86.75%。在胶质瘤中的甲基化修饰水平比正常脑组织明显增高(P<0.05),而高低级别之间无显著性差异。结论:在胶质瘤细胞中,GDNF启动子1区发生了高甲基化修饰,这种修饰很可能会影响GDNF基因的表达。     

基于集成类随机森林方法的神经胶质瘤特征基因选择的研究

神经胶质瘤(glioma)是一种严重的颅内肿瘤疾病,具有高复发率、高死亡率和低治愈率等特点。利用基因微阵列数据识别与神经胶质瘤相关的特征基因,对该疾病的临床诊断和生物医学研究将起到有益的参考和借鉴作用。作者针对神经胶质瘤数据,提出了一种集成类随机森林特征基因选择方法。首先应用有监督奇异值分解对数据进行降维并粗选出基因;其次应用类随机森林特征选择方法选出特征基因。实验结果显示,该方法对分类器的适应性强;对比其他方法,分类率优势明显;更重要的是,在选出的前50个特征基因中有39个基因与神经胶质瘤或肿瘤细胞生物过程存在着密切联系,证实该方法不仅保持了较高的分类率,而且保证了选择的特征基因具有很强的生物学关联意义,具有较高的可行性和实用性。     

基于单细胞转录组的多级别胶质瘤异质性及免疫微环境分析揭示了潜在的预后生物标志物

脑胶质瘤(glioma)是中枢神经系统最常见的内在肿瘤,具有发病率高、预后较差等特点。本研究旨在鉴定多形性胶质母细胞瘤(glioblastoma multiforme,GBM)和低级别胶质瘤(lower-grade gliomas, LGG)之间的差异表达基因(differentially expressed genes, DEGs),以探讨不同级别胶质瘤的预后影响因素。从NCBI基因表达综合数据库中收集了胶质瘤的单细胞转录组测序数据,其中包括来自3个数据集的共29 097个细胞样本。对于不同分级的人脑胶质瘤进行分析,经过滤得到21 071个细胞,通过基因本体分析、京都基因与基因组百科全书途径分析,从差异表达基因中筛选出70个基因,我们通过查阅文献,聚焦到delta样典型Notch配体3 (delta like canonical Notch ligand 3,DLL3)这个基因。基于TCGA的基因表达谱交互分析(gene expression profiling interactive analysis, GEPIA)数据库用于探索LGG和GBM中DLL3基因的表达差异,采用基因表达...     

MAML2基因表达及临床参数与低级别胶质瘤(LGG)患者的诊断及预后价值

脑胶质瘤(Glioma)是最常见的中枢系统恶性肿瘤,MAML2是NOTCH信号通路的共激活因子,通过癌基因组数据库(TCGA)分析验证MAML2基因表达及相关临床参数与低级别胶质瘤(LGG)的诊断及预后价值。从癌基因数据库LGG数据库中下载患者基因表达量数据及患者临床数据,采用统计学方法验证MAML2基因表达差异及临床参数与胶质瘤的诊断与预后关系。在TCGA LGG队列中,发现LGG组织中的MAML2基因较正常组织明显上调(P<0.001),其差异表达可作为低级别胶质瘤的潜在诊断标志物。同时,MAML2低表达组的LGG患者总体生存率低于高表达组(P=0.005 2)。此外,单因素多因素分析提示肿瘤分级,初治后肿瘤再发事件及MAML2低表达是低级别胶质瘤患者的独立危险因素。研究结果表明MAML2基因有可能成为诊断及预测低级别胶质瘤的一个潜在分子标记物。     

NSCLC分类及生存分析预测的全基因组特征基因识别

基于NSCLC(非小细胞肺癌)子类分类在临床和生物医学研究方面的意义,利用全基因组基因表达水平(GE)和甲基化(ME)水平的微阵列数据对NSCLC子类分类进行全基因组特征基因识别分析。针对全基因组微阵列数据的高噪声、超高维小样本特性,利用弹性正交贝叶斯算法对全基因组基因进行递归筛选,识别分类精度最优的特征基因集。以TCGA的490的基因表达数据和378个甲基化数据为例,分别识别出52个GE特征基因和25个ME特征基因,相应的分类准确率分别为99%和98%。结合特征基因和临床数据建立的多变量Cox模型明确说明了特征基因在病人生存分析方面的重要作用:仅利用相应的基因表达数据和甲基化数据即可对病人样本的"高/低风险"进行正确分类,显著性水平均低于0.05。特征基因参与的代谢通路与p53、TGF-beta、Wnt等重要的癌症分类和发展的代谢通路的密切关系进一步证实了特征基因对NSCLC分类的重要性。     

基于PCA和神经网络的多形性胶质母细胞瘤驱动基因预测模型

目的:通过对癌症基因表达数据的分析,预测多形性胶质母细胞瘤的驱动基因集。方法:基于主成分分析方法和神经网络,提出一种用于预测多形性胶质母细胞瘤驱动基因的系统生物学模型。首先对实验样本的原始表达谱数据进行预清洗,过滤掉无信息或表达不符合实验要求的表达数据,并对肿瘤表达谱数据进行标准化处理;然后对基因进行划分,相似突变率的基因将被划分到同一块中;最后通过学习神经网络,构建癌症相关基因的调控网络,得出驱动基因的预测集。结果:本研究应用上述模型,对多形性胶质母细胞瘤(glioblastoma multiforme,GBM)驱动基因进行预测。已发表的大量实验结果表明,我们预测出的大部分驱动基因在GBM中起重要作用。结论:我们提出一种对GBM表达谱数据分析的新方法,能够高精度地预测出该疾病的驱动基因,该模型同样能够较好地用于分析其它疾病的表达谱数据。     

基于CFS-mRMR特征筛选方法和Adaboost算法的胶质瘤 相关基因筛选及预测模型的建立

目的:找出胶质瘤病变发生机制相关的基因群,并在此基础上建立预测胶质瘤病变发生的预测模型。方法:收集GEO中胶质瘤芯片数据,使用关联特征选择(Correlation-based Feature Subset, CFS)和最小冗余最大相关性(Minimum Redundancy MaximumRelevance, mRMR)特征选择方法筛选出差异基因,分析这些差异基因的功能,然后使用Adaboost算法建立胶质瘤的预测模型,并对模型的预测能力进行评估。结果:通过特征筛选,得到了19个和胶质瘤病变相关的的基因;以该19个基因建组成特征子集,结合AdaBoost算法建立了胶质瘤的预测模型,经验证,模型的预报准确率可以达到95.59%。通过对19个差异基因的GO和KEGG分析,发现这些基因和肿瘤的发生发展有一定作用。结论:CFS-mRMR特征筛选方法可以有效地发现与胶质瘤疾病有关的基因,所筛选的19个差异基因具有生物学意义,且以此构建的胶质瘤预测模型,可以有效地对预测胶质瘤的发生。     

融合基因关系网和功能预测恶性胶质瘤基因方法研究

目的:建立挖掘恶性胶质瘤候选基因的方法并进行系统分析。方法:结合恶性胶质瘤已知通路内基因和发生点突变和拷贝数改变的基因构建扩展基因关系网络,计算并分别寻找在网络中度和中心性得分高,脆弱性为正数的节点(基因),将满足一种或多种测度并与已知恶性胶质瘤基因共功能的基因作为恶性胶质瘤候选基因。最后,通过文献验证方法评价多种测度预测恶性胶质瘤基因的效能。结果:融合基因功能后,利用基因在网络中的度和脆弱性可识别大部分恶性胶质瘤基因,但利用中心性预测的结果较差;当将三个测度融合后,效能并没比单独使用脆弱性高。结论:融合基因功能关系和网络脆弱性是预测恶性胶质瘤基因的最佳测度。     

编者按

<正>中国科学技术协会生命科学学会联合体于2019年1月2日公布了2018年度"中国生命科学十大进展"评选结果,首都医科大学江涛团队、香港科技大学王吉光团队和北京师范大学樊小龙团队合作完成的"多维基因组学大数据指导下的继发胶质母细胞瘤精准治疗"入选.他们首次证实了MET基因系列变异是驱动低级别脑胶质瘤恶性进展为高级别的关键机制,首次在基因变异全景图的广度提出继发性胶质母细胞瘤克隆进化模型,并完成可通过     

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.     

VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.     

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.     

An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes

Background

Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”.

Results

Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.

Conclusion

The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.     

Finding Driver Pathways in Cancer: Models and Algorithms

ABSTRACT: BACKGROUND: Cancer sequencing projects are now measuring somatic mutations in large numbers of cancer genomes. A key challenge in interpreting these data is to distinguish driver mutations, mutations important for cancer development, from passenger mutations that have accumulated in somatic cells but without functional consequences. A common approach to identify genes harboring driver mutations is a single gene test that identifies individual genes that are recurrently mutated in a significant number of cancer genomes. However, the power of this test is reduced by: (1) the necessity of estimating the background mutation rate (BMR) for each gene; (2) the mutational heterogeneity in most cancers meaning that groups of genes (e.g. pathways), rather than single genes, are the primary target of mutations. RESULTS: We investigate the problem of discovering driver pathways, groups of genes containing driver mutations, directly from cancer mutation data and without prior knowledge of pathways or other interactions between genes. We introduce two generative models of somatic mutations in cancer and study the algorithmic complexity of discovering driver pathways in both models. We show that a single gene test for driver genes is highly sensitive to the estimate of the BMR. In contrast, we show that an algorithmic approach that maximizes a straightforward measure of the mutational properties of a driver pathway successfully discovers these groups of genes without an estimate of the BMR. Moreover, this approach is also successful in the case when the observed frequencies of passenger and driver mutations are indistinguishable, a situation where single gene tests fail. CONCLUSIONS: Accurate estimation of the BMR is a challenging task. Thus, methods that do not require an estimate of the BMR, such as the ones we provide here, can give increased power for the discovery of driver genes.     

Identification of Constrained Cancer Driver Genes Based on Mutation Timing

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver–passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression.     

Finding co-mutated genes and candidate cancer genes in cancer genomes by stratified false discovery rate control

Finding candidate cancer genes playing causal roles in carcinogenesis is an important task in cancer research. The non-randomness of the co-mutation of genes in cancer samples can provide statistical evidence for these genes' involvement in carcinogenesis. It can also provide important information on the functional cooperation of gene mutations in cancer. However, due to the relatively small sample sizes used in current high-throughput somatic mutation screening studies and the extraordinary large-scale hypothesis tests, the statistical power of finding co-mutated gene pairs based on high-throughput somatic mutation data of cancer genomes is very low. Thus, we proposed a stratified FDR (False Discovery Rate) control approach, for identifying significantly co-mutated gene pairs according to the mutation frequency of genes. We then compared the identified co-mutated gene pairs separately by pre-selecting genes with higher mutation frequencies and by the stratified FDR control approach. Finally, we searched for pairs of pathways annotated with significantly more between-pathway co-mutated gene pairs to evaluate the functional roles of the identified co-mutated gene pairs. Based on two datasets of somatic mutations in cancer genomes, we demonstrated that, at a given FDR level, the power of finding co-mutated gene pairs could be increased by pre-selecting genes with higher mutation frequencies. However, many true co-mutation between genes with lower mutation rates will still be missed. By the stratified FDR control approach, many more co-mutated gene pairs could be found. Finally, the identified pathway pairs significantly overrepresented with between-pathway co-mutated gene pairs suggested that their co-dysregulations may play causal roles in carcinogenesis. The stratified FDR control strategy is efficient in identifying co-mutated gene pairs and the genes in the identified co-mutated gene pairs can be considered as candidate cancer genes because their non-random co-mutations in cancer genomes are highly unlikely to be attributable to chance.     

Mathematical modeling of multiple pathways in colorectal carcinogenesis using dynamical systems with Kronecker structure

Like many other types of cancer, colorectal cancer (CRC) develops through multiple pathways of carcinogenesis. This is also true for colorectal carcinogenesis in Lynch syndrome (LS), the most common inherited CRC syndrome. However, a comprehensive understanding of the distribution of these pathways of carcinogenesis, which allows for tailored clinical treatment and even prevention, is still lacking. We suggest a linear dynamical system modeling the evolution of different pathways of colorectal carcinogenesis based on the involved driver mutations. The model consists of different components accounting for independent and dependent mutational processes. We define the driver gene mutation graphs and combine them using the Cartesian graph product. This leads to matrix components built by the Kronecker sum and product of the adjacency matrices of the gene mutation graphs enabling a thorough mathematical analysis and medical interpretation. Using the Kronecker structure, we developed a mathematical model which we applied exemplarily to the three pathways of colorectal carcinogenesis in LS. Beside a pathogenic germline variant in one of the DNA mismatch repair (MMR) genes, driver mutations in APC, CTNNB1, KRAS and TP53 are considered. We exemplarily incorporate mutational dependencies, such as increased point mutation rates after MMR deficiency, and based on recent experimental data, biallelic somatic CTNNB1 mutations as common drivers of LS-associated CRCs. With the model and parameter choice, we obtained simulation results that are in concordance with clinical observations. These include the evolution of MMR-deficient crypts as early precursors in LS carcinogenesis and the influence of variants in MMR genes thereon. The proportions of MMR-deficient and MMR-proficient APC-inactivated crypts as first measure for the distribution among the pathways in LS-associated colorectal carcinogenesis are compatible with clinical observations. The approach provides a modular framework for modeling multiple pathways of carcinogenesis yielding promising results in concordance with clinical observations in LS CRCs.     

基于多目标评价方法的乳腺癌易感基因排序及其网络表达研究

目的利用已有的研究结果和数据,采用多目标评价方法建立乳腺癌易感基因评价模型,对与已知乳腺癌基因关系密切的其它基因进行分析和排序,并给出结果的网络表达模式。方法通过分析已有的文献,并利用有关的基因数据库和已有文献中的数据,提炼出乳腺癌易感基因的多目标评价体系,构建基于加权和法的乳腺癌易感基因评价模型,并利用Cytoscape软件进行评价结果计算和评价结果的网络模式表达。结果利用多目标模型所得到的评价结果,与已有的研究结果一致。其中,乳腺癌易感基因TopBP1排名第二,已知乳腺癌候选易感基因HMMR排名第六。结论文章提出的多目标评价模型能够准确评价被选基因与乳腺癌易感性之间的关系,所提出的评价方法与相关软件结合使用,将成为癌症易感基因研究方面有效的分析方法和途径。     

基因突变在结直肠癌诊疗及预后中的研究进展

结直肠癌是常见的恶性肿瘤之一,其发病率居全球恶性肿瘤发病率的第三位,死亡率呈逐年上升趋势。中国已成为全球结直肠癌每年新发病例数和死亡病例数最多的国家。对结直肠癌基因突变状态的识别以及对结直肠癌发生发展过程进行精确分类,可实现对患者进行个性化精准治疗的目的,而精准治疗的实现有赖于基因测序技术。目前,二代测序技术(Next generation sequencing,NGS)结合基因捕获技术,集中对研究者感兴趣的候选基因或外显子进行平行测序,极大拓展了对肿瘤特征基因的认识,为发展新的治疗手段和治疗策略奠定了基础。整合癌症基因组数据库IntOgen已明确72个结直肠癌驱动突变基因,包括“TP53”、“KRAS”、“PIK3CA”等;癌基因数据库Cancer Gene Census目前收录的结直肠癌突变基因有59个,包括原癌基因“BRAF”、抑癌基因“SMAD4”等;在线人类孟德尔遗传OMIM数据库已收录55个与结直肠癌相关的体细胞突变基因,包括“SRC”、“APC”等。本文通过26篇国内外文献,对结直肠癌基因突变检测的共识基因进行综述,并总结了与结直肠癌患者临床诊断、分型、预后、治疗等临床病理特征相关的突变基因标志物。