||Genome-wide Meta-analysis of Cancer Biomarkers and Genetic Variations in Cellular Differentiation
||Institute of Computer Science and Information Engineering
gene regulatory network
微陣列與次世代定序技術進行大規模基因組分子活動探索已成為生物資訊中越來越重要之研究議題。同時，大量有價值的網路信息也已被存放於數個公開的資料庫中。伴隨著這些不同型態的資料產生，如何針對不同有興趣的研究主題正確地整合與分析不同型態的資料（整合性分析）是非常重要的。在本論文中，我們將探討關於生物資訊整合性分析之數個重要研究議題，如以下所示：I) 人類疾病常是受到一組相關的基因變異所造成，而不是單一各自無相關的基因變異。先前大部分基於微陣列的整合分析確認疾病之獨立性相關基因或生物標誌。我們提出第一個能夠考慮基因組合效應之整合分析方法，在不同微陣列平台進行有效辨別關聯生物標誌 (ABs)。II) 預後標誌物的鑑定有助於防止或控制癌症轉移之進程，整合微陣列與生物網路也已被用於找這樣的標誌，但往往此發現沒有在另一個獨立的樣本群中驗證。我們設計了一個新的整合性分析框架，可有效找出癌症預後標誌物，並應用於食道鱗狀癌發展相關基因調控網路，及找出與食道癌預後能力有關之DNA 上CpG甲基化差異位點。III) 常見的人類遺傳變異的庫存已接近完成，但其對生物的影響並不是很清楚。從不同的組織和細胞註解基因與調節元件，提供功能性說明來預測遺傳變異之後果。其中的一些後果可能僅在特定的生理條件之下被揭示，如壓力反應或細胞分化。我們因此提出了一個有效的整合性分析流程以結合各種已註解資訊，並應用於找出與心臟分化有關之遺傳變異。
關於第二個研究主題，我們初步篩選44個最顯著之甲基化位點，這些位點不僅與病人存活率有關而且還落於食道癌進展調控網路所包含之基因的啟動子上。在另一個獨立食道癌病人群中，我們證實了食道癌網路所包含之8/10 CpG位點與患者的存活率有顯著相關。相較之下，在食道癌網路外的基因之啟動子，無 (0/10) CpG位點與患者的存活率有關。我們推論我們的分析框架能夠在部分基因體之DNA甲基化研究中改善真正預後標誌物之找尋。
關於第三個研究主題，我們所提出的整合性分析流程除了包含由體外心臟分化過程中活化組織蛋白H3K4me3修飾之啟動子資訊。為了增加其生理性，我們限制了最強之啟動子標記於人類胎兒心臟之DNA酶 (DNase I) 高敏感性位置。我們的結果說明了遺傳變異可發生在心臟分化過程中之特定DNA調控元件。
Large-scale exploring of genome-wide molecular activities by using microarray or next-generation sequencing (NGS) techniques has become a very important topic in bioinformatics. Meanwhile, a lot of valuable network information has also been deposited in several publicly available databases. With these diverse data, it is very important to properly integrate and analyze, i.e., to do meta-analysis, for different research topics of interest. In this dissertation, we will discuss about several research topics of bioinformatics meta-analysis as follows: I) Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. We present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms. II) The identification of prognostic biomarkers would help prevent or control cancer metastatic progression. The integration of microarray and network analyses has also been used to find such markers, but do not always validate in separate cohorts. We present a new meta-analysis framework, which is able to find prognostic cancer biomarkers by applying cancer progression networks. We designed the framework with esophageal squamous cell carcinoma (ESCC) progression-associated gene regulatory network (GRNescc), to identify differentially DNA methylated CpG sites prognostic of ESCC progression. III) The inventory of common human genetic variation is nearly complete but its impact on biology is not always clearly understood. Annotations of genes and, more recently regulatory elements through tissue and cell types, provide the functional context to predict the consequences of genetic variation. Some of these consequences may only be revealed under certain physiological conditions such as stress response or cellular differentiation. We therefore propose a useful meta-analysis pipeline involving several annotated information and then apply the pipeline on cardiac differentiation to identify relevant genetic variations.
Regarding the first research topic, we propose a new meta-analysis approach called MiningABs to mine ABs across different array-based datasets. The method is able to efficiently identify ABs from different independently performed array-based datasets, and we show its validity in cancer biology via GO enrichment, literature survey and network analyses. We postulate that the ABs may facilitate novel target and drug discovery, leading to improved clinical treatment.
Regarding the second research topic, we selected 44 CpG loci most highly associated with survival and located in the promoters of genes more likely to belong to GRNescc. Using an independent ESCC cohort, we confirmed that 8/10 of CpG loci in the promoter of GRNescc genes significantly correlated with patient survival. In contrast, 0/10 CpG loci in the promoter genes outside the GRNescc were correlated with patient survival. We postulate that our analysis framework improves the identification of bona fide prognostic biomarkers from DNA methylation studies especially with partial genome coverage.
Regarding the third research topic, the proposed pipeline involved gene promoters, identified through histone H3K4me3 modifications, activated during in vitro cardiac differentiation. We increased the physiological relevance by restricting to analysis to human fetal heart DNase I hypersensitive sites (DHSs), which overlapped with the strongest promoter marks. Our final report illustrates the prevalence of genetic variation in the regulatory elements that are remodeled during cardiac differentiation.
摘 要 I
LIST OF FIGURES XI
LIST OF TABLES XII
LIST OF SUPPLEMENTAL INFORMATION XIII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 OVERVIEW OF DISSERTATION 4
1.2.1 Mining associated biomarkers across multi-connected gene expression datasets 4
1.2.2 Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression 5
1.2.3 Identification of genetic variations in dynamic regulatory elements of cardiac differentiation 6
1.3 ORGANIZATION OF DISSERTATION 7
CHAPTER 2 BACKGROUND AND RELATED WORK 8
2.1 META-ANALYSIS OF MICROARRAY DATASETS 8
2.2 META-ANALYSIS OF MICROARRAY DATA AND BIOLOGICAL NETWORKS 10
2.3 META-ANALYSIS OF NEXT GENERATION SEQUENCING-BASED DATA AND THE ANNOTATION OF GENOMIC COORDINATES 10
CHAPTER 3 MINING ASSOCIATED BIOMARKERS ACROSS MULTI-CONNECTED GENE EXPRESSION DATASETS 12
3.1 INTRODUCTION 12
3.2 PROPOSED METHOD 13
3.2.1 Overview of datasets 13
3.2.2 Integrating sk-LMs to classify cancer samples 15
126.96.36.199 Development of individual logit model from single dataset 15
188.8.131.52 Probe sequence similarity matrix development as a bridge to connect datasets 16
184.108.40.206 Identification of c-LM from multi-connected-datasets 19
3.2.3 Improving c-LM via a heuristic selection process 21
3.2.4 Evaluating improved c-LM using a reciprocal test 23
3.3 RESULTS AND DISCUSSION 24
3.3.1 Discovering improved c-LMs using a genetic algorithm 24
3.3.2 Considering more datasets yields better accuracy compared to increasing the gene number 25
3.3.3 Improved c-LMs reveal ABs 26
3.3.4 ABs are highly related to cancer development and connected in network 28
3.4 SUMMARY 33
CHAPTER 4 NETWORK-BASED ANALYSIS IDENTIFIES EPIGENETIC BIOMARKERS OF ESOPHAGEAL SQUAMOUS CELL CARCINOMA PROGRESSION 35
4.1 INTRODUCTION 35
4.2 PROPOSED METHOD 36
4.2.1 Patients and biopsy specimens 36
4.2.2 Construction of an ESCC-related gene regulatory network 37
4.2.3 Generation and analysis of DNA methylation microarray data 38
220.127.116.11 Microarray data generation 38
18.104.22.168 Microarray data analysis 39
22.214.171.124 Identification of CpG sites associated with progression 39
126.96.36.199 Identification of CpG sites associated with survival 40
4.2.4 Network analysis 40
4.2.5 Pyrosequencing validation and survival analysis 41
4.2.6 Quantitative RT-PCR 41
4.3 RESULTS AND DISCUSSION 42
4.3.1 Overall framework and ESCC network construction 42
4.3.2 Identification of candidate CpG sites associated with ESCC progression 44
4.3.3 Network based selection of candidate CpG sites associated with survival 45
4.3.4 Validation of the findings 47
4.3.5 GRNescc network topology 51
4.4 SUMMARY 52
CHAPTER 5 IDENTIFICATION OF GENETIC VARIATIONS IN DYNAMIC REGULATORY ELEMENTS OF CARDIAC DIFFERENTIATION 54
5.1 INTRODUCTION 54
5.2 PROPOSED METHODS 56
5.2.1 Identifying dynamic DHSs in cardiac development 56
5.2.2 Clustering dynamic DHSs 57
5.2.3 Transcription factor motif enrichment analysis of clustered dynamic DHSs 58
5.2.4 Enrichment of cardiac development-related genes 59
5.2.5 Enrichment of cardiac disease-related SNPs in dynamic DHSs 59
5.2.6 Identification of temporal SNPs with allele-specific binding and eQTLs 60
5.2.7 Identification of SNP-containing regulatory motifs 61
5.3 RESULTS AND DISCUSSION 61
5.3.1 Overlapping fetal heart DHSs and a temporal H3K4me3 signature 61
5.3.2 Inducible dynamic DHSs corresponding to well-known genes in cardiac development 64
5.3.3 Inducible dynamic DHSs corresponding to transcription factor binding motifs 65
5.3.4 Well-known heart SNPs are enriched in inducible dynamic DHSs 68
5.3.5 Identification of allele specific events at dynamic fetal heart DHSs 68
5.4 SUMMARY 70
CHAPTER 6 CONCLUSION AND FUTURE WORK 71
SUPPLEMENTAL INFORMATION 89
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