臺灣博碩士論文加值系統

利用轉錄體學技術，對肝癌檢體進行全域性基因表現的特徵分析，可以找出與臨床病理特徵相關的基因表現模式，而這項技術已廣泛地應用於臨床診斷及預後症狀的預測上。近來科學家對微小核糖核酸 (microRNA) 表現的特徵分析，已成為一個強而有力的工具來找出在癌細胞中，重要的調控因子。僅管如此，如何經由生物晶片分析的結果，了解基因表現在腫瘤生長調控上的生物意義，仍是一個重大的挑戰。在本研究中，我利用12-O-Tetradecanoylphorbol-13-acetate (TPA)處理HepG2細胞，使細胞會停留在細胞週期中的G1時期。在藥物測試環璄下，差異表現基因和microRNAs (miRs) 也許和肝癌的生長調控有關，而mRNAs和miRs的差異表現，可以分別反映出在轉錄及轉譯上的調控。為了找出特定地被轉錄因子及miRs調控的基因，我們利用系統生物學方法，計算涵蓋轉錄因子、miRs及其目標基因的最短路徑的調控網路。我們認為在癌化過程中，基因的轉錄及轉譯在特定調控網路上會被適度地同時調控。我們的整合性方法能過加強找尋更安全、有效的肝癌治療標靶，並提供一個新的分析方法，找出傳統單獨分析mRNA和miR晶片所忽略的重要因子。

Using transcriptome data to profile the global gene expression has been widely applied to identify genes whose expression pattern is associated with clinical pathological features of tumors which may be used for the diagnosis and prognostic predictions of hepatocellular carcinoma (HCC). Recently, researchers have focused on the expression profiling of microRNAs (miRNAs) to provide more comprehensive results. A significant challenge in the post-genomic era is how to use large-scale and multiple dimensions data to extract and understand the underlying biology of hepatocarcinogenesis. Previous studies indicate that 12-O-Tetradecanoylphorbol-13-acetate (TPA) induce G1 growth arrest in HepG2 cells. Deregulation of growth control mechanism may play a key role in the cancer development. We hypothesize that the genes and miRNAs whose expression altered by TPA treatment may be related to growth control of human hepatoma cells. We proposed that expression level of critical regulatory components in particular networks may be moderately modulated at both transcriptional and translational level during tumorgenesis. In this study, we used a systems biology approach to identify regulatory networks by integrating mRNAs and miRNAs expressions and provide a new dimension of target identification which may be ignored by conventional microarray analysis for mRNAs and miRNAs alone.

TABLE OF CONTENT
中文摘要 IV
ABSTRACT VI
CHAPTER 1. INTRODUCTION - 1 -
CHAPTER 2. MATERIALS AND METHODS - 3 -
2.1 CELL LINES AND PRE-MIRNA TRANSIENT TRANSFECTION - 3 -
2.2. RNA PREPARATION - 3 -
2.3. REVERSE TRANSCRIPTION (RT) - 3 -
2.4. QUANTITATIVE RT-PCR (QRT-PCR) - 4 -
2.5. DATA PRE-PROCESSING - 4 -
2.6. GENE ONTOLOGY SEMANTIC SIMILARITY CLUSTERING ENRICHMENT (GOSIMENRICHMENT) - 5 -
2.7. REGULATORY NETWORK MODELING AND TOPOLOGICAL ANALYSIS - 6 -
CHAPTER 3. RESULTS - 8 -
3.1. IDENTIFICATION OF DIFFERENTIALLY EXPRESSED MRNAS AND MICRORNAS IN HUMAN HCC - 8 -
3.2. FUNCTIONAL ANNOTATION OF SIGNIFICANTLY ENRICHED MIRNA::CLUSTERS - 9 -
3.3. CONSTRUCTION AND VALIDATION OF REGULATORY INTERACTIONS IN THE MICRORNAS-MEDIATED REGULATORY NETWORK - 11 -
CHAPTER 4. DISCUSSION - 13 -
CHAPTER 5. REFERENCES - 16 -


TABLE OF FIGURES
FIGURE 1. AN OVERVIEW OF IN SILICO INTEGRATIVE FRAMEWORK. IN THIS PARALLEL ANALYTIC FRAMEWORK, WE INPUT A LIST OF PUTATIVE MIR-TARGET PAIRS FOR 9 MIRNAS THAT ARE INVERSELY EXPRESSED IN TPA-TREATED HEPG2 CELLS AND HCC SAMPLES. IN ADDITION, WE IMPORTED DIFFERENTIALLY EXPRESSED GENES IN HCC SAMPLES AND CALCULATED THEIR SIMILARITY OF GO TERMS. AFTER FUNCTIONAL CLUSTERING, WE USED TWO-TAILED FISHER’S EXACT TEST TO CALCULATE P-VALUE OF MIRNAS TO EACH CLUSTER AND THEN UPLOADED MIR-ENRICHED CLUSTER TO GENEGO® METACORE™ FOR NETWORK CONSTRUCTION. THEREFORE, WE CAN DEFINE SIGNIFICANT MIR-MEDIATED FUNCTIONAL NETWORKS. - 24 -
FIGURE 2. THE PATTERN OF DIFFERENTIALLY EXPRESSED (DE) MRNAS AND MICRORNAS DISTINGUISH HCC TISSUE SAMPLES AND ADJACENT NORMAL LIVER TISSUE. (A) UNSUPERVISED HIERARCHICAL CLUSTERING OF DE 3845 MRNA PROBES IN 3 PAIRS OF HCC (BLUE) AND ADJACENT NORMAL LIVER (RED) TISSUES. PEARSON’S DISSIMILARITY WAS USED FOR DISTANCE MEASURE AND WARD’S METHOD FOR LINKAGE ANALYSIS IN THE HIERARCHICAL CLUSTERING. (B) UNSUPERVISED HIERARCHICAL CLUSTERING OF 34 DE MIRNAS IN 20 PAIRS OF HCC (BLUE) AND ADJACENT NORMAL LIVER (RED) TISSUES. PEARSON’S DISSIMILARITY WAS USED FOR DISTANCE MEASURE AND WARD’S METHOD FOR LINKAGE ANALYSIS IN THE HIERARCHICAL CLUSTERING. MICRORNA LEVELS WERE EXPRESSED AS 39 – CT AFTER GLOBAL MEDIAN NORMALIZATION. (C) DIFFERENTIALLY EXPRESSION OF MIRNAS IN TPA-TREATED HEPG2 CELLS. THE SCATTER PLOT WAS PERFORMED FOR THE MIRNA LEVELS (39 – CT) OF TPA-TREATED HEPG2 CELLS AND UNTREATED CONTROL. THE RED DOTTED LINES INDICATE THE BOUNDARY OF ONE DCT VALUE. (D) DISTRIBUTION OF THE NUMBER OF DIFFERENT MIRNAS BINDS TO THE UP-REGULATED GENES IN HCC SAMPLES. BLANK BAR INDICATES 9 INVERSELY EXPRESSED MIRNAS BETWEEN TPA-TREATED HEPG2 CELLS AND HCC SAMPLES; THE OTHER INDICATES TESTING DATA-SETS. - 26 -
FIGURE 3. DIFFERENTIALLY EXPRESSED GENES SEPARATED INTO 6 FUNCTIONAL CLUSTERS. (A) DAVIES-BOULDIN (DB) VALUE WAS CALCULATED TO DETERMINE THE OPTIMAL CLUSTER NUMBER. THE BEST CLUSTER NUMBER VALUE (THE LOWEST ONE) WAS TWO. HOWEVER, WE TRIED TO SEPARATE THESE GENES INTO MORE THAN 2 CLUSTERS WITH A LOW DB VALUE. THEREFORE, WE SELECTED THE NEXT LOWEST NUMBER, 6, AS OUR OPTIMAL GROUP NUMBER. (B) PCA ANALYSIS OF THE 6 FUNCTIONAL CLUSTERS. - 28 -
FIGURE 4. THE DISTRIBUTION OF MIR-ENRICHED SCORE IN EACH CLUSTER. THE AREA FILLED WITH WHITE SHOW THE DISTRIBUTION OF ENRICHED SCORE OF INVERSELY EXPRESSED MIRNAS; THE OTHER SHOWS THE NON-SPECIFIC MIRNAS. THE SCORE WERE CALCULATED BY –LOG (P-VALUE) AND THE TICKS ON AXIS INDICATE THAT THE P-VALUE EQUAL TO 0.05. - 30 -
FIGURE 5. THE REGULATORY INTERACTIONS IN THE 3-LAYERED SHORTEST PATH OF MIR-101 WERE EXPERIMENTALLY VALIDATED. (A) THE 3-LAYERED SHORTEST PATH OF MIR-101 WAS VISUALIZED USING CYTOSCAPE. THE HIGH SCORING NODES WERE COLORED BY YELLOW. THE RED AND GREEN LINE INDICATES THE NEGATIVE AND POSITIVE REGULATORY INTERACTION, RESPECTIVELY. (B) PREDICTED MIRNA::MRNA RECOGNITION SITES. (C). QUANTIFICATION OF MIR-101 AND ITS 1ST , 2ND OR 3RD NEIGHBORS IN HEPG2 CELLS AFTER TRANSIENTLY TRANSFECTED WITH EITHER NEGATIVE CONTROL OR PRE-MIR-101. EXPRESSION LEVELS OF MIRNAS WERE EXPRESSED AS 39 – CT. MIR-16 AND MIR-93 WERE INTERNAL CONTROL MIRNAS. THE RESULTS ARE MEAN±S.D. OF TWO INDEPENDENT EXPERIMENTS. (*** P-VALUE < 0.001; ** P-VALUE < 0.01; * P-VALUE < 0.05) - 32 -



TABLE OF TABLES
TABLE 1. 23 DOWN-REGULATED MIRNAS AND 11 UP-REGULATED MIRNAS IN HCC SAMPLES. - 33 -
TABLE 2. THE INVERSELY EXPRESSED MICRORNAS BETWEEN TPA-TREATED HEPG2 CELLS AND HCC SAMPLES. - 35 -
TABLE 3. THE ENRICHED MIRNAS IN CLUSTER III AND VI. - 36 -
TABLE 4. THE MAIN FUNCTION OF CLUSTER III AND VI - 37 -
TABLE 5. THE LIST OF RELEVANT NODES WHOSE SCORE WERE LARGER THAN 3-QUANTILES OF ALL NODES. - 39 -
TABLE 6. EXPRESSION LEVELS OF 1ST, 2ND OR 3RD NEIGHBORS OF MIR-101 IN HCC SAMPLES. - 40 -

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