臺灣博碩士論文加值系統

肝癌在全球是最常見的惡性腫瘤之一，尤其在非洲和亞洲有更高的發生率。肝癌致病的機轉是由許多因子交互作用累積的，例如環境、病毒感染、內分泌失調及代謝等問題而形成各種病理變化。目前最常用的預測性診斷的分子為胎兒球蛋白(alpha-fetoprotein)，可以利用血清中的胎兒球蛋白濃度當作肝癌早期發現的指標，其準確率大約只有50%。因此我們希望利用生物資訊學的方式藉由現有的肝癌微陣列資料庫，進行分子標幟的探勘，找出初步的候選人基因，再使用分子生物學方法確認我們的結果及設計更進一步的研究。本研究的分子標的探勘方式分為兩部分，首先尋找肝癌之分子標幟；其次為探勘腫瘤抑制基因。在第一部份我們利用Okabe的肝癌微陣列資料庫和CGH中得到與肝癌相關的染色體變異我們由資料庫的篩選，分別得到23個基因可能可以作為肝癌標幟及23個基因與腫瘤抑制相關基因；第二部分利用Stanford Microarray Database（SMD）的肝癌微陣列資料庫進行候選基因或遺傳標記的探勘，初步結果表現85個基因可能可以作為肝癌分子指標，106個基因與抑致肝癌的生長有關。我們綜合第一部分和第二部分的結果，進一步縮小範圍至14個基因與肝癌分子指標及七個抑癌基因，針對肝癌細胞株與組織進行RNA定量的表現。本研究共計使用了10個肝癌的細胞株及21位肝癌病人，成對的腫瘤及正常的組織進行以上21個候選基因的定量表現，結果顯示有六個(PRO2000, PYGB, STMN1, AFM, C8FW, NNMT)基因表現符合我們資料庫探勘的結果。希望利用生物資訊學資料探勘法配合傳統分子生物學的方式以其確認肝癌分子標誌並進一步探討致病機轉。

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world, especially in Asia and Africa countries. The distribution pattern shows geographical variation and pathogesis in multifactors as environment, infection, nutrition, metabolism, and endocrine contribute to hepatocarcinogenesis. Alpha fetal protein (AFP), the major tumor marker used at present accounts only 50% HCC diagnostic efficiency. This study aims to identify potential tumor markers or suppressor genes for further application in early HCC diagnoses and treatments. Therefore we utilized available cDNA microarray databases in conjunction with other bioinformatic resources to identify our candidate genes related to HCCs. cDNA microarray technology and bioinformatic resources which enable investigators to obtain comprehensive data with respect to gene-expression profiles, is progressing rapidly. The Okabe’s and Stanford’s HCC database were our major data-mining material. A total of 85 potential tumor markers and 106 potential tumor suppressors were found via preliminary in silico datamining. We furthermore narrowed down to 14 candidate tumor markers and 7 candidate tumor suppressor genes by the way of quantitative RT-PCR technologies were applied in various HCC cancer cell lines and 21 patient’s in pair, tumor/non-tumor tissues to confirm gene expression profile. The results revealed that 6 genes (PRO2000, PYGB, STMN1, AFM, C8FW, NNMT) conformed to our data-mining studies.

CHAPTER 1 A REVIEW OF HEPATOCELLULAR CARCINOMA (HCC) 1
1.1 PATHOLOGY OF HCC 1
1.2 PATHOGENESIS OF HCC 1
1.2.1 Chronic hepatitis and liver cirrhosis 2
1.2.2 Carcinogens and HCC 3
1.2.3 Metabolic disorder 3
1.2.4 Genetic alternations and imbalances 4
1.2.5 Chromosomal abnormalities and genetic instability in HCC 4
1.2.6 Oncogene and tumor suppressor gene 6
1.3 PROGNOSTIC MARKER IN HCCS 9
1.3.1 Tumor markers 10
1.3.2 Other molecular markers related to HCCs 11
1.4 LARGE- SCALE ANALYSIS OF GENE EXPRESSION IN HCCS 13
TABLE 1. REVIEWS OF CHROMOSOME ABNORMALITIES IN HCCS 15
TABLE 2. LARGE-SCALE ANALYSIS IN HUMAN HCC BASED ON HIGH-THROUGHPUT TECHNOLOGIES 16
CHAPTER 2 DATA-MINING FOR HCC-RELATED GENES/ESTS FROM EXPRESSION DATABASES 17
2.1 INTRODUCTION 17
2.2 MATERIALS, METHODS AND RESULTS 17
2.2.1 Materials 17
2.2.2 Methods and Results 18
2.2.2.1 Method one: search for up- or down-regulated genes related to HCCs against the Okabe’s HCC microarray database and Digital Differential Display (DDD) 19
2.2.2.2 Method two: search for HCC-related genes/ESTS against the SMD cDNA microarray database 20
2.2.2.2.1 Quest for HCC potential tumor markers 20
2.2.2.2.1.1 Approach A: search for genes/ESTs with expression profiles similar to the expression profile of ideal tumor marker 20
2.2.2.2.1.2 Approach B: search for genes/ESTs with similar expression profiles to that of AFP and also highly differentially expressed between HCCs and normal liver samples (p<0.01) in the SMD HCC microarray database 22
2.2.2.2.2 Quest for HCC potential tumor suppressors 22
2.2.2.2.2.1 Approach C: search for genes/ESTs whose expression profiles are opposite to that of AFP expression profile in the SMD HCC cDNA microarray database 23
2.2.2.2.2.2 Approach D: search for genes/ESTs whose expression profiles were opposite to that of AFP in Gene Expression Atlas, GNF and in comparison with the SMD HCC cDNA microarray database 23
2.2.2.2.2.3 Approach E: search for genes/ESTs whose expression profiles were opposite to that of an ideal tumor marker in the SMD HCC cDNA microarray database 24
2.2.2.3 Comparison on the identified genes/ESTs from various data-mining technologies 25
2.2.3 Annotation of identified candidate genes/ESTs 27
2.2.3.1 Search through the GeneCardsä 27
2.3 DISCUSSIONS 27
TABLE 3. TWENTY-THREE UP-REGULATED GENES FROM DATA-MINING FOR HCC CANDIDATE GENES FROM THE OKABE’S CDNA MICROARRAY DATABASE (METHOD ONE) 31
TABLE 4. TWENTY-THREE DOWN-REGULATED GENES FROM DATA-MINING FOR HCC CANDIDATE GENES FROM OKABE’S CDNA MICROARRAY DATABASE (METHOD ONE). 32
TABLE 5. EIGHTY-FIVE CANDIDATE TUMOR MARKERS IN HCC FROM DATA-MINING ANALYSIS OF SMD (METHOD TWO) 33
TABLE 6. PRELIMINARY 106 TUMOR SUPPRESSOR GENES IN HCCS FROM MICROARRAY DATA-MINING IN SMD (METHOD TWO) 36
TABLE 7. CHROMOSOMAL DISTRIBUTIONS OF 83 CANDIDATE TUMOR MARKERS IDENTIFIED FROM THE SMD HCC CDNA MICROARRAY DATABASE (METHOD TWO). 40
TABLE 8. CHROMOSOMAL DISTRIBUTIONS OF 105 CANDIDATE TUMOR SUPPRESSOR GENES IDENTIFIED FROM SMD HCC CDNA MICROARRAY DATABASE (METHOD TWO) 41
TABLE 9. FOURTEEN GENES SIMILAR TO THE AFP EXPRESSION PATTERN IDENTIFIED IN APPROACH B OF METHOD TWO FROM THE SMD HCC CDNA MICROARRAY DATABASE 42
TABLE 10. IDENTIFICATION OF 21 GENES IN METHOD ONE AND TWO 43
TABLE 11. ANNOTATION OF TWENTY-ONE CANDIDATE GENES ANNOTATION 44
TABLE 12. DATABASE AND INTERNET SERVES USED IN OUR STUDY 47
FIGURE 1. THE PROCESS OF DATA-MINING OF HCC RELATED GENES AGAINST THE OKABE’S HCC CDNA DATABASE AND DDD (METHOD ONE).. 48
FIGURE 2. SEARCH FOR HCC-RELATED GENES/ESTS AGAINST THE SMD HCC CDNA MICROARRAY DATABASE (METHOD TWO). 49
FIGURE 3. FLOW CHARTS OF SEARCH FOR HCC-RELATED GENES/ESTS AGAINST THE SMD HCC CDNA MICROARRAY DATABASE (METHOD TWO). 50
FIGURE 4. EXPRESSION PROFILE OF ALPHA-FETOPROTEIN (AFP) DISPLAYED . 51
FIGURE 5. SEARCH FOR POTENTIAL TUMOR SUPPRESSOR GENES/ESTS IN THE SMD HCC CDNA MICROARRAY DATABASE (METHOD TWO). 52
FIGURE 6. FLOW CHARTS OF SEARCH FOR POTENTIAL TUMOR SUPPRESSOR GENES/ESTS IN THE SMD HCC CDNA MICROARRAY DATABASE (METHOD TWO). 53
FIGURE 7. CHROMOSOMAL DISTRIBUTIONS OF 83 UP-REGULATED GENES/ESTS AND 105 DOWN-REGULATED GENES/ESTS IDENTIFIED FROM VARIOUS DATA-MINING TECHNOLOGIES 54
CHAPTER 3 VALIDATION OF CANDIDATE GENES’ EXPRESSION PROFILES BY QUANTITATIVE RT-PCR AND WESTERN BLOTTING 55
3.1 INTRODUCTION 55
3.2 MATERIALS AND METHODS 55
3.3 RESULTS 60
3.4 DISCUSSIONS 62
TABLE 13. TWENTY-ONE PRIMER PAIRS USED FOR QUANTITATIVE RT-PCR 64
TABLE 14. QUANTITATIVE RT-PCR DETECTIONS ON THE EXPRESSION PROFILES 65
FIGURE 8. WESTERN BLOTTING ANALYSIS OF STATHMIN (STMN1). 66
CHAPTER 4 MUTATION ANALYSES ON CODING REGIONS OF PIGC GENE 67
4.1 INTRODUCTION 67
4.2 MATERIALS AND METHODS 67
4.3 RESULTS 70
4.4 DISCUSSIONS 72
TABLE 15. PREDICTIONS OF FUNCTIONAL GENOMICS IN TWENTY-ONE GENES 74
TABLE 16. GENOMIC ORGANIZATION IN HUMAN PIGC 75
TABLE 17. SEQUENCE ANALYSES IN PIGC CODING REGION 76
FIGURE 9. FOUR PIGC VARIATIONS IN NUCLEOTIDE SEQUENCES 78
FIGURE 10. IN PROTEIN LEVEL, THERE ARE THREE PIGC VARIATIONS. 79
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