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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊曜宇 | |
| dc.contributor.author | Chi-Cheng Huang | en |
| dc.contributor.author | 黃其晟 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:04:34Z | - |
| dc.date.available | 2016-08-11 | |
| dc.date.copyright | 2014-08-11 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-06-29 | |
| dc.identifier.citation | 1. Annual Report 2010, Health Promotion Administration, Ministry of Health and Welfare, Executive Yuan, R.O.C.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58014 | - |
| dc.description.abstract | 基因拷貝數目變異(copy number variation, CNV)和差異性基因表現間的相互關聯可以增進乳癌分子醫學的了解並找出癌症相關的標的基因。在本研究中，我們使用微陣列基因晶片找出在競爭性基因雜交(comparative genomic hybridization, CGH)和基因表現間有同向性變異的基因，並使用這些同向性基因來建立漢民族乳癌的基因標記。
我們對台灣乳癌檢體進行了23片競爭性基因雜交和81片基因表現的微陣列基因晶片，其中有21組檢體同時進行了競爭性基因雜交和基因表現兩種實驗。我們把在兩種平台上有一致性變異的同向性基因找出，並使用這些同向性基因建立臨床雌激素接受體ER、人類上皮生長因子接受體第二型HER2和無疾病存活期相關的基因標記。 同向性基因標記基因的分布和染色體的位置有強烈的關聯:如雌激素接受體ER 標記基因多位於第16號染色體，人類上皮生長因子第二型接受體HER2標記基因位於第17號染色體。我們使用了16個基因(RCAN3, MCOLN2, DENND2D, RWDD3,ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E,HBXIP, CSDE1, MRPL20, IKZF1,與COL20A1)建立的第一主成分來建立乳癌風險預測模型，在合併408 片微陣列晶片組成的漢民族乳癌研究對象中，預測高風險和低風險的乳癌病患表現出不同的存活趨勢。經歷復發、遠端轉移或死亡的病患比起無病存活者有顯著較高的危險分數(0.241 相對於0，P值小於0.001)。在分組分析中，不論臨床雌激素接受體和人類上皮生長因子接受體第二型的狀態，同向性基因標記乳癌風險預測模型都能維持其鑑別能力。與其他已發表的乳癌基因標記相比，乳癌同性性基因標記有較佳的預後鑑別力，其中許多同向性基因標的無法由傳統的表現型相關或基因個別變異的篩選方式辨識篩選出來。 藉由同時進行競爭性基因雜交和基因表現的數據分析，我們可以找出乳癌中具預後價值的生物標記。 | zh_TW |
| dc.description.abstract | The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers.
We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival. Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. Prognostic comparisons with published gene expression signatures showed a better discerning ability of concurrent genes, many of which were rarely identifiable if expression data were pre-selected by phenotype correlations or variability of individual genes. We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:04:34Z (GMT). No. of bitstreams: 1 ntu-103-D95945005-1.pdf: 10098603 bytes, checksum: bba33a0fab5daa4c4ace5e26c387cb24 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 目錄
口試委員會審定書 i 致謝 ii 目錄 iii 中文摘要 1 英文摘要 3 Chapter 1 Genomic and transcriptional research in breast cancer: literature reviews 5 1.1 Background and significance 6 1.2 Gene expression in breast cancers 7 1.2.1 Intrinsic subtypes and basal-like breast cancer 7 1.2.2 The 70-gene signature 9 1.3 Gene expression associated with clinical stage and differential grade 9 1.4 Concordance across gene expression signatures 12 1.5 Genomic variations in breast cancers 13 1.5.1 Genomic variation studies: literature reviews 14 1.5.2 Prognostic genomic variations 14 1.5.3 Correlations between copy number variations and gene expression 15 1.5.4 Concurrent analysis: a hypothesis 15 1.6 An overview of the dissertation 16 Chapter 2 Study population and microarray experiments 18 2.1 Breast cancer samples 18 2.2 Microarray experiments: gene expression 19 2.3 Microarray experiments: comparative genomic hybridization 20 2.4 Copy number variation detection 21 Chapter 3 Gene expression experiments: Prediction consistency and clinical presentations of breast cancer molecular subtypes for Han Chinese population 23 3.1 Abstract 23 3.2 Background 24 3.3 Materials and methods 26 3.3.1 Study population and microarray experiments 26 3.3.2 Intrinsic gene lists and prototypical samples 28 3.3.3 Single sample prediction and systemic bias adjustment 29 3.4 Results 29 3.4.1 Distributions of molecular subtypes 29 3.4.2 Agreement between adjustment methods with the same intrinsic genes 30 3.4.3 Agreement between intrinsic gene sets with the same adjustment 30 3.4.4 Clinical features of molecular subtypes 31 3.5 Discussion 32 Chapter 4 Concurrent analysis between genomic and transcriptional alternations: Concurrent genes signatures for Han Chinese breast cancers 38 4.1 Abstract 38 4.2 Materials and methods 40 4.2.1 Concurrent gains and losses 40 4.2.2 Combined dataset 41 4.2.3 Determining clinical ER and HER2 status from gene expression data 42 4.2.4 Concurrent signatures and classification algorithms 43 4.2.5 Breast cancer risk (survival) predictive model 45 4.2.6 Molecular subtyping by intrinsic genes 46 4.2.7 Breast cancer risk predictive model based on genes from Amsterdam, Rotterdam, and Oncotype DXTM signatures 47 4.2.8 Gene set enrichment analysis for concurrent genes 48 4.2.9 Concurrent gene sets for GSEA 49 4.3 Results 50 4.3.1 Analysis of array CGH experiments 50 4.3.2 Correlations between CNV and gene expression profiles 51 4.3.3 ER signature 52 4.3.4 HER2 signature 54 4.3.5 Survival prediction model 56 4.3.6 Prognostic comparisons between concurrent genes and Stanford/UNC intrinsic genes 58 4.3.7 Prognostic comparisons between concurrent genes and genes reported in Amsterdam/Rotterdam/Oncotype DXTM signatures 60 4.3.8 GSEA for concurrent gene signatures 61 4.4 Discussion 63 Chapter 5 Microarray classification algorithm: Multiclass prediction with partial least square regression for gene expression data: applications in breast cancer intrinsic taxonomy 73 5.1 Abstract 73 5.2 Introduction 74 5.3 Materials and methods 76 5.3.1 Breast cancer intrinsic taxonomy 76 5.3.2 PLS-regression classifier 77 5.3.3 Validation dataset 80 5.4 Results 82 5.4.1 PLS-regression in prototypical arrays 82 5.4.2 PLS regression in validation arrays 82 5.4.3 Clinical presentations and prognostic discrepancies among intrinsic taxonomy 83 5.5 Discussion 84 Chapter 6 Refinement of breast cancer risk prediction with concordant leading edge subsets from prognostic gene signatures 92 6.1 Abstract 92 6.2 Introduction 93 6.3 Materials and Methods 96 6.3.1 Prognostic signatures for breast cancer 96 6.3.2 Gene identity conversions 97 6.3.3 Microarray depositories for training and validation purposes 98 6.3.4 Prognostic gene signature sets identified by supervised principal components 100 6.3.5 Concordant leading edge analysis 101 6.3.6 Risk prediction model with partial least squares regression 102 6.3.7 Prognostic signature of breast cancer 103 6.3.8 Breast cancer microarray studies 106 6.4 Results 110 6.4.1 Breast cancer prognostic signatures and derived gene sets in training datasets 110 6.4.2 Concordant leading edge subsets for individual prognostic signature 111 6.4.3 Breast cancer risk prediction in training datasets 112 6.4.4 Breast cancer risk prediction in independent datasets 113 6.5 Discussion 114 Chapter 7 Extended concurrent signature 124 7.1 Abstract 124 7.2 Materials and methods 126 7.2.1 Microarray experiments 126 7.2.2 PLS-regression tuned by concurrence 126 7.2.3 In-silicon validation 127 7.3 Results 127 7.3.1 Array CGH experiments 128 7.3.2 Extended concurrent genes 128 7.3.3 Signatures for ER, HER2, and disease-free survival 129 7.4 Conclusion 130 Chapter 8 Conclusion and future perspectives 131 8.1 Relevant breast cancer genomic/transcriptional studies 131 8.2 Summary and conclusion 142 8.3 Future perspectives 143 8.4 Purposed aims in future studies 144 References 147 表目錄 Tables 156 Table 2-1.. 156 Table 3-1.. 157 Table 3-2. 158 Table 3-3. 159 Table 3-4.. 160 Table 3-5.. 161 Table 3-6.. 162 Table 4-1. 163 Table 4-2.. 165 Table 4-3. 166 Table 4-4.. 167 Table 4-5. 169 Table 4-6.. 171 Table 4-7.. 172 Table 4-8.. 173 Table 4-9. 174 Table 4-10. 175 Table 4-11. 176 Table 4-12. 177 Table 4-13. 178 Table 4-14. 179 Table 4-15. 180 Table 5-1. 182 Table 5-2. 183 Table 5-3. 184 Table 5-4. 185 Table 5-5.. 186 Table 5-6. 187 Table 6-1. 188 Table 6-2. 189 Table 6-3. 190 Table 6-4.. 191 Table 6-5. 192 Table 6-6. 193 Table 6-7. 194 Table 6-8. 195 Table 6-9. 196 Table 6-10.. 197 Table 6-11. 198 Table 6-12. 199 Table 6-13. 201 Table 6-14. 202 Table 6-15. 203 Table 7-1. 204 Table 7-2. 205 Table 7-3. 206 Table 7-4. 207 圖目錄 Figures 208 Figure 1-1. 208 Figure 3-1. 209 Figure 4-1. 211 Figure 4-2. 212 Figure 4-3.. 214 Figure 4-4. 215 Figure 4-5. 216 Figure 4-6. 217 Figure 4-7. 218 Figure 4-8. 219 Figure 4-9. 220 Figure 4-10. 221 Figure 4-11. 222 Figure 4-12. 223 Figure 4-13. 224 Figure 4-14. 225 Figure 4-15. 226 Figure 4-16. 227 Figure 5-1. 228 Figure 5-2. 229 Figure 6-1. 231 Figure 6-2. 232 Figure 6-3. 233 Figure 6-4. 234 Figure 6-5. 235 Figure 6-6. 236 Figure 6-7. 239 Figure 7-1. 242 Figure 7-2. 243 Figure 7-3.. 244 Figure 7-4. 245 Figure 7-5. 246 Figure 7-6. 247 Figure 7-7. 248 Figure 7-8. 249 Figure 7-9. 250 Figure 8-1. 251 Figure 8-2. 252 | |
| dc.language.iso | en | |
| dc.subject | 乳癌 | zh_TW |
| dc.subject | 競爭性基因雜交 | zh_TW |
| dc.subject | 同向性基因 | zh_TW |
| dc.subject | 基因表現 | zh_TW |
| dc.subject | 漢民族 | zh_TW |
| dc.subject | 基因微陣列 | zh_TW |
| dc.subject | microarray | en |
| dc.subject | concurrent genes | en |
| dc.subject | gene expression | en |
| dc.subject | Han Chinese | en |
| dc.subject | breast cancer | en |
| dc.subject | comparative genomic hybridization | en |
| dc.title | 漢民族乳癌基因表現與競爭基因雜交之同向變異分析 | zh_TW |
| dc.title | Concurrent Analysis of Gene Expression and Comparative Genomic Hybridization for Han Chinese Breast Cancer | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 張金堅,侯明鋒,黃俊升,歐陽彥正,賴亮全 | |
| dc.subject.keyword | 乳癌,競爭性基因雜交,同向性基因,基因表現,漢民族,基因微陣列, | zh_TW |
| dc.subject.keyword | breast cancer,comparative genomic hybridization,concurrent genes,gene expression,Han Chinese,microarray, | en |
| dc.relation.page | 252 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-06-30 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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