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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 莊曜宇(Eric Y. Chuang) | |
dc.contributor.author | Yu-Wen Chen | en |
dc.contributor.author | 陳郁文 | zh_TW |
dc.date.accessioned | 2021-06-16T10:30:16Z | - |
dc.date.available | 2013-08-20 | |
dc.date.copyright | 2013-08-20 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-14 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60789 | - |
dc.description.abstract | 近年來,肺癌在世界上高居癌症死亡原因的前幾位。其中非小細胞肺癌(non-small cell lung cancer)佔了大部分的肺癌案例。到目前為止,仍舊有許多我們所不了解的異質性存在於肺癌的病患當中,因而間接造成了長期以來肺癌居高不下的死亡率。復發風險至今還是難以預測,而且即使是在擁有相近臨床特徵的病人當中,最終的結果依然存在很大的變異性。因此,為了改善不同子型肺癌病人的預後診斷及管理,對於尋找出新的分子標記來輔助現今的臨床特徵,產生了急迫的需求。
大部分用來尋找預後標記的方法都是基於單一基因的統計分析,這種方式造成了幾個主要的問題。不同研究所找出來的預後標記彼此之間的重疊性往往很低,而當這些標記被應用到許多不同的資料組上的時候,往往也缺乏穩定的表現。另一方面,存在這些預後標記當中的生物意義,以及他們彼此間互動的關係,常常缺乏合理的解釋。對於為何這些標記會影響病人最終的結果,也因而相當難以闡述。 在本研究當中,我們提出了一個跨平台、基於基因群層級的存活分析方法。此方法結合了Cox正比例風險迴歸模型(Cox proportional hazard regression model)與基因群富集分析(gene set enrichment analysis, GSEA)。透過此方法,基於存在於基因群定義當中的知識,生物意義在一開始就被納入。同時,因為預後標記彼此間功能上的互動關係增多了,鑑定出來的預後標記在跨資料組的穩定度也獲得了提昇。藉助這些穩定的預後標記的幫助,病人的預後診斷及存活率在未來將能夠獲得顯著的提昇。 | zh_TW |
dc.description.abstract | Lung cancer has been the leading cause of cancer death worldwide in recent years and non-small cell lung cancer (NSCLC) accounts for most cases of lung cancer. There is still great heterogeneity poorly understood in lung cancer, which accounts for poor survival rate. Risk of recurrence of lung cancer is not predictable and a large variability of disease outcome has been observed for patients with similar clinical features. Therefore, there is an urgent need to discover new molecular signatures that could incorporate with current clinical features to improve the prognosis and management of patients within each subtype of NSCLC.
Most approaches for finding prognostic signatures were based on individual gene testing, which caused several major problems. First, there were only limited overlaps between prognostic signatures identified from different studies and these signatures demonstrated a lack of robustness when applying to multiple datasets. Moreover, it was difficult to elucidate the biological mechanisms underlining those prognostic signatures in connection to clinical outcomes. In this study, a cross-platform gene-set level approach integrating the cox proportional hazard regression model with gene set enrichment analysis (GSEA) was proposed. Biological meaning was included at first based on prior knowledge in gene-set definition. Robustness of signatures was also strengthened in companion with more functional interactions among these signatures. With the help of robust prognostic signatures, prognosis and survival of patients could be improved a lot in the future. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T10:30:16Z (GMT). No. of bitstreams: 1 ntu-102-R00945004-1.pdf: 1981161 bytes, checksum: 5e3e37365b37c52f732f006611a153cb (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii Abstract iii Contents v List of Figures viii List of Tables x Chapter 1 Introduction 1 1.1 Lung cancer 1 1.2 Microarray 2 1.3 Literature review 3 1.4 Cross-platform gene-set level survival analysis 7 Chapter 2 Materials and Methods 8 2.1 Patients and samples 8 2.2 Database 9 2.2.1 Database of transforming probes to genes 9 2.2.2 Molecular Signature Database (MsigDB) 9 2.3 Programming language and application 10 2.4 Microarray data preprocessing 10 2.5 Gene-level survival analysis 11 2.6 Gene-set level survival analysis 12 2.6.1 Gene set enrichment analysis (GSEA) 12 2.6.2 Leading-edge subsets 14 2.7 Identification of robust gene sets and prognostic signature 15 2.8 Outcome prediction model with weighted scoring 16 2.9 Comparison and integration with clinical factors 17 Chapter 3 Results 19 3.1 Identification of robust gene sets and prognostic signature 19 3.1.1 Robust gene sets 19 3.1.2 Core members of robust gene sets 20 3.1.3 Prognostic signature with corresponding weightings 20 3.2 Prognostic performance of the 24-robust-gene signature 21 3.2.1 Cross-dataset prognostic performance 21 3.2.2 ADC-specific characteristic 22 3.3 Comparison and integration with clinical factors 22 3.4 Comparison with gene-level selection method 23 3.4.1 Comparing the prognostic performances 24 3.4.2 Comparing the biological information content 24 3.5 Comparison with signatures in other studies 25 Chapter 4 Discussion 26 4.1 Prognostic performance in patients with ACT or ART 27 4.2 Identification of prognostic signature in SCC 28 4.3 Biological implications of the 24-robust-gene signatures 28 4.4 Decision of the weightings of the signature 30 References 32 | |
dc.language.iso | zh-TW | |
dc.title | 利用基因群層級之存活分析方法鑑定肺癌之穩定預後基因標記 | zh_TW |
dc.title | Identification of Common Prognostic Gene Expression Signature in Lung Cancer with Gene-Set Level Survival Analysis | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 賴亮全(Liang-Chuan Lai),蔡孟勳(Mong-Hsun Tsai),歐陽彥正(Yen-Cheng Oyang) | |
dc.subject.keyword | 非小細胞肺癌,微陣列生物晶片,預後標記,存活預測,基因群, | zh_TW |
dc.subject.keyword | NSCLC,microarray,prognostic signature,survival prediction,gene set, | en |
dc.relation.page | 68 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-08-15 | |
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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