(3.238.130.97) 您好!臺灣時間:2021/05/10 12:14
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:陳郁文
研究生(外文):Yu-Wen Chen
論文名稱: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
論文名稱(外文):Identification of Common Prognostic Gene Expression Signature in Lung Cancer with Gene-Set Level Survival Analysis
指導教授:莊曜宇
指導教授(外文):Eric Y. Chuang
口試委員:賴亮全蔡孟勳歐陽彥正
口試委員(外文):Liang-Chuan LaiMong-Hsun TsaiYen-Cheng Oyang
口試日期:2013-07-11
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生醫電子與資訊學研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:68
中文關鍵詞:非小細胞肺癌微陣列生物晶片預後標記存活預測基因群
外文關鍵詞:NSCLCmicroarrayprognostic signaturesurvival predictiongene set
相關次數:
  • 被引用被引用:0
  • 點閱點閱:861
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
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。

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.

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

1.Jemal, A., et al., Cancer statistics, 2010. CA Cancer J Clin, 2010. 60(5): p. 277-300.
2.Ferlay, J., D.M. Parkin, and E. Steliarova-Foucher, Estimates of cancer incidence and mortality in Europe in 2008. Eur J Cancer, 2010. 46(4): p. 765-81.
3.Ettinger, D.S., et al., Non-small cell lung cancer clinical practice guidelines in oncology. J Natl Compr Canc Netw, 2006. 4(6): p. 548-82.
4.Pretreatment evaluation of non-small-cell lung cancer. The American Thoracic Society and The European Respiratory Society. Am J Respir Crit Care Med, 1997. 156(1): p. 320-32.
5.McDoniels-Silvers, A.L., et al., Differential gene expression in human lung adenocarcinomas and squamous cell carcinomas. Clin Cancer Res, 2002. 8(4): p. 1127-38.
6.Daraselia, N., et al., Molecular signature and pathway analysis of human primary squamous and adenocarcinoma lung cancers. Am J Cancer Res, 2012. 2(1): p. 93-103.
7.Chen, C.J., et al., Epidemiologic characteristics and multiple risk factors of lung cancer in Taiwan. Anticancer Res, 1990. 10(4): p. 971-6.
8.Flehinger, B.J., M. Kimmel, and M.R. Melamed, The effect of surgical treatment on survival from early lung cancer. Implications for screening. Chest, 1992. 101(4): p. 1013-8.
9.Strauss, G.M., et al., Molecular and pathologic markers in stage I non-small-cell carcinoma of the lung. J Clin Oncol, 1995. 13(5): p. 1265-79.
10.Kato, H., et al., A randomized trial of adjuvant chemotherapy with uracil-tegafur for adenocarcinoma of the lung. N Engl J Med, 2004. 350(17): p. 1713-21.
11.Winton, T., et al., Vinorelbine plus cisplatin vs. observation in resected non-small-cell lung cancer. N Engl J Med, 2005. 352(25): p. 2589-97.
12.Olaussen, K.A., G. Mountzios, and J.C. Soria, ERCC1 as a risk stratifier in platinum-based chemotherapy for nonsmall-cell lung cancer. Curr Opin Pulm Med, 2007. 13(4): p. 284-9.
13.Trodella, L., et al., Adjuvant radiotherapy in non-small cell lung cancer with pathological stage I: definitive results of a phase III randomized trial. Radiother Oncol, 2002. 62(1): p. 11-9.
14.Gandara, D.R., et al., Molecular-clinical correlative studies in non-small cell lung cancer: application of a three-tiered approach. Lung Cancer, 2001. 34 Suppl 3: p. S75-80.
15.Jaluria, P., et al., A perspective on microarrays: current applications, pitfalls, and potential uses. Microb Cell Fact, 2007. 6: p. 4.
16.Kwiatkowski, P., et al., [DNA microarray-based gene expression profiling in diagnosis, assessing prognosis and predicting response to therapy in colorectal cancer]. Postepy Hig Med Dosw (Online), 2012. 66: p. 330-8.
17.Schena, M., et al., Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 1995. 270(5235): p. 467-70.
18.Lockhart, D.J., et al., Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol, 1996. 14(13): p. 1675-80.
19.Rattray, M., et al., Propagating uncertainty in microarray data analysis. Brief Bioinform, 2006. 7(1): p. 37-47.
20.Chen, D.T., et al., Prognostic and predictive value of a malignancy-risk gene signature in early-stage non-small cell lung cancer. J Natl Cancer Inst, 2011. 103(24): p. 1859-70.
21.Chen, H.Y., et al., A five-gene signature and clinical outcome in non-small-cell lung cancer. N Engl J Med, 2007. 356(1): p. 11-20.
22.Lu, Y., et al., A gene expression signature predicts survival of patients with stage I non-small cell lung cancer. PLoS Med, 2006. 3(12): p. e467.
23.Navab, R., et al., Prognostic gene-expression signature of carcinoma-associated fibroblasts in non-small cell lung cancer. Proc Natl Acad Sci U S A, 2011. 108(17): p. 7160-5.
24.Tomida, S., et al., Gene expression-based, individualized outcome prediction for surgically treated lung cancer patients. Oncogene, 2004. 23(31): p. 5360-70.
25.Wigle, D.A., et al., Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res, 2002. 62(11): p. 3005-8.
26.Xie, Y., et al., Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients. Clin Cancer Res, 2011. 17(17): p. 5705-14.
27.Jeong, Y., et al., Nuclear receptor expression defines a set of prognostic biomarkers for lung cancer. PLoS Med, 2010. 7(12): p. e1000378.
28.Fujii, T., et al., A preliminary transcriptome map of non-small cell lung cancer. Cancer Res, 2002. 62(12): p. 3340-6.
29.Yao, R., et al., Differentially expressed genes associated with mouse lung tumor progression. Oncogene, 2002. 21(37): p. 5814-21.
30.Kikuchi, T., et al., Expression profiles of non-small cell lung cancers on cDNA microarrays: identification of genes for prediction of lymph-node metastasis and sensitivity to anti-cancer drugs. Oncogene, 2003. 22(14): p. 2192-205.
31.Jones, M.H., et al., Two prognostically significant subtypes of high-grade lung neuroendocrine tumours independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. Lancet, 2004. 363(9411): p. 775-81.
32.Lee, E.S., et al., Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. Clin Cancer Res, 2008. 14(22): p. 7397-404.
33.Yao, J., et al., Identification of common prognostic gene expression signatures with biological meanings from microarray gene expression datasets. PLoS One, 2012. 7(9): p. e45894.
34.Lau, S.K., et al., Three-gene prognostic classifier for early-stage non small-cell lung cancer. J Clin Oncol, 2007. 25(35): p. 5562-9.
35.Shedden, K., et al., Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med, 2008. 14(8): p. 822-7.
36.Bianchi, F., et al., Survival prediction of stage I lung adenocarcinomas by expression of 10 genes. J Clin Invest, 2007. 117(11): p. 3436-44.
37.Tang, H., et al., A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients. Clin Cancer Res, 2013. 19(6): p. 1577-86.
38.Kang, J., A.D. D''Andrea, and D. Kozono, A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy. J Natl Cancer Inst, 2012. 104(9): p. 670-81.
39.Subramanian, A., et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 2005. 102(43): p. 15545-50.
40.Edgar, R., M. Domrachev, and A.E. Lash, Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 2002. 30(1): p. 207-10.
41.Raponi, M., et al., Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung. Cancer Res, 2006. 66(15): p. 7466-72.
42.Bhattacharjee, A., et al., Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A, 2001. 98(24): p. 13790-5.
43.Bild, A.H., et al., Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature, 2006. 439(7074): p. 353-7.
44.Takeuchi, T., et al., Expression profile-defined classification of lung adenocarcinoma shows close relationship with underlying major genetic changes and clinicopathologic behaviors. J Clin Oncol, 2006. 24(11): p. 1679-88.
45.Tomida, S., et al., Relapse-related molecular signature in lung adenocarcinomas identifies patients with dismal prognosis. J Clin Oncol, 2009. 27(17): p. 2793-9.
46.Zhu, C.Q., et al., Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer. J Clin Oncol, 2010. 28(29): p. 4417-24.
47.Nguyen, D.X., et al., WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis. Cell, 2009. 138(1): p. 51-62.
48.Kanehisa, M. and S. Goto, KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 2000. 28(1): p. 27-30.
49.Joshi-Tope, G., et al., Reactome: a knowledgebase of biological pathways. Nucleic Acids Res, 2005. 33(Database issue): p. D428-32.
50.Allen, J.D., M. Chen, and Y. Xie, Model-Based Background Correction (MBCB): R Methods and GUI for Illumina Bead-array Data. J Cancer Sci Ther, 2009. 1(1): p. 25-27.
51.Lind, P.A., et al., Receiver operating characteristic curves to assess predictors of radiation-induced symptomatic lung injury. Int J Radiat Oncol Biol Phys, 2002. 54(2): p. 340-7.
52.Polager, S. and D. Ginsberg, p53 and E2f: partners in life and death. Nat Rev Cancer, 2009. 9(10): p. 738-48.
53.Aksoy, O., et al., The atypical E2F family member E2F7 couples the p53 and RB pathways during cellular senescence. Genes Dev, 2012. 26(14): p. 1546-57.
54.Brosh, R., et al., p53-Repressed miRNAs are involved with E2F in a feed-forward loop promoting proliferation. Mol Syst Biol, 2008. 4: p. 229.
55.Lomazzi, M., et al., Suppression of the p53- or pRB-mediated G1 checkpoint is required for E2F-induced S-phase entry. Nat Genet, 2002. 31(2): p. 190-4.
56.Chiarugi, V., L. Magnelli, and M. Cinelli, Complex interplay among apoptosis factors: RB, p53, E2F, TGF-beta, cell cycle inhibitors and the bcl2 gene family. Pharmacol Res, 1997. 35(4): p. 257-61.
57.Ookawa, K., et al., Alterations in expression of E2F-1 and E2F-responsive genes by RB, p53 and p21(Sdi1/WAF1/Cip1) expression. FEBS Lett, 2001. 500(1-2): p. 25-30.
58.Tophkhane, C., et al., p53 inactivation upregulates p73 expression through E2F-1 mediated transcription. PLoS One, 2012. 7(8): p. e43564.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔