整合多種基因組型態資料預測肺腺癌患者存活之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：46

、訪客IP：13.59.92.247

姓名

鄒友翔(Yu-Hsiang Tsou) 查詢紙本館藏

畢業系所

系統生物與生物資訊研究所

論文名稱

整合多種基因組型態資料預測肺腺癌患者存活之研究
(Integrative analysis of multiple genomic data types to predict the survival in lung adenocarcinoma)

相關論文

★ 整合深度學習方法預測年齡以及衰老基因之研究	★ 運用深度學習方法預測阿茲海默症惡化與腦中風手術存活
★ 運用深度學習方法預測癌症種類及存活死亡與治癒復發	★ 基於檢驗數值的糖尿病腎病變預測模型
★ 機械循環拉伸對肺癌細胞功能的影響之研究	★ 以系統生物學策略探討臍帶血來源之造血幹細胞分子調控網路
★ TP53突變對具有EGFR突變的非小細胞肺癌患者帶來的影響	★ 以系統生物學方法探討肺腺癌抗藥性成因
★ 機械循環拉伸力對3D培養肺癌細胞之影響	★ PM2.5對人類心肺細胞的影響
★ 尼曼匹克症轉錄體學研究	★ 體外仿生肺肝纖維化3D模型研究
★ 肝纖維化細胞與動物模型以轉錄體資料分析比較	★ 基於深度學習之皮膚病兆切割之研究
★ 體外仿生心臟衰竭三維模型研究	★ 在大腸桿菌與酵母菌蛋白質體晶片中量化其蛋白質的濃度

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

目前高通量定序資料已廣泛的運用在肺腺癌的研究中，包括表觀基因組學，基因體學和轉錄組學等等；全面分子特徵有助於確定生物標誌物以及用於早期診斷與治療。因應精準醫療的臨床需求，目前次世代定序隨著高通量定序品質提高以及成本的降低，使得其應用在臨床的可行性大幅提高。本篇論文使用基因體圖譜計畫（TCGA）中的肺腺癌基因組數據。我們採用生物統計分析以及深度學習的方法，分析TCGA肺腺癌多種基因組數據。我們整合TCGA肺腺癌372位病人包括RNA、miRNA、甲基化基因組數據，建立預測因子模型來預測肺腺癌高風險和低風險患者疾病進展存活。在統計分析中找出十七基因印記可顯著預測患者疾病進展存活，並在另外三組肺腺癌病人中驗證所找出的基因印記能準確預測存活。在深度學習分析方面，我們透過自編碼器深度學習模型建立預測模型並在驗證組上有良好的預測效果。

摘要(英)

High-throughput genomic assays have been widely used to investigate lung adenocarcinoma by employing technologies of epigenomics, genomics and transcriptomics. Comprehensive characterization of molecular mechanisms has contributed to identify biomarkers for early diagnosis and treatment. Nowadays, high-throughput sequencing technology providing a higher coverage and lower cost is prevalent in clinical application. This study used biostatistical and deep learning-based computing methodologies to conduct data mining and modeling on adenocarcinoma lung cancer datasets extracted from TCGA data. We integrated RNA, miRNA, and DNA methylation genomic data from 372 lung adenocarcinoma patients. Based on biostatistical methods, we developed a 17-gene signature to distinguish the risk groups with disease-free survival. In addition, we validated a 17-gene signature in three independent lung adenocarcinoma cohorts. Besides statistical validation, we also used Deep Learning-based autoencoder modeling to validate these datasets.

關鍵字(中)

★ 肺腺癌
★ 基因定序
★ 生物統計
★ 深度學習
★ 機器學習

關鍵字(英)

★ lung adenocarcinoma
★ multiple genomic data
★ biostatistical
★ deep learning
★ machine learning

論文目次

中文摘要 i
Abstract ii
致謝 iii
一、緒論 1
1-1 研究背景、動機及目的 1
1-2肺腺癌（Lung Adenocarcinoma） 2
1-3目前肺癌治療方式 3
二、研究材料與方法 4
2-1 研究材料 4
2-1-1 癌症基因體圖譜（The Cancer Genome Atlas, TCGA） 4
2-1-2 基因表達資料庫（Gene Expression Omnibus , GEO） 4
2-2 研究方法 6
2-2-1 分析環境與套件 6
2-2-2 資料前處理 6
2-2-3 存活分析 7
2-2-4 斯皮爾曼等級相關係數 10
2-2-5　miRTarBase資料庫 10
2-2-6 自動編碼器（Autoencoder） 10
2-2-7 Ｋ-means分群法 11
2-2-8 變異數分析（Analysis of variance, ANOVA） 11
2-2-9 支援向量機（Support vector machine, SVM) 11
三、結果 12
3-1 生物統計分析 12
3-1-1 基因篩選 12
3-1-2 基因印記 15
3-1-3 驗證基因印記在驗證組中預測疾病存活復發 18
3-1-4 基因印記分類 21
3-1-5 Gene ontology分類 24
3-1-6 小結 25
3-2 深度學習分析 26
3-2-1 存活風險分群 26
3-2-2 在TCGA肺腺癌多基因組學中找出差異顯著的分群 28
3-2-3 支援向量機模型預測驗證組資料存活風險分群 28
四、討論 32
4-1 生物統計及深度學習所篩選基因交集 32
4-2 生物統計及深度學習所篩選miRNA交集 34
4-3 在生物統計及深度學習結果同為高風險或為低風險分群以及不同分群 34
4-4 分析方法比較 37
五、結論 38
六、參考文獻 39

參考文獻

1. Jiang Q, Greenberg RA: Deciphering the BRCA1 tumor suppressor network. Journal of Biological Chemistry 2015, 290(29):17724-17732.
2. Silver DP, Livingston DM: Mechanisms of BRCA1 tumor suppression. Cancer discovery 2012, 2(8):679-684.
3. Boulton S: Cellular functions of the BRCA tumour-suppressor proteins. Biochemical Society Transactions 2006, 34(5):633-645.
4. Chen H-Y, Yu S-L, Chen C-H, Chang G-C, Chen C-Y, Yuan A, Cheng C-L, Wang C-H, Terng H-J, Kao S-F: A five-gene signature and clinical outcome in non–small-cell lung cancer. New England Journal of Medicine 2007, 356(1):11-20.
5. Chaudhary K, Poirion OB, Lu L, Garmire LX: Deep Learning based multi-omics integration robustly predicts survival in liver cancer. Clinical Cancer Research 2017:clincanres. 0853.2017.
6. Cheng T-YD, Cramb SM, Baade PD, Youlden DR, Nwogu C, Reid ME: The international epidemiology of lung cancer: latest trends, disparities, and tumor characteristics. Journal of Thoracic Oncology 2016, 11(10):1653-1671.
7. Lai T-C, Chiang C-Y, Wu C-F, Yang S-L, Liu D-P, Chan C-C, Lin H-H: Ambient air pollution and risk of tuberculosis: a cohort study. Occup Environ Med 2016, 73(1):56-61.
8. Xing Y-F, Xu Y-H, Shi M-H, Lian Y-X: The impact of PM2. 5 on the human respiratory system. Journal of thoracic disease 2016, 8(1):E69.
9. Chiang P, Chen CW, Hsieh DP, Chan T-C, Chiang H-C, Wen C-P: Lung cancer risk in females due to exposures to PM2. 5 in Taiwan. The Open Epidemiology Journal 2014, 7(1).
10. Fu J, Jiang D, Lin G, Liu K, Wang Q: An ecological analysis of PM2. 5 concentrations and lung cancer mortality rates in China. BMJ open 2015, 5(11):e009452.
11. Chen H-Y, Yu S-L, Ho B-C, Su K-Y, Hsu Y-C, Chang C-S, Li Y-C, Yang S-Y, Hsu P-Y, Ho H: R331W missense mutation of oncogene YAP1 is a germline risk allele for lung adenocarcinoma with medical actionability. Journal of clinical oncology 2015, 33(20):2303-2310.
12. Coté ML, Liu M, Bonassi S, Neri M, Schwartz AG, Christiani DC, Spitz MR, Muscat JE, Rennert G, Aben KK: Increased risk of lung cancer in individuals with a family history of the disease: a pooled analysis from the International Lung Cancer Consortium. European Journal of Cancer 2012, 48(13):1957-1968.
13. Gaughan EM, Cryer SK, Yeap BY, Jackman DM, Costa DB: Family history of lung cancer in never smokers with non-small-cell lung cancer and its association with tumors harboring EGFR mutations. Lung cancer 2013, 79(3):193-197.
14. TCGA Home : https://cancergenome.nih.gov/.
15. 鄒佩玲吳: 美國癌症基因體圖譜計畫TCGA(The Cancer Genome Atlas)簡介. 內科學誌 2013.
16. Rousseaux S, Debernardi A, Jacquiau B, Vitte A-L, Vesin A, Nagy-Mignotte H, Moro-Sibilot D, Brichon P-Y, Lantuejoul S, Hainaut P: Ectopic activation of germline and placental genes identifies aggressive metastasis-prone lung cancers. Science translational medicine 2013, 5(186):186ra166-186ra166.
17. Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, Nicholson AG, Groome P, Mitchell A, Bolejack V: The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. Journal of Thoracic Oncology 2016, 11(1):39-51.
18. Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, Furuta K, Tsuta K, Shibata T, Yamamoto S: Identification of Genes Up-regulated in ALK-positive and EGFR/KRAS/ALK-negative Lung Adenocarcinomas. Cancer research 2011:canres. 1403.2011.
19. Yamauchi M, Yamaguchi R, Nakata A, Kohno T, Nagasaki M, Shimamura T, Imoto S, Saito A, Ueno K, Hatanaka Y: Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma. PloS one 2012, 7(9):e43923.
20. Sandoval J, Mendez Gonzalez J, Nadal E, Chen G, Carmona FJ, Sayols S, Moran S, Heyn H, Vizoso M, Gomez A: A prognostic DNA methylation signature for stage I non-small-cell lung cancer. Journal of clinical oncology 2013, 31:4140-4147.
21. Ross Ihaka RG: The R Project for Statistical Computing.
22. Trevor Hastie RT, Balasubramanian Narasimhan, Gilbert Chu: impute: Imputation for microarray data.
23. Xiang Q, Dai X, Deng Y, He C, Wang J, Feng J, Dai Z: Missing value imputation for microarray gene expression data using histone acetylation information. BMC bioinformatics 2008, 9(1):252.
24. David Meyer ED, Kurt Hornik, Andreas Weingessel, Friedrich Leisch, Chih-Chung Chang, Chih-Chen Lin: e1071.
25. Chang C-C, Lin C-J: LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST) 2011, 2(3):27.
26. JJ Allaire FC, RStudio, Google, Yuan Tang, Daniel Falbel, Wouter Van Der Bijl, Martin Studer R Interface to ′Keras′.
27. Chou C-H, Shrestha S, Yang C-D, Chang N-W, Lin Y-L, Liao K-W, Huang W-C, Sun T-H, Tu S-J, Lee W-H: miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic acids research 2017, 46(D1):D296-D302.
28. Rosa A, Vlassaks E, Pichaud F, Baum B: Ect2/Pbl acts via Rho and polarity proteins to direct the assembly of an isotropic actomyosin cortex upon mitotic entry. Developmental cell 2015, 32(5):604-616.
29. Murata Y, Minami Y, Iwakawa R, Yokota J, Usui S, Tsuta K, Shiraishi K, Sakashita S, Satomi K, Iijima T: ECT2 amplification and overexpression as a new prognostic biomarker for early‐stage lung adenocarcinoma. Cancer science 2014, 105(4):490-497.
30. Shi Y-X, Yin J-Y, Shen Y, Zhang W, Zhou H-H, Liu Z-Q: Genome-scale analysis identifies NEK2, DLGAP5 and ECT2 as promising diagnostic and prognostic biomarkers in human lung cancer. Scientific reports 2017, 7(1):8072.
31. Zhou S, Wang P, Su X, Chen J, Chen H, Yang H, Fang A, Xie L, Yao Y, Yang J: High ECT2 expression is an independent prognostic factor for poor overall survival and recurrence-free survival in non-small cell lung adenocarcinoma. PloS one 2017, 12(10):e0187356.
32. Justilien V, Ali SA, Jamieson L, Yin N, Cox AD, Der CJ, Murray NR, Fields AP: Ect2-dependent rRNA synthesis is required for KRAS-TRP53-driven lung adenocarcinoma. Cancer cell 2017, 31(2):256-269.
33. Baker MJ, Cooke M, Kazanietz MG: Nuclear PKCι-ECT2-Rac1 and ribosome biogenesis: a novel axis in lung tumorigenesis. Cancer cell 2017, 31(2):167-169.
34. Chen J, Xia H, Zhang X, Karthik S, Pratap SV, Ooi LL, Hong W, Hui KM: ECT2 regulates the Rho/ERK signalling axis to promote early recurrence in human hepatocellular carcinoma. Journal of hepatology 2015, 62(6):1287-1295.
35. Hirata D, Yamabuki T, Miki D, Ito T, Tsuchiya E, Fujita M, Hosokawa M, Chayama K, Nakamura Y, Daigo Y: Involvement of epithelial cell transforming sequence-2 oncoantigen in lung and esophageal cancer progression. Clinical Cancer Research 2009, 15(1):256-266.
36. Huff LP, DeCristo MJ, Trembath D, Kuan PF, Yim M, Liu J, Cook DR, Miller CR, Der CJ, Cox AD: The role of Ect2 nuclear RhoGEF activity in ovarian cancer cell transformation. Genes & cancer 2013, 4(11-12):460-475.
37. Jin Y, Yu Y, Shao Q, Ma Y, Zhang R, Yao H, Xu Y: Up-regulation of ECT2 is associated with poor prognosisn gastric cancer patients. International journal of clinical and experimental pathology 2014, 7(12):8724.
38. Tsai RY, Pederson T: Connecting the nucleolus to the cell cycle and human disease. The FASEB Journal 2014, 28(8):3290-3296.
39. Fields AP, Justilien V: The guanine nucleotide exchange factor (GEF) Ect2 is an oncogene in human cancer. Advances in enzyme regulation 2010, 50(1):190.
40. Wu N, Ren D, Li S, Ma W, Hu S, Jin Y, Xiao S: RCC2 over-expression in tumor cells alters apoptosis and drug sensitivity by regulating Rac1 activation. BMC cancer 2018, 18(1):67.
41. Chen Q-y, Zheng Y, Jiao D-m, Chen F-y, Hu H-z, Wu Y-q, Song J, Yan J, Wu L-j, Lv G-y: Curcumin inhibits lung cancer cell migration and invasion through Rac1-dependent signaling pathway. The Journal of nutritional biochemistry 2014, 25(2):177-185.
42. Fusella F, Ferretti R, Recupero D, Rocca S, Di Savino A, Tornillo G, Silengo L, Turco E, Cabodi S, Provero P: Morgana acts as a proto‐oncogene through inhibition of a ROCK–PTEN pathway. The Journal of pathology 2014, 234(2):152-163.
43. Ferretti R, Palumbo V, Di Savino A, Velasco S, Sbroggiò M, Sportoletti P, Micale L, Turco E, Silengo L, Palumbo G: Morgana/chp-1, a ROCK inhibitor involved in centrosome duplication and tumorigenesis. Developmental cell 2010, 18(3):486-495.
44. Amin E, Dubey BN, Zhang S-C, Gremer L, Dvorsky R, Moll JM, Taha MS, Nagel-Steger L, Piekorz RP, Somlyo AV: Rho-kinase: regulation,(dys) function, and inhibition. Biological chemistry 2013, 394(11):1399-1410.
45. Fang D, Huang S, Su S-B: Cyclin E1-CDK 2, a potential anticancer target. In.: Impact Journals, LLC; 2016.
46. Nakayama K, Rahman MT, Rahman M, Nakamura K, Ishikawa M, Katagiri H, Sato E, Ishibashi T, Iida K, Ishikawa N: CCNE1 amplification is associated with aggressive potential in endometrioid endometrial carcinomas. International journal of oncology 2016, 48(2):506-516.
47. Wang X, Sun Q, Chen C, Yin R, Huang X, Wang X, Shi R, Xu L, Ren B: ZYG11A serves as an oncogene in non-small cell lung cancer and influences CCNE1 expression. Oncotarget 2016, 7(7):8029.
48. Frickey T, Lupas AN: Phylogenetic analysis of AAA proteins. Journal of structural biology 2004, 146(1-2):2-10.
49. Li S, Rousseau D: ATAD3, a vital membrane bound mitochondrial ATPase involved in tumor progression. Journal of bioenergetics and biomembranes 2012, 44(1):189-197.
50. Mao J, Fan S, Ma W, Fan P, Wang B, Zhang J, Wang H, Tang B, Zhang Q, Yu X: Roles of Wnt/β-catenin signaling in the gastric cancer stem cells proliferation and salinomycin treatment. Cell death & disease 2015, 5(1):e1039.
51. Mu J, Pang Q, Guo Y-H, Chen J-G, Zeng W, Huang Y-J, Zhang J, Feng B: Functional implications of microRNA-215 in TGF-β1-induced phenotypic transition of mesangial cells by targeting CTNNBIP1. PloS one 2013, 8(3):e58622.
52. Takahashi-Yanaga F, Kahn M: Targeting Wnt signaling: can we safely eradicate cancer stem cells? Clinical cancer research 2010, 16(12):3153-3162.
53. Sumita K, Lo Y-H, Takeuchi K, Senda M, Kofuji S, Ikeda Y, Terakawa J, Sasaki M, Yoshino H, Majd N: The lipid kinase PI5P4Kβ is an intracellular GTP sensor for metabolism and tumorigenesis. Molecular cell 2016, 61(2):187-198.
54. Kollareddy M, Dimitrova E, Vallabhaneni KC, Chan A, Le T, Chauhan KM, Carrero ZI, Ramakrishnan G, Watabe K, Haupt Y: Regulation of nucleotide metabolism by mutant p53 contributes to its gain-of-function activities. Nature communications 2015, 6:7389.
55. Fiume R, Jones DR, Divecha N: PIP4K2B: Coupling GTP Sensing to PtdIns5P Levels to Regulate Tumorigenesis. Trends in biochemical sciences 2016, 41(6):473-475.
56. Guillemot L, Paschoud S, Jond L, Foglia A, Citi S: Paracingulin regulates the activity of Rac1 and RhoA GTPases by recruiting Tiam1 and GEF-H1 to epithelial junctions. Molecular biology of the cell 2008, 19(10):4442-4453.
57. Wang B, Krall EB, Aguirre AJ, Kim M, Widlund HR, Doshi MB, Sicinska E, Sulahian R, Goodale A, Cowley GS: ATXN1L, CIC, and ETS transcription factors modulate sensitivity to MAPK pathway inhibition. Cell reports 2017, 18(6):1543-1557.
58. Schwarz-Romond T, Asbrand C, Bakkers J, Kühl M, Schaeffer H-J, Huelsken J, Behrens J, Hammerschmidt M, Birchmeier W: The ankyrin repeat protein Diversin recruits Casein kinase Iε to the β-catenin degradation complex and acts in both canonical Wnt and Wnt/JNK signaling. Genes & development 2002, 16(16):2073-2084.
59. Li S, Chai Z, Li Y, Liu D, Bai Z, Li Y, Situ Z: BZW1, a novel proliferation regulator that promotes growth of salivary muocepodermoid carcinoma. Cancer letters 2009, 284(1):86-94.
60. Lecona E, Rojas LA, Bonasio R, Johnston A, Fernández-Capetillo O, Reinberg D: Polycomb protein SCML2 regulates the cell cycle by binding and modulating CDK/CYCLIN/p21 complexes. PLoS biology 2013, 11(12):e1001737.
61. Yasunaga Si, Ohtsubo M, Ohno Y, Saeki K, Kurogi T, Tanaka-Okamoto M, Ishizaki H, Shirai M, Mihara K, Brock HW: Scmh1 has E3 ubiquitin ligase activity for geminin and histone H2A and regulates geminin stability directly or indirectly via transcriptional repression of Hoxa9 and Hoxb4. Molecular and cellular biology 2013, 33(4):644-660.
62. Bi J, Chase SE, Pellenz CD, Kurihara H, Fanning AS, Krendel M: Myosin 1e is a component of the glomerular slit diaphragm complex that regulates actin reorganization during cell-cell contact formation in podocytes. American Journal of Physiology-Renal Physiology 2013, 305(4):F532-F544.
63. Krendel M, Kim SV, Willinger T, Wang T, Kashgarian M, Flavell RA, Mooseker MS: Disruption of Myosin 1e promotes podocyte injury. Journal of the American Society of Nephrology 2009, 20(1):86-94.
64. Ouderkirk JL, Krendel M: Non‐muscle myosins in tumor progression, cancer cell invasion, and metastasis. Cytoskeleton 2014, 71(8):447-463.
65. Hallett RM, Dvorkin-Gheva A, Bane A, Hassell JA: A gene signature for predicting outcome in patients with basal-like breast cancer. Scientific reports 2012, 2:227.
66. Jackson SE: Hsp90: structure and function. In: Molecular chaperones. edn.: Springer; 2012: 155-240.
67. Mayer MP, Le Breton L: Hsp90: breaking the symmetry. Molecular cell 2015, 58(1):8-20.
68. Schliekelman MJ, Taguchi A, Zhu J, Dai X, Rodriguez J, Celiktas M, Zhang Q, Chin A, Wong C-H, Wang H: Molecular portraits of epithelial, mesenchymal and hybrid states in lung adenocarcinoma and their relevance to survival. Cancer research 2015:canres. 2535.2014.
69. Liu T-W, Ho C-W, Huang H-H, Chang S-M, Popat SD, Wang Y-T, Wu M-S, Chen Y-J, Lin C-H: Role for α-l-fucosidase in the control of Helicobacter pylori-infected gastric cancer cells. Proceedings of the National Academy of Sciences 2009, 106(34):14581-14586.

指導教授

許藝瓊(Yi-Chiung Hsu)

審核日期

2018-9-25

推文