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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 | 周玉山(Yuh-Shan Jou) | |
dc.contributor.author | Jou-Ho Shih | en |
dc.contributor.author | 施柔合 | zh_TW |
dc.date.accessioned | 2021-06-17T08:23:25Z | - |
dc.date.available | 2020-08-22 | |
dc.date.copyright | 2019-08-22 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-13 | |
dc.identifier.citation | 1. Ferlay, J., Colombet, M., Soerjomataram, I., Mathers, C., Parkin, D.M., Piñeros, M., Znaor, A. and Bray, F. (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. International Journal of Cancer, 144, 1941-1953. 2. Siegel, R.L., Miller, K.D. and Jemal, A. (2019) Cancer statistics, 2019. CA: A Cancer Journal for Clinicians, 69, 7-34. 3. Hanahan, D. and Weinberg, Robert A. (2011) Hallmarks of Cancer: The Next Generation. Cell, 144, 646-674. 4. Siegel, R.L., Miller, K.D. and Jemal, A. (2017) Cancer Statistics, 2017. CA Cancer J Clin, 67, 7-30. 5. Chan, B.A. and Hughes, B.G. (2015) Targeted therapy for non-small cell lung cancer: current standards and the promise of the future. Transl Lung Cancer Res, 4, 36-54. 6. Harrow, J., Frankish, A., Gonzalez, J.M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B.L., Barrell, D., Zadissa, A., Searle, S. et al. (2012) GENCODE: the reference human genome annotation for The ENCODE Project. Genome research, 22, 1760-1774. 7. Consortium, E.P. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature, 489, 57-74. 8. Garraway, L.A. and Lander, E.S. (2013) Lessons from the cancer genome. Cell, 153, 17-37. 9. Calore, F., Lovat, F. and Garofalo, M. (2013) Non-coding RNAs and cancer. International journal of molecular sciences, 14, 17085-17110. 10. Pereira Fernandes, D., Bitar, M., Jacobs, M.F. and Barry, G. (2018) Long Non-Coding RNAs in Neuronal Aging. Non-Coding RNA, 4. 11. Vance, K.W. and Ponting, C.P. (2014) Transcriptional regulatory functions of nuclear long noncoding RNAs. Trends Genet, 30, 348-355. 12. Ng, S.-Y., Bogu, Gireesh K., Soh, Boon S. and Stanton, Lawrence W. (2013) The Long Noncoding RNA <em>RMST</em> Interacts with SOX2 to Regulate Neurogenesis. Molecular Cell, 51, 349-359. 13. Spizzo, R., Almeida, M.I., Colombatti, A. and Calin, G.A. (2012) Long non-coding RNAs and cancer: a new frontier of translational research? Oncogene, 31, 4577-4587. 14. Lu, T., Wang, Y., Chen, D., Liu, J. and Jiao, W. (2018) Potential clinical application of lncRNAs in non-small cell lung cancer. Onco Targets Ther, 11, 8045-8052. 15. Weber, D.G., Johnen, G., Casjens, S., Bryk, O., Pesch, B., Jöckel, K.-H., Kollmeier, J. and Brüning, T. (2013) Evaluation of long noncoding RNA MALAT1 as a candidate blood-based biomarker for the diagnosis of non-small cell lung cancer. BMC Research Notes, 6, 518. 16. Xie, H., Ma, H. and Zhou, D. (2013) Plasma HULC as a promising novel biomarker for the detection of hepatocellular carcinoma. Biomed Res Int, 2013, 136106-136106. 17. Li, Z. and Rana, T.M. (2014) Therapeutic targeting of microRNAs: current status and future challenges. Nature Reviews Drug Discovery, 13, 622. 18. Leyten, G.H.J.M., Hessels, D., Jannink, S.A., Smit, F.P., de Jong, H., Cornel, E.B., de Reijke, T.M., Vergunst, H., Kil, P., Knipscheer, B.C. et al. (2014) Prospective Multicentre Evaluation of PCA3 and TMPRSS2-ERG Gene Fusions as Diagnostic and Prognostic Urinary Biomarkers for Prostate Cancer. European Urology, 65, 534-542. 19. van Dam, S., Vosa, U., van der Graaf, A., Franke, L. and de Magalhaes, J.P. (2017) Gene co-expression analysis for functional classification and gene-disease predictions. Briefings in bioinformatics. 20. Sas-Chen, A., Srivastava, S. and Yarden, Y. (2017) The short and the long: non-coding RNAs and growth factors in cancer progression. Biochemical Society transactions, 45, 51-64. 21. Ehsani, R. and Drabløs, F. (2018) Measures of co-expression for improved function prediction of long non-coding RNAs. BMC bioinformatics, 19, 533-533. 22. Guo, X., Gao, L., Liao, Q., Xiao, H., Ma, X., Yang, X., Luo, H., Zhao, G., Bu, D., Jiao, F. et al. (2013) Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks. Nucleic Acids Res, 41, e35-e35. 23. Liao, Q., Liu, C., Yuan, X., Kang, S., Miao, R., Xiao, H., Zhao, G., Luo, H., Bu, D., Zhao, H. et al. (2011) Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network. Nucleic Acids Res, 39, 3864-3878. 24. Pyfrom, S.C., Luo, H. and Payton, J.E. (2019) PLAIDOH: a novel method for functional prediction of long non-coding RNAs identifies cancer-specific LncRNA activities. BMC Genomics, 20, 137. 25. Prensner, J.R., Iyer, M.K., Sahu, A., Asangani, I.A., Cao, Q., Patel, L., Vergara, I.A., Davicioni, E., Erho, N., Ghadessi, M. et al. (2013) The long noncoding RNA SChLAP1 promotes aggressive prostate cancer and antagonizes the SWI/SNF complex. Nature genetics, 45, 1392-1398. 26. Takayama, K.-I., Horie-Inoue, K., Katayama, S., Suzuki, T., Tsutsumi, S., Ikeda, K., Urano, T., Fujimura, T., Takagi, K., Takahashi, S. et al. (2013) Androgen-responsive long noncoding RNA CTBP1-AS promotes prostate cancer. EMBO J, 32, 1665-1680. 27. Chen, L., Puri, R., Lefkowitz, E.J. and Kakar, S.S. (2000) Identification of the human pituitary tumor transforming gene (hPTTG) family: molecular structure, expression, and chromosomal localization. Gene, 248, 41-50. 28. Marques, A.C., Dupanloup, I., Vinckenbosch, N., Reymond, A. and Kaessmann, H. (2005) Emergence of Young Human Genes after a Burst of Retroposition in Primates. PLOS Biology, 3, e357. 29. Zou, H., McGarry, T.J., Bernal, T. and Kirschner, M.W. (1999) Identification of a vertebrate sister-chromatid separation inhibitor involved in transformation and tumorigenesis. Science, 285, 418-422. 30. Zhang, W., Gong, W., Ai, H., Tang, J. and Shen, C. (2014) Gene expression analysis of lung adenocarcinoma and matched adjacent non-tumor lung tissue. Tumori, 100, 338-345. 31. Li, H., Yin, C., Zhang, B., Sun, Y., Shi, L., Liu, N., Liang, S., Lu, S., Liu, Y., Zhang, J. et al. (2013) PTTG1 promotes migration and invasion of human non-small cell lung cancer cells and is modulated by miR-186. Carcinogenesis, 34, 2145-2155. 32. Huang, J.L., Cao, S.W., Ou, Q.S., Yang, B., Zheng, S.H., Tang, J., Chen, J., Hu, Y.W., Zheng, L. and Wang, Q. (2018) The long non-coding RNA PTTG3P promotes cell growth and metastasis via up-regulating PTTG1 and activating PI3K/AKT signaling in hepatocellular carcinoma. Molecular cancer, 17, 93. 33. Mendez-Vidal, C., Gamez-Del Estal, M.M., Moreno-Mateos, M.A., Espina-Zambrano, A.G., Torres, B. and Pintor-Toro, J.A. (2013) PTTG2 silencing results in induction of epithelial-to-mesenchymal transition and apoptosis. Cell death disease, 4, e530. 34. Weng, W., Ni, S., Wang, Y., Xu, M., Zhang, Q., Yang, Y., Wu, Y., Xu, Q., Qi, P., Tan, C. et al. (2017) PTTG3P promotes gastric tumour cell proliferation and invasion and is an indicator of poor prognosis. Journal of cellular and molecular medicine, 21, 3360-3371. 35. Borgan, Ø. (2001) Modeling Survival Data: Extending the Cox Model. Terry M. Therneau and Patricia M. Grambsch, Springer-Verlag, New York, 2000. No. of pages: xiii + 350. Price: $69.95. ISBN 0-387-98784-3. Statistics in Medicine, 20, 2053-2054. 36. Langfelder, P. and Horvath, S. (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559. 37. Kao, H.-L., Yeh, Y.-C., Lin, C.-H., Hsu, W.-F., Hsieh, W.-Y., Ho, H.-L. and Chou, T.-Y. (2016) Diagnostic algorithm for detection of targetable driver mutations in lung adenocarcinomas: Comprehensive analyses of 205 cases with immunohistochemistry, real-time PCR and fluorescence in situ hybridization methods. Lung Cancer, 101, 40-47. 38. Lin, K.-T., Wang, Y.-W., Chen, C.-T., Ho, C.-M., Su, W.-H. and Jou, Y.-S. (2012) HDAC Inhibitors Augmented Cell Migration and Metastasis through Induction of PKCs Leading to Identification of Low Toxicity Modalities for Combination Cancer Therapy. Clinical Cancer Research, 18, 4691. 39. Wang, Y.-W., Tu, P.-H., Lin, K.-T., Lin, S.-C., Ko, J.-Y. and Jou, Y.-S. (2011) Identification of oncogenic point mutations and hyperphosphorylation of anaplastic lymphoma kinase in lung cancer. Neoplasia, 13, 704-715. 40. Okimoto, R.A., Breitenbuecher, F., Olivas, V.R., Wu, W., Gini, B., Hofree, M., Asthana, S., Hrustanovic, G., Flanagan, J., Tulpule, A. et al. (2016) Inactivation of Capicua drives cancer metastasis. Nature Genetics, 49, 87. 41. (2017) Enhancer RNAs - 2017 Methods and Protocols, Springer New York. 42. Rinn, J.L., Kertesz, M., Wang, J.K., Squazzo, S.L., Xu, X., Brugmann, S.A., Goodnough, L.H., Helms, J.A., Farnham, P.J., Segal, E. et al. (2007) Functional Demarcation of Active and Silent Chromatin Domains in Human HOX Loci by Noncoding RNAs. Cell, 129, 1311-1323. 43. Panda, A.C., Martindale, J.L. and Gorospe, M. (2016) Affinity Pulldown of Biotinylated RNA for Detection of Protein-RNA Complexes. Bio Protoc, 6, e2062. 44. Au - Chu, C., Au - Quinn, J. and Au - Chang, H.Y. (2012) Chromatin Isolation by RNA Purification (ChIRP). JoVE, e3912. 45. Langfelder, P. and Horvath, S. (2012) Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software; Vol 1, Issue 11 (2012). 46. Wang, J., Duncan, D., Shi, Z. and Zhang, B. (2013) WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic acids research, 41, W77-83. 47. Dallol, A., Da Silva, N.F., Viacava, P., Minna, J.D., Bieche, I., Maher, E.R. and Latif, F. (2002) SLIT2, a human homologue of the Drosophila Slit2 gene, has tumor suppressor activity and is frequently inactivated in lung and breast cancers. Cancer Res, 62, 5874-5880. 48. Nasarre, P., Potiron, V., Drabkin, H. and Roche, J. (2010) Guidance molecules in lung cancer. Cell adhesion migration, 4, 130-145. 49. Grando, S.A. (2014) Connections of nicotine to cancer. Nature reviews. Cancer, 14, 419-429. 50. Basile, J.R., Castilho, R.M., Williams, V.P. and Gutkind, J.S. (2006) Semaphorin 4D provides a link between axon guidance processes and tumor-induced angiogenesis. Proceedings of the National Academy of Sciences of the United States of America, 103, 9017-9022. 51. GM., C. (2000) In Associates, S. M. S. (ed.), The Cell: A Molecular Approach. 2nd edition. Sinauer Associates. 52. GM., C. (2000) In Associates, S. M. S. (ed.), The Cell: A Molecular Approach. 2nd edition. 53. Tomczak, K., Czerwinska, P. and Wiznerowicz, M. (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemporary oncology (Poznan, Poland), 19, A68-77. 54. Iyer, M.K., Niknafs, Y.S., Malik, R., Singhal, U., Sahu, A., Hosono, Y., Barrette, T.R., Prensner, J.R., Evans, J.R., Zhao, S. et al. (2015) The landscape of long noncoding RNAs in the human transcriptome. Nat Genet, 47, 199-208. 55. Dominguez, A.A., Lim, W.A. and Qi, L.S. (2016) Beyond editing: repurposing CRISPR-Cas9 for precision genome regulation and interrogation. Nat Rev Mol Cell Biol, 17, 5-15. 56. Poliseno, L., Marranci, A. and Pandolfi, P.P. (2015) Pseudogenes in Human Cancer. Front Med (Lausanne), 2, 68-68. 57. Burge, C. and Karlin, S. (1997) Prediction of complete gene structures in human genomic DNA11Edited by F. E. Cohen. Journal of Molecular Biology, 268, 78-94. 58. Kong, L., Zhang, Y., Ye, Z.-Q., Liu, X.-Q., Zhao, S.-Q., Wei, L. and Gao, G. (2007) CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res, 35, W345-W349. 59. Sun, K., Chen, X., Jiang, P., Song, X., Wang, H. and Sun, H. (2013) iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data. BMC genomics, 14 Suppl 2, S7-S7. 60. Wang, L., Park, H.J., Dasari, S., Wang, S., Kocher, J.-P. and Li, W. (2013) CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res, 41, e74-e74. 61. Chen, H., Lee, J., Kljavin, N.M., Haley, B., Daemen, A., Johnson, L. and Liang, Y. (2015) Requirement for BUB1B/BUBR1 in tumor progression of lung adenocarcinoma. Genes Cancer, 6, 106-118. 62. Fu, X., Chen, G., Cai, Z.-D., Wang, C., Liu, Z.-Z., Lin, Z.-Y., Wu, Y.-D., Liang, Y.-X., Han, Z.-D., Liu, J.-C. et al. (2016) Overexpression of BUB1B contributes to progression of prostate cancer and predicts poor outcome in patients with prostate cancer. Onco Targets Ther, 9, 2211-2220. 63. Yuan, B., Xu, Y., Woo, J.-H., Wang, Y., Bae, Y.K., Yoon, D.-S., Wersto, R.P., Tully, E., Wilsbach, K. and Gabrielson, E. (2006) Increased Expression of Mitotic Checkpoint Genes in Breast Cancer Cells with Chromosomal Instability. Clinical Cancer Research, 12, 405. 64. Noh, J.H., Kim, K.M., McClusky, W.G., Abdelmohsen, K. and Gorospe, M. (2018) Cytoplasmic functions of long noncoding RNAs. Wiley Interdiscip Rev RNA, 9, e1471. 65. Long, Y., Wang, X., Youmans, D.T. and Cech, T.R. (2017) How do lncRNAs regulate transcription? Science advances, 3, eaao2110. 66. Wonsey, D.R. and Follettie, M.T. (2005) Loss of the Forkhead Transcription Factor FoxM1 Causes Centrosome Amplification and Mitotic Catastrophe. Cancer Res, 65, 5181. 67. Ma, Q., Liu, Y., Shang, L., Yu, J. and Qu, Q. (2017) The FOXM1/BUB1B signaling pathway is essential for the tumorigenicity and radioresistance of glioblastoma. Oncol Rep, 38, 3367-3375. 68. Wan, X., Yeung, C., Kim, S.Y., Dolan, J.G., Ngo, V.N., Burkett, S., Khan, J., Staudt, L.M. and Helman, L.J. (2012) Identification of FoxM1/Bub1b signaling pathway as a required component for growth and survival of rhabdomyosarcoma. Cancer Res, 72, 5889-5899. 69. Lee, Y., Kim, K.H., Kim, D.G., Cho, H.J., Kim, Y., Rheey, J., Shin, K., Seo, Y.J., Choi, Y.-S., Lee, J.-I. et al. (2015) FoxM1 Promotes Stemness and Radio-Resistance of Glioblastoma by Regulating the Master Stem Cell Regulator Sox2. PLoS One, 10, e0137703-e0137703. 70. Ganguly, R., Mohyeldin, A., Thiel, J., Kornblum, H.I., Beullens, M. and Nakano, I. (2015) MELK-a conserved kinase: functions, signaling, cancer, and controversy. Clin Transl Med, 4, 11-11. 71. Kim, S.-H., Joshi, K., Ezhilarasan, R., Myers, T.R., Siu, J., Gu, C., Nakano-Okuno, M., Taylor, D., Minata, M., Sulman, E.P. et al. (2015) EZH2 protects glioma stem cells from radiation-induced cell death in a MELK/FOXM1-dependent manner. Stem Cell Reports, 4, 226-238. 72. Yongsheng Li, L.L., Zishan Wang, Tao Pan, Nidhi Sahni, Xiyun Jin, Guangjuan Wang, Junyi Li, Xiangyi Zheng, Yunpeng Zhang, Juan Xu, Song Yi and Xia Li. (2018) LncMAP: Pan-cancer atlas of long noncoding RNA-mediated transcriptional network perturbations. Nucleic Acids Research, 46, 1113–1123. 73. Signal, B., Gloss, B.S. and Dinger, M.E. (2016) Computational Approaches for Functional Prediction and Characterisation of Long Noncoding RNAs. Trends in genetics : TIG, 32, 620-637. 74. Botling, J., Edlund, K., Lohr, M., Hellwig, B., Holmberg, L., Lambe, M., Berglund, A., Ekman, S., Bergqvist, M., Ponten, F. et al. (2013) Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis, and tissue microarray validation. Clin Cancer Res, 19, 194-204. 75. Feng, L., Tong, R., Liu, X., Zhang, K., Wang, G., Zhang, L., An, N. and Cheng, S. (2016) A network-based method for identifying prognostic gene modules in lung squamous carcinoma. Oncotarget, 7, 18006-18020. 76. Li, Y., Tang, H., Sun, Z., Bungum, A.O., Edell, E.S., Lingle, W.L., Stoddard, S.M., Zhang, M., Jen, J., Yang, P. et al. (2013) Network-based approach identified cell cycle genes as predictor of overall survival in lung adenocarcinoma patients. Lung Cancer, 80, 91-98. 77. Yang, Y., Han, L., Yuan, Y., Li, J., Hei, N. and Liang, H. (2014) Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nature communications, 5, 3231. 78. Zhang, H., Su, Y., Xu, F., Kong, J., Yu, H. and Qian, B. (2013) Circulating microRNAs in relation to EGFR status and survival of lung adenocarcinoma in female non-smokers. PLoS One, 8, e81408. 79. Yongchun, Z., Linwei, T., Xicai, W., Lianhua, Y., Guangqiang, Z., Ming, Y., Guanjian, L., Yujie, L. and Yunchao, H. (2014) MicroRNA-195 inhibits non-small cell lung cancer cell proliferation, migration and invasion by targeting MYB. Cancer letters, 347, 65-74. 80. Wang, X., Wang, Y., Lan, H. and Li, J. (2014) MiR-195 inhibits the growth and metastasis of NSCLC cells by targeting IGF1R. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine, 35, 8765-8770. 81. Liu, B., Qu, J., Xu, F., Guo, Y., Wang, Y., Yu, H. and Qian, B. (2015) MiR-195 suppresses non-small cell lung cancer by targeting CHEK1. Oncotarget, 6, 9445-9456. 82. Guo, H., Li, W., Zheng, T. and Liu, Z. (2014) MiR-195 targets HDGF to inhibit proliferation and invasion of NSCLC cells. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine, 35, 8861-8866. 83. Guo, A.-Y., Sun, J., Jia, P. and Zhao, Z. (2010) A Novel microRNA and transcription factor mediated regulatory network in schizophrenia. BMC Systems Biology. 84. F Chen, Y.Z., E Parra, J Rodriguez, C Behrens, R Akbani, Y Lu, JM Kurie, DL Gibbons, GB Mills, II Wistuba and CJ Creighton. (2017) Multiplatform-based molecular subtypes of non-small-cell lung cancer. Oncogene, 36, 1384-1393. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74185 | - |
dc.description.abstract | 肺癌是全球癌症十大死因中排名第一的癌症,因此開發能提高肺癌病人存活率的標靶仍具急迫性。非編碼核糖核酸 (ncRNA) 被研究指出在癌症腫瘤生成過程中顯示異常表現,然而對於ncRNA在腫瘤生成的確切機制未知之外且臨床應用仍屬匱乏。因此,發展ncRNA所設計之基因網絡來精準預測ncRNA之生物功能及致病機制實屬必要。在此篇研究,我們設計了一個系統化方法來偵測具診療預測性之ncRNA,並預測其在肺癌中最主要的亞型,肺腺癌中的生物功能並且探討他們的致病機制。多筆肺腺癌病人的資料藉由生物資訊分析以及實驗檢測之下來評估診斷ncRNA之預測能力,我們從六個具預測能力之ncRNA (MIR497HG, HSP078, TBX5-AS1, LOC100506990, and C14orf64) 當中選了在所有資料中數值最一致的PTTG3P來做後續之分子生物機制探討。PTTG3P 在肺腺癌細胞中高度表現並縮短細胞有絲分裂過程的中期到末期轉換時間,增加細胞對抗癌藥物包含順鉑(cisplatin)以及紫杉醇(paclitaxel)的耐受性,增加癌細胞增生導致肺部原位腫瘤小鼠動物模式傾向較差存活率。同時在癌症基因體圖譜計畫(TCGA)所收集到的病人中PTTG3P表現量較高的病人其接受化療時亦傾向較差存活率。從機制層面探討發現,PTTG3P ncRNA藉由調控轉錄因子FOXM1來調節有絲分裂檢查酵素BUB1B的轉錄活動,並藉此影響腫瘤增生,抗藥性以及導致肺腺癌病人的高死亡率。整體而言,我的論文研究建立了一個整合性策略來探討具預後預測之驅動ncRNA並預測他們的功能,而此方法可以適用於所有癌症研究。不僅如此,我的研究結果顯示出由於高表現的PTTG3P透過ncRNA/FOXM1/BUB1B細胞信息傳導而導致在肺腺癌病人低存活率,而這個可以被視為一個在肺腺癌病人當中很好的標靶治療目標。 | zh_TW |
dc.description.abstract | Lung cancer is the most common cause of human cancer death worldwide with urgent needs of targets to prolong patient survival. Aberrant activation of non-coding RNAs (ncRNAs) during tumor progression remain functional unknown and thus limit clinical impacts. Precise ncRNA-based network prediction is necessary to reveal ncRNA cellular functions and pathological mechanisms. Here, we established a systemic approach to identify prognostic ncRNAs to predict their functions and explore their pathological mechanisms in lung adenocarcinoma (LUAD), which is the most predominant subtype of lung cancer. After in silico and experimental validation based on evaluations of prognostic value in multiple LUAD cohorts, we selected the PTTG3P pseudogene from among other prognostic ncRNAs (MIR497HG, HSP078, TBX5-AS1, LOC100506990, and C14orf64) for mechanistic studies. PTTG3P upregulation in LUAD cells shortened the metaphase to anaphase transition in mitosis, increases cell viability after cisplatin or paclitaxel treatment, facilitated tumor growth that led to poor survival in orthotopic lung models, and is associated with a poor survival rate in LUAD patients in The Cancer Genome Atlas (TCGA) cohort who received chemotherapy. Mechanistically, PTTG3P acts as an ncRNA that mediates the transcription factor FOXM1 to regulate the transcriptional activation of the mitotic checkpoint kinase BUB1B, which augments tumor growth and chemoresistance and leads to poor outcomes for LUAD patients. Overall, we established an integrative approach to uncover prognostic driver ncRNAs with functional prediction methods suitable for pancancer studies. Moreover, we revealed that PTTG3P, due to its upregulation of the ncRNA/FOXM1/BUB1B axis, could be a therapeutic target for LUAD patients. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:23:25Z (GMT). No. of bitstreams: 1 ntu-108-F00b48002-1.pdf: 4843150 bytes, checksum: 383e6886a3971bac3bcf91a449e4c004 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | Content 謝誌 i 中文摘要 iii Abstract v Chapter 1. Background and Introduction 1 1.1 The lethality of lung cancer in the leading cause of deaths 1 1.2 Treatment options for lung cancer 2 1.3 Non-coding RNAs (ncRNAs) in cancer biology 3 1.4 The applications of prognostic ncRNAs 5 1.5 Functional predictions and mechanistic examinations for ncRNAs 6 1.6 Pituitary tumor transforming gene (PTTG) 7 Chapter 2. Materials and Methods 9 2.1 Dataset collection 9 2.2 Identification of prognostic differential expressed probes in LUAD 9 2.3 Identification of ncRNAs-associated networks with functional prediction 10 2.4 LUAD Tissue arrays and RNA-ISH 11 2.5 LUAD cell lines 12 2.6 Transfections with knockdown or overexpression reagents 13 2.7 Lung orthotopic mouse models 14 2.8 Subcellular fractionation, RNA extraction and RT-qPCR 14 2.9 Cell proliferation and cell viability assays 15 2.10 Flow Cytometry 15 2.11 Live cell time-lapse imaging 15 2.12 Western blot 16 2.13 RNA-seq 16 2.14 Reporter constructs and luciferase assays 17 2.15 Chromatin-immunoprecipitation (ChIP) and RNA-immunoprecipitation (RIP) 18 2.16 RNA fluorescence in situ hybridization (RNA-FISH) 18 2.17 RNA pull-down 19 2.18 Chromatin Isolation by RNA Purification (ChIRP) 19 2.19 Statistical analysis 20 Chapter 3. Results 21 3.1 The comprehensive strategy for uncovering prognostic ncRNAs in LUAD. 21 3.2 Identification of prognostic candidates in LUAD. 22 3.3 Exploration of prognostic ncRNAs and their correlated modules in LUAD. 23 3.4 Biologically functional predictions of prognostic ncRNAs in LUAD. 24 3.5 In silico validations of prognostic ncRNAs in LUAD patients retrieved from TCGA. 26 3.6 PTTG3P expression was up-regulated and associated with poor prognosis in LUAD patients from Taiwan. 28 3.7 Selection of LUAD cell lines for examining functional validations. 30 3.8 Generation of PTTG3P-specific knockdown in H1299 and CL1-5 cells by CRISPR interference system. 31 3.9 Downregulated PTTG3P prolonged cell proliferation and increased the process of mitosis in LUAD cell lines. 32 3.10 PTTG3P expression was strongly associated with tumorigenesis and poor prognosis in orthotopic lung cancer model. 33 3.11 Examination of the coding potential for PTTG3P 34 3.12 PTTG3P as a ncRNA increased cell proliferation and decreased the process of mitosis in LUAD cell lines. 35 3.13 Functional examination of PTTG3P was validated in RNA-seq of PTTG3P-regulated LUAD cells. 36 3.14 BUB1B would be an important downstream target in PTTG3P-modulated signaling pathways. 37 3.15 PTTG3P regulated BUB1B transcriptional expression via modulating the promoter activity of BUB1B gene. 38 3.16 PTTG3P enhanced the promoter activities of BUB1B through cooperating with FOXM1. 39 3.17 PTTG3P formed the trimeric complex with FOXM1 and BUB1B. 41 3.18 Clinical applications for prognostic ncRNAs in LUAD. 42 3.19 PTTG3P expression was positively correlated with drug resistance. 43 3.20 Propose a comprehensive approach to identify the biological functions and molecular mechanisms of prognostic ncRNAs. 43 Chapter 4. Conclusion and Discussion 45 Chapter 5. Perspective 50 Reference 52 Figures 60 Figure 1. An integrative approach to predict the biological functions and molecular mechanisms of unexplored prognostic ncRNAs in LUAD. 60 Figure 2. Survival analysis of nine prognostic ncRNAs in LUAD. 61 Figure 3. Pathway analysis of 627 DEPs. 62 Figure 4. Construction of co-expressed modules for six prognostic ncRNAs in GSE31210. 63 Figure 5. Co-expression network of six prognostic ncRNAs in GSE31210. 64 Figure 6. Survival analysis of six prognostic ncRNAs in each LUAD dataset. 65 Figure 7. Pathway enrichment analysis of six prognostic ncRNAs. 66 Figure 8. In silico validation of selected ncRNAs expressions in TCGA. 67 Figure 9. RNA expression of six prognostic ncRNAs was examined by the LUAD cDNA array. 68 Figure 10. The competition assay was performed to examine the specificity of PTTG3P RNA-ISH probe. 69 Figure 11. Validation of the specificity of PTTG3P RNA-ISH probe. 70 Figure 12. RNA expression of PTTG3P was examined in the normal and tumor samples in Taiwanese LUAD patients. 71 Figure 13. Up-regulation of PTTG3P expression was highly associated with poor prognosis of Taiwanese LUAD patients. 72 Figure 14. PTTG3P expression intensity in different stages of LUAD patients. 73 Figure 15. PTTG3P expression was examined in LUAD cell lines. 74 Figure 16. PTTG3P expression was examined in LUAD cell lines for further experimental validation. 75 Figure 17. Knockdown of PTTG3P expression was performed in H1299 and CL1-5 cell lines by the CRISPR/dCas9-KRAB (CRISPRi) system. 76 Figure 18. Knockdown of PTTG3P expression decreased cell proliferation and prolonged metaphase-to-anaphase transition in H1299 and CL1-5 cells. 77 Figure 19. Knockdown of PTTG3P in H1299 cells were sequentially tracked by Flow Cytometry system. 78 Figure 20. Tumor progression was monitored in the lung orthotopic model with bioluminescence images of mice bearing H1299-sgCtrl-Luc+ and H1299-sgPTTG3P-1/2-Luc+ transfectants. 79 Figure 21. Knockdown of PTTG3P expression inhibited tumor growth and prolonged survival time in the lung orthotopic model. 80 Figure 22. PTTG3P expression was overexpressed in A549 and CL1-0 cell lines by transient transfection. 81 Figure 23. PTTG3P protein was detected in the 293T cell line. 82 Figure 24. PTTG3P-overexpressing A549 and CL1-0 cells were sorted and gated by Flow Cytometry analysis. 83 Figure 25. Overexpression of PTTG3P enhanced cell proliferation and prolonged metaphase-to-anaphase transition through RNA level. 84 Figure 26. RNA-seq of PTTG3P regulated groups (GSE114826) showed the highly consistent in the previous prediction. 85 Figure 27. BUB1B was detected as the downstream target of PTTG3P in LUAD datasets in comparison with RNA-seq of PTTG3P regulated groups. 86 Figure 28. BUB1B expression was regulated by PTTG3P. 87 Figure 29. PTTG3P perturbed cell proliferation via partially regulating BUB1B expression. 88 Figure 30. PTTG3P RNA was localized on the chromatin-associated region in LUAD cell lines. 89 Figure 31. Quality controls of subcellular fractionations was performed in A549 cells. 90 Figure 32. PTTG3P regulated BUB1B expression through modulating the binding complexes of the (-585/-236) promoter region on BUB1B gene. 91 Figure 33. PTTG3P regulated BUB1B expression via FOXM1. 92 Figure 34. PTTG3P formed the complex with FOXM1 to regulate the BUB1B transcriptional expression. 93 Figure 35. The clinical application of six prognostic candidates was predicted by WebGestalt. 94 Figure 36. Abnormal expression of PTTG3P perturbed the drug sensitivities of cisplatin and paclitaxel in LUAD cells. 95 Figure 37. Up-regulation of PTTG3P was highly correlated to poor prognosis in LUAD patients who received chemotherapy. 96 Figure 38. An integrative approach reveals prognostic ncRNAs with functional and mechanistic predictions in Lung Adenocarcinoma. 97 Figure 39. Up-regulation of PTTG3P expression was enriched in LUAD patients with up-regulation of cell cycle-associated molecular subtypes. 98 Figure 40. Up-regulation of PTTG3P was related to several cancer types with high hazard ratio. 99 Tables 100 Table 1. General characteristics of LUAD patients in selected public data sets. 100 Table 2. Clinical characteristics of LUAD patients for RNA-ISH and RT-qPCR. 101 Table 3. Chemical and genetic perturbation analysis of 627 DEPs. 102 Table 4. Coding potential prediction of PTTG3P was examined by several predicted tools. 103 Table 5. The prediction of PTTG3P-FOXM1 interaction was predicted by lncPro, RPISeq, and RPISeq. 104 Table 6. Oligonucleotide sequence applied in this study (5’ 3’). 105 Table 7. The source of antibodies and chemicals was provided in this study. 107 Table 8. The analysis pipeline of RNA-seq results. 108 | |
dc.language.iso | zh-TW | |
dc.title | 整合系統型分析非編碼核糖核酸基揭露其在肺腺癌增加惡性腫瘤生成之潛在機制 | zh_TW |
dc.title | Integrative analyses of noncoding RNAs disclose potential mechanisms augmenting tumor malignancy in lung adenocarcinoma | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 陳倩瑜,高承福,楊瑞斌,林文昌,賴亮全 | |
dc.subject.keyword | 具預後預測能力的ncRNA網絡,PTTG3P,抗藥性,FOXM1/BUB1B信息傳導,有絲分裂之中期到末期之過程, | zh_TW |
dc.subject.keyword | prognostic ncRNAs networks,PTTG3P,drug resistance,FOXM1/BUB1B signaling axis,metaphase-anaphase transition, | en |
dc.relation.page | 108 | |
dc.identifier.doi | 10.6342/NTU201903353 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-13 | |
dc.contributor.author-college | 生命科學院 | zh_TW |
dc.contributor.author-dept | 基因體與系統生物學學位學程 | zh_TW |
顯示於系所單位: | 基因體與系統生物學學位學程 |
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