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題名 液態生物檢體應用在卵巢癌分類與篩檢
An application of serum exosomes as biomarkers in differentiating histological subtypes of ovarian cancer作者 藍逵原
Lan, Kuei-Yuan貢獻者 張家銘
Chang, Jia-Ming
藍逵原
Lan, Kuei-Yuan關鍵詞 卵巢癌
小分子核糖核酸
核糖核酸測序
邏輯回歸分析
機器學習
Ovarian cancer
MiRNA
RNA-seq
Logistic regression
Machine learning日期 2018 上傳時間 1-十月-2018 12:11:26 (UTC+8) 摘要 卵巢癌是女性第八常見癌症，並且在婦科癌症中是致死率最高的一種。我的研究期望能找到卵巢癌相關的生物標記，幫助癌症能在早期確診。假設不同的卵巢癌形態會分泌不同的小分子核糖核酸 (miRNAs) 進而影響週遭細胞的表現導致癌化，那藉由比較這些在微環境中的小分子核糖核酸能夠幫助我們判斷病人是否得到卵巢癌。在小分子核糖核酸的研究中，我們使用了45位病人的樣本，其中29位是帶有不同亞型的癌症，另外16位是控制組。在我們所觀察到的2496個小分子核糖核酸中，263個在癌症病人與控制組間表現上有顯著的差異，再藉由機器學習的方式，我們建立了一個可靠的邏輯回歸模型來分辨病人是否得到卵巢癌，以及卵巢癌的那一種亞型。此外，針對具有較強抗藥性的亞型的病人，也對其基因的表現在正常細胞與癌細胞的不同進行研究。研究共有十位癌症病人，以其中六位病人的正常細胞當作控制組，得到有755個基因在兩組之間表現上有顯著的差異。最後，我們發現了許多過去不曾發現的小分子核糖核酸與基因之間的關係，未來可能用做標靶治療的目標。
Ovarian cancer is the eighth common cancer in women, and the most deadly gynecologic malignancy. My master project aims to identify candidates of biomarkers, which may be used in early detection of ovarian cancer. We hypothesize that different subtypes of ovarian cancer may secret exosomes carrying different miRNAs play different roles in cell-cell communication in microenvironment. Therefore, we aim to compare the expression profiles of exosomal miRNA in the serum from patients with or without ovarian cancer. Furthermore, we performed RNA-seq for mRNA profiles in the cancer tissue of a most drug-resistant subtype with their paired normal tissue from the same patients. A total of 45 patients were enrolled in this study. Sera from all 45 patients were used in the study of exosomal miRNA, in which 29 samples are cancer patients and the other 16 are non-cancer controls. RNA-seq data was generated from ten patients who had clear-cell ovarian cancer subtypes, six of them have corresponding paired normal tissue. In miRNA, 2496 miRNAs were identified and 263 miRNAs are differentially expressed between normal samples and cancer samples. We construct a reliable machine learning model to classify patient cancer subtypes base on the candidate miRNAs selected by the model. 755 RNAs are differentially expressed between normal samples and cancer samples. Lastly, we found couple unknown predicted miRNA and mRNA interaction, which may further the candidate of targeted therapy in the future.參考文獻 1. Weng,S.-L. et al. (2017) Genome-wide discovery of viral microRNAs based on phylogenetic analysis and structural evolution of various human papillomavirus subtypes. Brief Bioinform.2. Wang,Z. et al. (2009) RNA-Seq: a revolutionary tool for transcriptomics.Nat Rev Genet, 10, nrg2484.3. Elias,K. et al. (2017) Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer. Elife, 6, e28932.4. Yokoi,A. et al. (2014) A combination of circulating miRNAs for the early detection of ovarian cancer. Oncotarget, 5, 89811–89823.5. Katz,B. et al. (2015) MicroRNAs in Ovarian Cancer. Hum Pathol, 46, 1245–1256.6. Schwarzenbach,H. et al. (2014) Clinical relevance of circulating cell-free microRNAs in cancer. Nat Rev Clin Oncol, 11, nrclinonc.2014.5.7. Shahab,S. et al. (2012) The effects of MicroRNA transfections on global patterns of gene expression in ovarian cancer cells are functionally coordinated. Bmc Med Genomics, 5, 1–16.8. Love,M. et al. (2015) RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000research, 4, 1070.9. Translational Advances in Gynecologic Cancers. 1st Edition. (2017)Anticancer Res, 37, 5907.10. Karnezis,A. et al. (2016) The disparate origins of ovarian cancers: pathogenesis and prevention strategies. Nat Rev Cancer, 17, 65–74.11. Wang,J. et al. (2017) Circulating exosomal miR-125a-3p as a novel biomarker for early-stage colon cancer. Sci Reports, 7, 4150.12. Wu,C.-Y. et al. (2016) Exosomes and breast cancer: a comprehensive review of novel therapeutic strategies from diagnosis to treatment. Adv Exp Med Biol, 24, 6–12.13. Martinez-Garcia,E. et al. (2016) Development of a sequential workflow based on LC-PRM for the verification of endometrial cancer protein biomarkers in uterine aspirate samples. Oncotarget, 7, 53102–53115.14. Andrés-León,E. et al. (2016) miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis. Sci Reports, 6, 25749.15. Hauser,A. et al. (2017) Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov, 16, 829.16. Martinez-Garcia,E. et al. (2017) Targeted Proteomics Identifies Proteomic Signatures in Liquid Biopsies of the Endometrium to Diagnose Endometrial Cancer and Assist in the Prediction of the Optimal Surgical Treatment. Clin Cancer Res, 23, 6458–6467.17. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Soft, 28 (5), 1-26.18. Liu,T. et al. (2015) Verifying the markers of ovarian cancer using RNA-seq data. Mol Med Rep, 12, 1125–30.19. Raposo,G. and Stoorvogel,W. (2013) Extracellular vesicles: exosomes, microvesicles, and friends. J. Cell Biol., 200, 373–83.20. Anders,S. et al. (2010) Differential expression analysis for sequence count data. Nat Précéd.21. Yokoi,A. et al. (2017) Malignant extracellular vesicles carrying MMP1 mRNA facilitate peritoneal dissemination in ovarian cancer. Nature Communications, 8, 14470.22. Duan,H. et al. (2017) TET1 inhibits EMT of ovarian cancer cells through activating Wnt/β-catenin signaling inhibitors DKK1 and SFRP2. Gynecologic Oncology, 147, 408–417. 描述 碩士
國立政治大學
資訊科學系
105753035資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105753035 資料類型 thesis dc.contributor.advisor 張家銘 zh_TW dc.contributor.advisor Chang, Jia-Ming en_US dc.contributor.author (作者) 藍逵原 zh_TW dc.contributor.author (作者) Lan, Kuei-Yuan en_US dc.creator (作者) 藍逵原 zh_TW dc.creator (作者) Lan, Kuei-Yuan en_US dc.date (日期) 2018 en_US dc.date.accessioned 1-十月-2018 12:11:26 (UTC+8) - dc.date.available 1-十月-2018 12:11:26 (UTC+8) - dc.date.issued (上傳時間) 1-十月-2018 12:11:26 (UTC+8) - dc.identifier (其他 識別碼) G0105753035 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/120262 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 105753035 zh_TW dc.description.abstract (摘要) 卵巢癌是女性第八常見癌症，並且在婦科癌症中是致死率最高的一種。我的研究期望能找到卵巢癌相關的生物標記，幫助癌症能在早期確診。假設不同的卵巢癌形態會分泌不同的小分子核糖核酸 (miRNAs) 進而影響週遭細胞的表現導致癌化，那藉由比較這些在微環境中的小分子核糖核酸能夠幫助我們判斷病人是否得到卵巢癌。在小分子核糖核酸的研究中，我們使用了45位病人的樣本，其中29位是帶有不同亞型的癌症，另外16位是控制組。在我們所觀察到的2496個小分子核糖核酸中，263個在癌症病人與控制組間表現上有顯著的差異，再藉由機器學習的方式，我們建立了一個可靠的邏輯回歸模型來分辨病人是否得到卵巢癌，以及卵巢癌的那一種亞型。此外，針對具有較強抗藥性的亞型的病人，也對其基因的表現在正常細胞與癌細胞的不同進行研究。研究共有十位癌症病人，以其中六位病人的正常細胞當作控制組，得到有755個基因在兩組之間表現上有顯著的差異。最後，我們發現了許多過去不曾發現的小分子核糖核酸與基因之間的關係，未來可能用做標靶治療的目標。 zh_TW dc.description.abstract (摘要) Ovarian cancer is the eighth common cancer in women, and the most deadly gynecologic malignancy. My master project aims to identify candidates of biomarkers, which may be used in early detection of ovarian cancer. We hypothesize that different subtypes of ovarian cancer may secret exosomes carrying different miRNAs play different roles in cell-cell communication in microenvironment. Therefore, we aim to compare the expression profiles of exosomal miRNA in the serum from patients with or without ovarian cancer. Furthermore, we performed RNA-seq for mRNA profiles in the cancer tissue of a most drug-resistant subtype with their paired normal tissue from the same patients. A total of 45 patients were enrolled in this study. Sera from all 45 patients were used in the study of exosomal miRNA, in which 29 samples are cancer patients and the other 16 are non-cancer controls. RNA-seq data was generated from ten patients who had clear-cell ovarian cancer subtypes, six of them have corresponding paired normal tissue. In miRNA, 2496 miRNAs were identified and 263 miRNAs are differentially expressed between normal samples and cancer samples. We construct a reliable machine learning model to classify patient cancer subtypes base on the candidate miRNAs selected by the model. 755 RNAs are differentially expressed between normal samples and cancer samples. Lastly, we found couple unknown predicted miRNA and mRNA interaction, which may further the candidate of targeted therapy in the future. en_US dc.description.tableofcontents Abstract i摘要 iiContents iiiList of Figures vList of Tables vi1.Introduction 11.1 Ovarian cancer 11.2 miRNA in ovarian cancer 21.3 RNA-seq in ovarian cancer 41.4 Next generation sequencing technology: miRNA-seq and RNA-seq 42.Related Works 62.1 Development of a serum miRNA neural network 62.2 A combination of circulating miRNAs for the early detection of ovarian cancer 73.Data set 83.1 Sample collection 83.2 miRNA dataset 83.3 RNA-seq 84.Methods 104.1 miRNA data analysis 104.2 RNA-seq data analysis 104.3 Selection of candidate miRNAs for prediction model construction 114.4 Prediction models 124.5 Evaluation 124.6 mRNA and miRNA interaction 145.Results 155.1 Exosomal miRNA profiles in serum 155.2 mRNA profiles in tumors 245.3 miRNA & mRNA interaction 316.Conclusion 33Acknowledgment 34Reference 34Supplemental Data 36 zh_TW dc.format.extent 2360860 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105753035 en_US dc.subject (關鍵詞) 卵巢癌 zh_TW dc.subject (關鍵詞) 小分子核糖核酸 zh_TW dc.subject (關鍵詞) 核糖核酸測序 zh_TW dc.subject (關鍵詞) 邏輯回歸分析 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Ovarian cancer en_US dc.subject (關鍵詞) MiRNA en_US dc.subject (關鍵詞) RNA-seq en_US dc.subject (關鍵詞) Logistic regression en_US dc.subject (關鍵詞) Machine learning en_US dc.title (題名) 液態生物檢體應用在卵巢癌分類與篩檢 zh_TW dc.title (題名) An application of serum exosomes as biomarkers in differentiating histological subtypes of ovarian cancer en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Weng,S.-L. et al. (2017) Genome-wide discovery of viral microRNAs based on phylogenetic analysis and structural evolution of various human papillomavirus subtypes. Brief Bioinform.2. Wang,Z. et al. (2009) RNA-Seq: a revolutionary tool for transcriptomics.Nat Rev Genet, 10, nrg2484.3. Elias,K. et al. (2017) Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer. Elife, 6, e28932.4. Yokoi,A. et al. (2014) A combination of circulating miRNAs for the early detection of ovarian cancer. Oncotarget, 5, 89811–89823.5. Katz,B. et al. (2015) MicroRNAs in Ovarian Cancer. Hum Pathol, 46, 1245–1256.6. Schwarzenbach,H. et al. (2014) Clinical relevance of circulating cell-free microRNAs in cancer. Nat Rev Clin Oncol, 11, nrclinonc.2014.5.7. Shahab,S. et al. (2012) The effects of MicroRNA transfections on global patterns of gene expression in ovarian cancer cells are functionally coordinated. Bmc Med Genomics, 5, 1–16.8. Love,M. et al. (2015) RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000research, 4, 1070.9. Translational Advances in Gynecologic Cancers. 1st Edition. (2017)Anticancer Res, 37, 5907.10. Karnezis,A. et al. (2016) The disparate origins of ovarian cancers: pathogenesis and prevention strategies. Nat Rev Cancer, 17, 65–74.11. Wang,J. et al. (2017) Circulating exosomal miR-125a-3p as a novel biomarker for early-stage colon cancer. Sci Reports, 7, 4150.12. Wu,C.-Y. et al. (2016) Exosomes and breast cancer: a comprehensive review of novel therapeutic strategies from diagnosis to treatment. Adv Exp Med Biol, 24, 6–12.13. Martinez-Garcia,E. et al. (2016) Development of a sequential workflow based on LC-PRM for the verification of endometrial cancer protein biomarkers in uterine aspirate samples. Oncotarget, 7, 53102–53115.14. Andrés-León,E. et al. (2016) miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis. Sci Reports, 6, 25749.15. Hauser,A. et al. (2017) Trends in GPCR drug discovery: new agents, targets and indications. Nat Rev Drug Discov, 16, 829.16. Martinez-Garcia,E. et al. (2017) Targeted Proteomics Identifies Proteomic Signatures in Liquid Biopsies of the Endometrium to Diagnose Endometrial Cancer and Assist in the Prediction of the Optimal Surgical Treatment. Clin Cancer Res, 23, 6458–6467.17. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Soft, 28 (5), 1-26.18. Liu,T. et al. (2015) Verifying the markers of ovarian cancer using RNA-seq data. Mol Med Rep, 12, 1125–30.19. Raposo,G. and Stoorvogel,W. (2013) Extracellular vesicles: exosomes, microvesicles, and friends. J. Cell Biol., 200, 373–83.20. Anders,S. et al. (2010) Differential expression analysis for sequence count data. Nat Précéd.21. Yokoi,A. et al. (2017) Malignant extracellular vesicles carrying MMP1 mRNA facilitate peritoneal dissemination in ovarian cancer. Nature Communications, 8, 14470.22. Duan,H. et al. (2017) TET1 inhibits EMT of ovarian cancer cells through activating Wnt/β-catenin signaling inhibitors DKK1 and SFRP2. Gynecologic Oncology, 147, 408–417. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.CS.015.2018.B02 en_US
