臺灣博碩士論文加值系統

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.

Abstract i
摘要 ii
Contents iii
List of Figures v
List of Tables vi
1.Introduction 1
1.1 Ovarian cancer 1
1.2 miRNA in ovarian cancer 2
1.3 RNA-seq in ovarian cancer 4
1.4 Next generation sequencing technology: miRNA-seq and RNA-seq 4
2.Related Works 6
2.1 Development of a serum miRNA neural network 6
2.2 A combination of circulating miRNAs for the early detection of ovarian cancer 7
3.Data set 8
3.1 Sample collection 8
3.2 miRNA dataset 8
3.3 RNA-seq 8
4.Methods 10
4.1 miRNA data analysis 10
4.2 RNA-seq data analysis 10
4.3 Selection of candidate miRNAs for prediction model construction 11
4.4 Prediction models 12
4.5 Evaluation 12
4.6 mRNA and miRNA interaction 14
5.Results 15
5.1 Exosomal miRNA profiles in serum 15
5.2 mRNA profiles in tumors 24
5.3 miRNA & mRNA interaction 31
6.Conclusion 33
Acknowledgment 34
Reference 34
Supplemental Data 36

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.