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研究生： 陳慶鴻
Chen, Ching Hung
論文名稱： 開發不孕症患者子宮內膜微小核醣核酸特徵與最佳著床時機之檢測平台
Establishment of A Novel Diagnostic Platform for Endometrial miRNA Signature and Optimal Embryo Transfer Timing of Infertile Patients
指導教授： 王慧菁
Wang, Lily Hui-Ching
口試委員: 翁順隆
Weng, Shun-long
李冠林
Lee, Richard
曾大千
Tseng, Ta-Chien
楊博鈞
Yang, Pok Eric
學位類別： 博士
Doctor
系所名稱： 生命科學暨醫學院 - 分子與細胞生物研究所
Institute of Molecular and Cellular Biology
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 120
中文關鍵詞： 不孕症 、重覆著床失敗 、微小核糖核酸 、子宮內膜容受性 、非編碼核糖核酸 、多基因檢測分析晶片
外文關鍵詞： repeated implantation failure, endometrial receptivity, non-coding RNA, multi-gene analysis, PanelChip
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    • 隨著試管嬰兒(IVF)在技術與成功率的提升與進步，仍有許多不孕症患者受到重覆著床失敗（RIF）與胚胎植入失敗。胚胎無法成功著床的因素之一是子宮內膜容受性改變，而內膜容受性由許多基因表現與協調控制，當植入窗口（WOI）移位，子宮內膜的狀態與胚胎植入的時間不同調，將導致胚胎植入失敗。微小核醣核酸(miRNA)是一種非經編碼的核醣核酸，過去研究指出，微小核醣核酸在懷孕著床過程中發揮非常重要的作用，可以調控與子宮內膜容受性相關的基因表現，進而影響胚胎著床。
    針對重覆著床失敗的研究，我們設計了與生殖相關的晶片PanelChip®，透過新穎的多基因擴增分析平台，取得重覆著床失敗與一般不孕症患者子宮內膜的微小核醣核酸表達數據，進行比對分析後，發現3個具有診斷潛力的微小核醣核酸－hsa-miR-718，hsa-miR-155-5p和hsa-20b-5p作為生物標記，進一步進行驗證分析，結果發現這3個標記能夠準確預測重覆植入著床失敗的患者，準確度可以達到90%以上。
    進一步我們希望建立一種以微小核醣核酸為生物標記來確定胚胎移植最佳植入窗口的分類器。為達成此目標，我們透過PanelChip®的技術，由試管嬰兒治療的患者中獲取微小核醣核酸表達圖譜。透過分析，我們找到並選擇其中的143個來開發分類器。結果顯示，我們所開發分類器具有88.5%的準確度，這個分類器通過分析微小核醣核酸的表達模式在準確識別最佳胚胎移植時間方面深具潛力，將有機會可以幫助提高試管嬰兒植入的成功懷孕機率。
    這項研究不僅強調了特定微小核醣核酸在預測 重覆著床失敗方面的診斷潛力，而且還為優化胚胎移植時機提供了一個有前景的工具，這些資訊的應用，將有機會可以幫助提高試管嬰兒植入的成功懷孕機率。

    Despite advancements in in vitro fertilization (IVF), some patients still face repeated implantation failures (RIFs) and challenges with embryo implantation. A key factor contributing to these issues is the lack of endometrial receptivity, regulated by the spatial and temporal coordination of gene expression. Additionally, a displaced window of implantation (WOI), where synchronization between the endometrium and embryo transfer timing is disrupted, complicates successful implantation. Previous research has identified microRNAs (miRNAs) as crucial regulators of gene expression essential for endometrial receptivity, making them important biomarkers in reproductive processes.
    To address RIF, we developed an amplification-based multi-gene analysis platform to detect miRNAs with differential expression in RIF patients. Our analysis identified six miRNAs that may influence genes associated with endometrial receptivity. Among these, hsa-20b-5p, hsa-miR-155-5p, and hsa-miR-718 emerged as promising diagnostic biomarkers, predicting RIF with over 90% accuracy.
    Upon the above finding, we used a specialized PanelChip® for reproductive purposes and analyzed miRNA expression profiles from 200 IVF patients. Of the 167 miRNAs identified, 143 were selected for classifier development. The classifier showed strong performance, with an accuracy of 93.9%, sensitivity of 85.3%, and specificity of 92.4% in the training cohort. Validation in a separate cohort confirmed an accuracy of 88.5%, highlighting its potential to identify the optimal WOI for effective embryo transfer accurately.
    This research highlights the potential of specific miRNAs as diagnostic biomarkers for RIF and presents a promising tool for optimizing embryo transfer timing, thereby improving IVF outcomes.

    著作版權聲明 i 摘要 ii Abstract iii 誌謝 iv Contents v 第一部分 Part One ix 摘要 x Abstract xi List of Tables xii List of Figures xiii 第二部分 Part Two xiv 摘要 xv Abstract xvi List of Tables xvii List of Figures xviii Part One： Chapter I Introduction 1 1.1 Advances in Assisted Reproductive Technologies (ART) 1 1.2 The Role of RIF in Reproductive Medicine 2 1.3 The Role of miRNA in Reproductive Functions 2 1.4 Role of miRNAs in Recurrent Implantation Failure and Potential Biomarker 4 Chapter II Materials and Methods 5 2.1 Subjects 5 2.2 Study Design 5 2.3 RNA Extraction and miRNA Enrichment 6 2.4 Complementary Deoxyribonucleic Acid Synthesis 7 2.5 miRNA Expression Analysis with PanelChip 7 2.6 Data Processing and Analysis 8 Chapter III Results 10 3.1 Identification of Differentially Expressed miRNAs 10 3.2 Determination of RIF Prediction Signature 10 3.3 Validation of RIF Prediction Signature 11 3.4 miRNA-Gene Regulatory Pathway Analysis 11 Chapter IV Discussions 12 Chapter V Conclusion 14 Reference 15 Appendix 27 Supplemental Table 1 27 Supplemental Table 2 48 Supplemental Table 3 49 Supplemental Table 4 50 Supplemental Table 5 51 Supplemental Table 6 52 Supplemental Table 7 53 Part Two： Chapter I Introduction 54 Chapter II Materials and Methods 56 2.1 Endometrial Samples 56 2.2 Ethical Approval 56 2.3 Study Design 57 2.4 RNA Extraction and miRNA Enrichment 57 2.5 cDNA Synthesis 58 2.6 miRNA Expression Analysis 58 2.7 Data Processing and Analysis 59 2.8 Model Building 61 Chapter III Results 63 3.1 Evaluation of miRNA-based Classifier’s performance 63 3.2 Analysis of Inconsistent Results 64 Chapter IV Discussions 65 Chapter V Conclusions 69 Reference 70 Appendix 81 Supplemental Table S1 81 Supplemental Table S2 82 Supplemental Table S3 117 Supplemental Figure S1 118 Supplemental Figure S2 119 Supplemental Figure S3 120 Curriculum Vitae xix

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