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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林建達 | zh_TW |
| dc.contributor.advisor | Jian-Da Lin | en |
| dc.contributor.author | 楊辰萱 | zh_TW |
| dc.contributor.author | Chen-Hsuan Yang | en |
| dc.date.accessioned | 2024-03-22T16:39:25Z | - |
| dc.date.available | 2024-03-23 | - |
| dc.date.copyright | 2024-03-22 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-01-12 | - |
| dc.identifier.citation | 1. Ho, J. et al. Translational genomics in pancreatic ductal adenocarcinoma: A review with re-analysis of TCGA dataset. Semin Cancer Biol 55, 70-77 (2019).
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Microbiome and spatially resolved metabolomics analysis reveal the anticancer role of gut Akkermansia muciniphila by crosstalk with intratumoral microbiota and reprogramming tumoral metabolism in mice. Gut Microbes 15, 2166700 (2023). 61. Wo, Y.J. et al. The Roles of CD38 and CD157 in the Solid Tumor Microenvironment and Cancer Immunotherapy. Cells 9 (2019). 62. Dunne, M.R. et al. Characterising the prognostic potential of HLA-DR during colorectal cancer development. Cancer Immunol Immunother 69, 1577-1588 (2020). 63. Maarsingh, J.D., Laniewski, P. & Herbst-Kralovetz, M.M. Immunometabolic and potential tumor-promoting changes in 3D cervical cell models infected with bacterial vaginosis-associated bacteria. Commun Biol 5, 725 (2022). | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92470 | - |
| dc.description.abstract | 胰腺導管腺癌（PDAC）通常在晚期被診斷，而且五年存活率低。我們與國立臺灣大學醫院合作，招募了49位參與者：23位健康捐贈者、24位PDAC患者和2位其他疾病患者。我們建立了血液冷凍保存和細胞恢復的臨床工作流程，並使用光譜流式細胞儀開發了一個用於單一樣本的40種螢光標記的高參數免疫分析模型。我們的數據顯示，與PDAC患者相比，健康個體的周邊血中有較多的naive CD4+和CD8+ T 細胞，而PDAC組的效應CD4、CD8和PD-1表達則較高。我們進一步運用了先進的機器學習模型，包括隨機森林、梯度提升和貝葉斯加法回歸樹（BART）來分類不同組別之間的差異，我們確定了naive CD8免疫細胞群在PDAC和健康組之間最具差異，而CD95表達則成一個關鍵的生物標記。我們還觀察到某些腸道微生物群可以調節免疫表達，這表示介入微生物群的給予並結合免疫療法在胰腺導管腺癌（PDAC）患者的治療中有潛在的效果。在血液中CD95表達和naïve CD8+ T細胞的表達與腸道中的Erysipelotrichales和Akkermansia和Lancefieldella的存在之間發現了正相關。總結來說，腸道微生物群的分析和免疫反應的綜合應用強調了腸道微生物群與系統性免疫指標之間的關聯，我們的研究結果顯示了在癌症的檢測和診斷中廣泛的應用。 | zh_TW |
| dc.description.abstract | Pancreatic ductal adenocarcinoma (PDAC) is frequently identified at advanced stages, contributing to its dismal five-year survival statistics. This investigation, in collaboration with National Taiwan University Hospital, incorporated 49 subjects: 23 healthy controls, 24 PDAC patients, and 2 individuals with distinct pathologies. We optimized protocols for blood cryopreservation and cell recovery, and employed spectral flow cytometry for high-parameter immune profiling, simultaneously analyzing 40 fluorescence markers per sample. Our data indicate elevated naive CD4+ and CD8+ T-cells presents in the peripheral blood of healthy participants relative to PDAC patients. Conversely, PDAC patients exhibited increased levels of effector CD4+ and CD8+ T cells and PD-1 expression. We further employed advanced machine learning models, including Random Forest, Gradient Boosting, and Bayesian Additive Regression Trees (BART), to classify the differences across cohorts and identified the distinct features in naive CD8+ T-cell populations between PDAC and healthy cohorts, with CD95 expression emerging as a pivotal biomarker. We also noted that certain gut microbiota compositions modulate immune expression, suggesting the potential efficacy of combined immunotherapeutic and microbiome-targeted interventions in PDAC treatment. A positive association was identified between CD95 expression and naïve CD8+ T cell occurrence in the blood and the presence of Erysipelotrichales, Akkermansia, and Lancefieldella in the gut. Overall, the integrated application of microbial community analyses and immunological assessments emphasizes the connection between gut microbiota and systemic immunological indicators, suggesting the utility of a comprehensive omics strategy in the detection and diagnosis of cancer. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-22T16:39:25Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-03-22T16:39:25Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES ix LIST OF TABLES x Chapter 1 Introduction 1 1.1 Formation of pancreatic ductal adenocarcinoma (PDAC) 1 1.2 The progression of pancreatic ductal adenocarcinoma (PDAC) 1 1.3 The Tumor Microenvironment of PDAC 3 1.4 Expression of immune cells in the tumor microenvironment 4 1.5 Expression of immune cells in the peripheral blood 5 1.6 The relationship between the microbiome and immune cells: implications for future immunotherapy applications 6 Chapter 2 Methods 8 2.1 Sample procurement 8 2.2 Selection of the Health Group 8 2.3 Blood collection and preserve 9 2.4 Immunofluorescence staining 9 2.5 Flow cytometry data preprocessing 13 2.6 Flow cytometry data analysis 13 2.7 Treatment of batch effect 14 2.8 Human Th cytokine sample preparation 14 2.9 Human Th cytokine standard preparation 14 2.10 Human Th cytokine processing 15 2.11 Human Th cytokine data analysis 16 2.12 Data analysis 16 Chapter 3 Results 17 3.1 Preliminary Analysis Using a 33-fluorescence Panel on 6 Healthy Participants and 7 PDAC Patients via FlowSOM. 17 3.1.1 Differences in FlowSOM Metaclusters between Cluster 3 and Cluster 7 17 3.1.2 Differences in FlowSOM Metaclusters between Cluster 9 18 3.1.3 Differences in FlowSOM Metaclusters between Cluster 14 18 3.1.4 Differences in FlowSOM Metaclusters between Cluster 16 19 3.2 Analysis of Immune Cell Expression in Peripheral Blood of 6 Healthy Participants and 7 PDAC Patients through immune population. 19 3.2.1 Differences in immune population between CD8 T cells 19 3.2.2 Differences in immune population between Natural Killer T (NKT-Like) cells 20 3.2.3 Differences in immune population between Dendritic cells 20 3.2.4 Differences in immune population between B cells 21 3.3 Identification of a Cohort of 24 Similar Individuals from 101 Healthy Specimens for the Health Group 21 3.4 Analysis of 23 healthy individuals and 24 PDAC patients using a 40-fluorescence panel 22 3.4.1 Differences in FlowSOM Metaclusters between Cluster 1 and Cluster 8 22 3.4.2 Differences in FlowSOM Metaclusters between Cluster 9 and Cluster 12 23 3.4.3 Differences in FlowSOM Metaclusters between Cluster 14 24 3.4.4 Differences in FlowSOM Metaclusters between Cluster 17 25 3.4.5 Differences in immune population between CD4 T cells 25 3.4.6 Differences in immune population between CD8 T cells 27 3.4.7 Differences in immune population between Monocytes 28 3.4.8 Differences in immune population between B cells 29 3.4.9 Differences in immune population between NK cells 30 3.4.10 Differences in immune population between Dendritic cells 30 3.4.11 Differences in immune population between Innate Lymphoid Cells (ILCs) 31 3.4.12 Difference in immune population between Basophils 31 3.5 Implications of cytokine expression differences between Health and PDAC groups 32 3.6 Utilizing 40 fluorescence markers to differentiate Health and PDAC groups 34 3.6.1 CD95 as the Top-ranked marker for distinguishing between healthy individuals and PDAC patients 34 3.6.2 CD1c ranked second as a marker differentiating healthy and PDAC groups 35 3.6.3 The Significance of T Cell Markers in Healthy and PDAC Groups 35 3.6.4 CD159c as a marker significantly distinguishing between Health and PDAC groups in fluorescence expression levels 36 3.6.5 Clustering results of Health and PDAC using 40 fluorescence markers 37 3.7 Utilizing immune populaton to differentiate Health and PDAC groups 37 3.8 Investigating the Impact of Gut Microbiota on Immune Responses 38 Chapter 4 Discussion and Conclusion 63 4.1 Differences between the 33-fluorescence panel and the 40-fluorescence panel 63 4.2 Analyzing the differences in immune populations between Healthy and PDAC groups 64 4.3 Analysis of prediction results using 33 and 40 fluorescence panel 65 4.4 Discussion and analysis of data 67 4.5 Future work 68 Appendix 69 References 72 | - |
| dc.language.iso | en | - |
| dc.subject | 胰腺島管腺癌 | zh_TW |
| dc.subject | 血液 | zh_TW |
| dc.subject | 腸道微生物群 | zh_TW |
| dc.subject | 腫瘤微環境 | zh_TW |
| dc.subject | 流式細胞儀 | zh_TW |
| dc.subject | 第II型免疫反應 | zh_TW |
| dc.subject | Pancreatic Ductal Adenocarcinomas | en |
| dc.subject | Type II Immunity | en |
| dc.subject | Gut microbiota | en |
| dc.subject | Flow cytometry | en |
| dc.subject | Tumor microenvironment | en |
| dc.subject | Blood | en |
| dc.title | 高參數免疫分析血液檢測為人類胰臟癌提供早期的診斷策略 | zh_TW |
| dc.title | High-parameter immune profiling in the blood provides a novel strategy for the early diagnosis of human pancreatic ductal cancer | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 廖偉智;吳偉愷;顧正崙 | zh_TW |
| dc.contributor.oralexamcommittee | Wei-Chih Liao;Wei-Kai Wu;Cheng-Lung Ku | en |
| dc.subject.keyword | 胰腺島管腺癌,血液,第II型免疫反應,流式細胞儀,腫瘤微環境,腸道微生物群, | zh_TW |
| dc.subject.keyword | Pancreatic Ductal Adenocarcinomas,Blood,Type II Immunity,Flow cytometry,Tumor microenvironment,Gut microbiota, | en |
| dc.relation.page | 76 | - |
| dc.identifier.doi | 10.6342/NTU202400004 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-01-15 | - |
| dc.contributor.author-college | 生命科學院 | - |
| dc.contributor.author-dept | 生化科技學系 | - |
| dc.date.embargo-lift | 2029-01-09 | - |
| 顯示於系所單位： | 生化科技學系 | |
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