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  1. NTU Theses and Dissertations Repository
  2. 醫學院
  3. 醫學檢驗暨生物技術學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95049
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dc.contributor.advisor俞松良zh_TW
dc.contributor.advisorSung-Liang Yuen
dc.contributor.author周宇晢zh_TW
dc.contributor.authorYu-Chih Chouen
dc.date.accessioned2024-08-26T16:26:22Z-
dc.date.available2024-08-27-
dc.date.copyright2024-08-26-
dc.date.issued2024-
dc.date.submitted2024-08-12-
dc.identifier.citation1. 衛生福利部。112年國人死因統計結果。上網日期:2024年7月10日。檢自:https://www.mohw.gov.tw/cp-16-79055-1.html
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4. Van Houcke, S.K. and L.M. Thienpont, "Good samples make good assays" – the problem of sourcing clinical samples for a standardization project. Clin Chem Lab Med, 2013. 51(5): p. 967-72.
5. Bao, Y., et al., Beyond blood: Advancing the frontiers of liquid biopsy in oncology and personalized medicine. Cancer Sci, 2024. 115(4): p. 1060-1072.
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7. Marrugo-Ramirez, J., M. Mir, and J. Samitier, Blood-Based Cancer Biomarkers in Liquid Biopsy: A Promising Non-Invasive Alternative to Tissue Biopsy. Int J Mol Sci, 2018. 19(10).
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9. Batool, S.M., et al., The Liquid Biopsy Consortium: Challenges and opportunities for early cancer detection and monitoring. Cell Rep Med, 2023. 4(10): p. 101198.
10. Ntzifa, A. and E. Lianidou, Pre-analytical conditions and implementation of quality control steps in liquid biopsy analysis. Crit Rev Clin Lab Sci, 2023. 60(8): p. 573-594.
11. Tomasik, B., et al., Current and future applications of liquid biopsy in non-small-cell lung cancer—a narrative review. Translational Lung Cancer Research, 2023. 12(3): p. 594-614.
12. Ding, Z., et al., Proteomics technologies for cancer liquid biopsies. Mol Cancer, 2022. 21(1): p. 53.
13. Charkhchi, P., et al., CA125 and Ovarian Cancer: A Comprehensive Review. Cancers, 2020. 12(12): p. 3730.
14. Zhou, Q., et al., Quantitative proteomics identifies brain acid soluble protein 1 (BASP1) as a prognostic biomarker candidate in pancreatic cancer tissue. EBioMedicine, 2019. 43: p. 282-294.
15. Patel, A.J., et al., A highly predictive autoantibody-based biomarker panel for prognosis in early-stage NSCLC with potential therapeutic implications. Br J Cancer, 2022. 126(2): p. 238-246.
16. Huang, L., et al., Human body-fluid proteome: quantitative profiling and computational prediction. Brief Bioinform, 2021. 22(1): p. 315-333.
17. Indrasena, B.S., Use of thyroglobulin as a tumour marker. World J Biol Chem, 2017. 8(1): p. 81-85.
18. Giovanella, L., et al., Diagnostic, Theranostic and Prognostic Value of Thyroglobulin in Thyroid Cancer. J Clin Med, 2024. 13(9).
19. Li, S., et al., The Role of Thyroglobulin in Preoperative and Postoperative Evaluation of Patients With Differentiated Thyroid Cancer. Front Endocrinol (Lausanne), 2022. 13: p. 872527.
20. Dee, M., et al., B-366 Thyroglobulin Reflex to Manage Thyroglobulin Autoantibody Interference in an Academic Medical Center: A Comparison of Two Immunoassays and an LC-MS/MS Assay in the Presence of Thyroglobulin Autoantibodies. Clinical Chemistry, 2023. 69(Supplement_1).
21. Dekker, B.L., et al., Clinical irrelevance of lower titer thyroglobulin autoantibodies in patients with differentiated thyroid carcinoma. Eur Thyroid J, 2022. 11(6).
22. Barbesino, G., A. Algeciras-Schimnich, and J. Bornhorst, Thyroglobulin Assay Interferences: Clinical Usefulness of Mass-Spectrometry Methods. Journal of the Endocrine Society, 2022. 7(1).
23. Netzel, B.C., et al., Thyroglobulin (Tg) Testing Revisited: Tg Assays, TgAb Assays, and Correlation of Results With Clinical Outcomes. J Clin Endocrinol Metab, 2015. 100(8): p. E1074-83.
24. Bray, F., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024. 74(3): p. 229-263.
25. Molina, J.R., et al., Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc, 2008. 83(5): p. 584-94.
26. Zappa, C. and S.A. Mousa, Non-small cell lung cancer: current treatment and future advances. Transl Lung Cancer Res, 2016. 5(3): p. 288-300.
27. Mountzios, G., et al., Immune-checkpoint inhibition for resectable non-small-cell lung cancer - opportunities and challenges. Nat Rev Clin Oncol, 2023. 20(10): p. 664-677.
28. Reck, M., et al., Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med, 2016. 375(19): p. 1823-1833.
29. Huang, Y., et al., Biomarkers for Immunotherapy in Driver-Gene-Negative Advanced NSCLC. Int J Mol Sci, 2023. 24(19).
30. Ancel, J., et al., Soluble biomarkers to predict clinical outcomes in non-small cell lung cancer treated by immune checkpoints inhibitors. Front Immunol, 2023. 14: p. 1171649.
31. Jardim, D.L., et al., The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer Cell, 2021. 39(2): p. 154-173.
32. Fritzler, M.J., et al., The Utilization of Autoantibodies in Approaches to Precision Health. Front Immunol, 2018. 9: p. 2682.
33. Höppner, J., et al., Comprehensive autoantibody profiles in systemic sclerosis: Clinical cluster analysis. Front Immunol, 2022. 13: p. 1045523.
34. Yang, R., et al., Autoantibodies as biomarkers for breast cancer diagnosis and prognosis. Front Immunol, 2022. 13: p. 1035402.
35. Jernbom Falk, A., et al., Autoantibody profiles associated with clinical features in psychotic disorders. Transl Psychiatry, 2021. 11(1): p. 474.
36. Sexauer, D., E. Gray, and P. Zaenker, Tumour- associated autoantibodies as prognostic cancer biomarkers- a review. Autoimmunity Reviews, 2022. 21(4): p. 103041.
37. Yadav, S., et al., Autoantibodies as diagnostic and prognostic cancer biomarker: Detection techniques and approaches. Biosens Bioelectron, 2019. 139: p. 111315.
38. Tabernero, M.D., L.L. Lv, and K.S. Anderson, Autoantibody profiles as biomarkers of breast cancer. Cancer Biomark, 2010. 6(5-6): p. 247-56.
39. Tan, Q., et al., Autoantibody profiling identifies predictive biomarkers of response to anti-PD1 therapy in cancer patients. Theranostics, 2020. 10: p. 6399 - 6410.
40. Milchram, L., et al., Antibody Profiling and In Silico Functional Analysis of Differentially Reactive Antibody Signatures of Glioblastomas and Meningiomas. Int J Mol Sci, 2023. 24(2).
41. Sumera, A., et al., A Novel Method to Identify Autoantibodies against Putative Target Proteins in Serum from beta-Thalassemia Major: A Pilot Study. Biomedicines, 2020. 8(5): p. 97.
42. Suresh, K., et al., Immune Checkpoint Immunotherapy for Non-Small Cell Lung Cancer: Benefits and Pulmonary Toxicities. Chest, 2018. 154(6): p. 1416-1423.
43. Zhou, K., et al., Mechanisms of drug resistance to immune checkpoint inhibitors in non-small cell lung cancer. Front Immunol, 2023. 14: p. 1127071.
44. Zheng, M., et al., Caspase-6 Is a Key Regulator of Innate Immunity, Inflammasome Activation, and Host Defense. Cell, 2020. 181(3): p. 674-687.e13.
45. Wei, S., M. Feng, and S. Zhang, Molecular Characteristics of Cell Pyroptosis and Its Inhibitors: A Review of Activation, Regulation, and Inhibitors. Int J Mol Sci, 2022. 23(24).
46. Li, Z., et al., Pyroptosis-Related Signature as Potential Biomarkers for Predicting Prognosis and Therapy Response in Colorectal Cancer Patients. Front Genet, 2022. 13: p. 925338.
47. Velez, M.A., T.F. Burns, and L.P. Stabile, The estrogen pathway as a modulator of response to immunotherapy. Immunotherapy, 2019. 11(13): p. 1161-1176.
48. King, R.J., P.K. Singh, and K. Mehla, The cholesterol pathway: impact on immunity and cancer. Trends Immunol, 2022. 43(1): p. 78-92.
49. Zhang, H., et al., Cholesterol Metabolism as a Potential Therapeutic Target and a Prognostic Biomarker for Cancer Immunotherapy. Onco Targets Ther, 2021. 14: p. 3803-3812.
50. Cheng, L., et al., mTOR pathway gene mutations predict response to immune checkpoint inhibitors in multiple cancers. J Transl Med, 2022. 20(1): p. 247.
51. Kitamura, Y., et al., Thyroglobulin immunoassay with a fully automated pretreatment process provides accurate thyroglobulin values in anti-thyroglobulin antibody positive specimens. Clin Biochem, 2023. 118: p. 110598.
52. Passaro, A., et al., Managing Resistance to Immune Checkpoint Inhibitors in Lung Cancer: Treatment and Novel Strategies. J Clin Oncol, 2022. 40(6): p. 598-610.
53. Milchram, L., et al., Antibody Profiling and In Silico Functional Analysis of Differentially Reactive Antibody Signatures of Glioblastomas and Meningiomas. International Journal of Molecular Sciences, 2023. 24(2): p. 1411.
54. Li, Y. and Q. Jiang, Uncoupled pyroptosis and IL-1β secretion downstream of inflammasome signaling. Front Immunol, 2023. 14: p. 1128358.
55. Qi, L., et al., Caspase-6 is a key regulator of cross-talk signal way in PANoptosis in cancer. Immunology, 2023. 169(3): p. 245-259.
56. Shao, W., et al., The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer. Front Cell Dev Biol, 2021. 9: p. 676485.
57. Guo, K., et al., CASP6 predicts poor prognosis in glioma and correlates with tumor immune microenvironment. Front Oncol, 2022. 12: p. 818283.
58. Rodriguez-Lara, V., J.M. Hernandez-Martinez, and O. Arrieta, Influence of estrogen in non-small cell lung cancer and its clinical implications. J Thorac Dis, 2018. 10(1): p. 482-497.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95049-
dc.description.abstract癌症仍然是一種死亡率很高的重要疾病。臨床上檢體採樣及侵入性的考量,使用液態活檢來檢測、診斷和監測癌症有其優勢。液態活檢同時具有非侵入性,使得在所有臨床階段的樣本採集變得更加容易和可能。而蛋白質體學分析被認為在開發生物標記方面有巨大潛力,並能為生物學研究的進展提供了寶貴的見解。因此,結合蛋白質體學分析和液態活檢在癌症研究方面有助於科學家取得重大進展。本研究整合液態活檢和蛋白質體分析,分別在兩個關鍵領域有所發現。(一) 訓練人員使用 LC-MS/MS 檢測甲狀腺球蛋白的方法,在本次研究中顯現了使用LC-MS/MS作為監測甲狀腺癌的方法的前景。(二) 分析自體抗體圖譜,用以開發生物標記並深入生物機制的研究。研究結果顯示,新的LC-MS/MS的方法有改進LC-MS/MS對於樣品分析的精準度,但需要更進一步的驗證。另一方面在自體抗體圖譜分析研究中我們發現雌激素透過 PRKCZ 刺激的信號傳導與半胱天冬酶介導的細胞骨架蛋白裂解這兩個生物路徑與ICIs 的反應存在相關性。同時也觀察到 CASP6 和雌激素相關基因具有作為預後生物標記的潛力。
透過本次的研究,針對LC-MS/MS 檢測甲狀腺球蛋白,我們從臨床檢驗的角度出發完成了對於這項方法學的人員訓練的檢驗。而關於 ICIs 反應的生物標記的開發,我們鑑定出 10 種中心自體抗體,雖然結果顯示這些中心抗體目前無法作為生物標記,但它們有潛力作為未來開發預測模型的依據。
zh_TW
dc.description.abstractCancer continues to significantly impact global health due to the high mortality rates associated with the disease. Adopting liquid biopsy as a method for cancer detection, diagnosis, and monitoring is notably advantageous due to its non-invasive nature, rendering sample collection simpler and less strenuous. The proteomics analysis holds significant potential in discovering biomarkers and contributes valuable insights to the progress of biological research. As a result, the combination of proteomics analysis and liquid biopsy has the potential to make significant strides in addressing cancer-related challenges. This study integrates liquid biopsy and proteomic analysis in two key areas. The first involves personnel training in thyroglobulin measurement using LC-MS/MS, showing promise as a biomarker for monitoring thyroid cancer. The second area pertains to identifying autoantibody profiles for developing biomarkers and in-depth mechanistic research. LC-MS/MS training results show that the modified method is an improvement, but further validation in more samples is needed. In the analysis of the autoantibody profile, the study found that estrogen-stimulated signaling through PRKCZ and caspase-mediated cleavage of cytoskeletal proteins were related to ICIs response. It was also observed that CASP6 and estrogen-related genes hold potential as predictive biomarkers. Additionally, the identification of potential ICIs response biomarkers revealed ten hub autoantibodies. Although these hub antibodies do not currently meet the criteria for biomarkers, they exhibit promise for developing a prediction model.en
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dc.description.tableofcontents口試委員審定書 i
誌謝 ii
摘要 iii
Abstract v
Context vii
圖次 ix
表次 x
I Introduction 1
1. Cancer is still causing the leading death recently 1
2. Liquid biopsy is a promising tool for cancer diagnosis 1
3. Proteomics analysis has great potential for developing biomarkers and provides valuable insights for advancing biological research. 2
4. The example of combinations of liquid biopsy and proteomics analysis 3
A、 Thyroglobulin measurement using LC-MS/MS is a promising method for monitoring thyroid cancer 4
B、 Autoantibody protein array is a potential strategy to impact the immunotherapy outcome in NSCLC 5
5. Specific aims 7
II Materials and Methods 8
1. Materials and Methods for Validating Thyroglobulin Measurement 8
A、 Pre-treatment before LC-MS/MS analysis 8
B、 LC-MS/MS Analysis 9
C、 Modified LC-MS/MS method 10
2. Materials and Methods for autoantibody profile 10
A. Patient cohort selection 10
B. Autoantibody microarray 11
C. Data pre-processing 12
D. Data normalization 13
E. Penetrance and frequency fold change analysis 13
F. STRING and DAVID analysis 15
G. Clustering 15
H. Identification of hub autoantibodies 15
III Result 16
1. Measure thyroglobulin to test its stability. 16
2. Patient cohort selection 17
3. Cohort clinical characteristics 18
4. The study workflow 18
5. Screen autoantibodies by penetrance and frequency fold change 19
6. Elucidate the functional significance of the autoantibody profile by STRING and DAVID 19
7. Identify the hub autoantibodies using STING and Cytoscapes software. 21
IV Discussion 23
V Conclusion and Perspective 30
Tables 31
Figures 42
Reference 54
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dc.language.isoen-
dc.subject液態活檢zh_TW
dc.subject肺癌zh_TW
dc.subject甲狀腺癌zh_TW
dc.subject液相層析質譜儀zh_TW
dc.subject蛋白質微陣列zh_TW
dc.subject蛋白質體學zh_TW
dc.subjectProteomicsen
dc.subjectLiquid biopsyen
dc.subjectLung canceren
dc.subjectThyroid canceren
dc.subjectLC-MS/MSen
dc.subjectProtein microarrayen
dc.title開發基於液態活檢的癌症診斷生物標記:以甲狀腺癌監測和肺癌免疫治療反應為例zh_TW
dc.titleDevelopment of Liquid Biopsy-based Biomarkers for Cancer Diagnosis – Examples of Thyroid Cancer Surveillance and Lung Cancer Immunotherapy Responseen
dc.typeThesis-
dc.date.schoolyear112-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee陳玉如;潘思樺;巫坤品;蘇剛毅zh_TW
dc.contributor.oralexamcommitteeYu-Ju Chen;Szu-Hua Pan;Kun-Pin Wu;Kang-Yi Suen
dc.subject.keyword液態活檢,肺癌,甲狀腺癌,液相層析質譜儀,蛋白質微陣列,蛋白質體學,zh_TW
dc.subject.keywordLiquid biopsy,Lung cancer,Thyroid cancer,LC-MS/MS,Protein microarray,Proteomics,en
dc.relation.page58-
dc.identifier.doi10.6342/NTU202404212-
dc.rights.note未授權-
dc.date.accepted2024-08-12-
dc.contributor.author-college醫學院-
dc.contributor.author-dept醫學檢驗暨生物技術學系-
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