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研究生：	林家榛 Lin, Chia-Chen
論文名稱：	利用蛋白質體學找尋血漿中肺癌生物標誌物 Identification of Potential Plasma Biomarkers for Lung Cancer by Proteomic Approaches
指導教授：	張權發 Chang, Chuan-Fa
學位類別：	碩士 Master
系所名稱：	醫學院 - 醫學檢驗生物技術學系 Department of Medical Laboratory Science and Biotechnology
論文出版年：	2022
畢業學年度：	110
語文別：	英文
論文頁數：	77
中文關鍵詞：	肺癌、生物標誌、蛋白質體學、機器學習方法、富含半胱氨酸分泌蛋白3
外文關鍵詞：	Lung cancer, Biomarker, Proteomic, Machine learning, Cysteine Rich Secretory Protein 3 (CRISP3)
相關次數：	點閱：46 下載：2
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肺癌是全球排行第二高盛行率的癌症，也是癌症致死的主要原因。然而，肺癌多半使用胸部X-光或者顯影技術、病理切片診斷，儘管臨床上已有肺癌相關的生物標記，但發展出有效的肺癌生物標誌物協助肺癌診斷仍至關重要，生物標誌物可用於癌症篩檢或早期檢測，最重要的是在高風險患者篩檢癌症。本研究的目的是通過蛋白質體學及機器學習方法分析肺癌的血漿生物標誌物。首先，使用六重串聯質量標籤 (TMT) 進行同質量異位標記，以對肺癌血漿蛋白質進行相對定量，並通過液相色譜-串聯質譜 (LC-MS/MS) 鑑定差異表達的蛋白質。最後找出富含半胱氨酸分泌蛋白 3 (Cysteine-rich secretory proteins 3)、胰島素樣生長因子結合蛋白 2 (Insulin-Like Growth Factor Binding Protein 2) 在肺癌患者血漿中有上調趨勢以及卡利他汀 (Kallistatin)、Semaphorin 3C (SEMA3C) 在肺癌患者血漿中有下降趨勢。接著，我們使用酵素免疫分析法作確認，發現在肺癌血漿中真實濃度，富含半胱氨酸分泌蛋白 3、胰島素樣生長因子結合蛋白2有上調趨勢以及卡利他汀 (Kallistatin)、Semaphorin 3C (SEMA3C)有下降趨勢。ROC曲線 (receiver operating characteristic curve) 分析中，我們發現富含半胱氨酸分泌蛋白 3 (Cysteine-rich secretory proteins 3)、胰島素樣生長因子結合蛋白 2 (Insulin-Like Growth Factor Binding Protein 2)以及卡利他汀 (Kallistatin)、Semaphorin 3C (SEMA3C)，分辨肺癌病患的表現有良好的靈敏度與特異度。接下來，我們進一步使用支持向量機 (SVM) 機器學習方法來預測早期肺癌的生物標誌蛋白模型，利用富含半胱氨酸分泌蛋白 3、胰島素樣生長因子結合蛋白 2以及卡利他汀特徵建立機器學習模型。結果發現富含半胱氨酸分泌蛋白 3與卡利他汀在驗證模型中，同樣有良好的分辨能力。因此，根據本研究結果，我們認為富含半胱氨酸分泌蛋白 3 (Cysteine-rich secretory proteins 3)及卡利他汀 (Kallistatin) 是最有潛力的肺癌生物標誌蛋白，在未來也許能發展出有效的方法幫助肺癌的篩檢與診斷。

Lung cancer with the second high incidence rate in the world is mostly the main cause of death. Lung cancers are diagnosed by chest X-ray, imaging technology, or Pathology Examination. Although lung cancer-associated biomarkers have been reported, to development of novel and effective biomarkers is essential against cancer. Biomarkers can be applied for cancer screening or early detection which the focus is on detecting cancer in a high-risk patient population. The objective of this study is to identify plasma biomarkers for lung cancer by proteomic and machine learning approaches. We used a 6-plex Tandem Mass Tag (TMT) for isobaric labeling to relative quantification of human plasma proteins and identify differentially expressed proteins by liquid chromatography-tandem mass spectrometry (LC‑MS/MS). Finally, found that Cysteine Rich Secretory Protein 3 (CRISP3), Insulin-Like Growth Factor Binding Protein 2 (IGFBP2) were upregulated in lung cancer, and Kallistatin (KAL)、Semaphorin 3C (SEMA3C) were downregulated in lung cancer. Further verified by ELISA analysis, we found that the level of CRISP3, IGFBP2 was upregulated in lung cancer patients, and the level of KAL, SEMA3C was downregulated in lung cancer patients. ROC curve analysis showed the CRISP3, IGFBP2, KAL, SEMA3C with well diagnosis performance. Moreover, we further used a support vector machine (SVM) machine learning method to predict the cancer biomarker model of early-stage lung cancer. The cancer biomarkers of CRISP3, IGFBP2, and KAL to establish machine learning models based on the cancer biomarker features. The results found that CRISP3 and KAL with validation model obtained good diagnostic performance as well. Taken together, CRISP3 and KAL with better performance in detecting lung cancer can become useful biomarkers and have the potential to establish a useful test for early-stage lung cancer screening.

摘要	I
Abstract	II
Acknowledgement	III
List of tables	VIII
List of figures	IX
Abbreviations	XI
Chapter 1. Introduction	1
1-1	Lung cancer	1
1-2	Cancer biomarkers	3
1-3	Proteomics	5
1-4	Isobaric tags for relative quantitative protein analysis	6
1-5	Machine learning	7
Chapter 2. Objective	8
2-1	Study objective	8
2-2	Specific aim	9
2-3	Experimental flow chart	9
Chapter 3. Materials and Methods	10
3-1	Plasma samples	10
3-2	Depletion of high-abundance proteins in plasma	10
3-3	Sample preparation for proteomic analysis	11
3-4	Labeling Peptides with the Tandem Mass Tag (TMT) Isobaric Mass Tags	12
3-5	Fractionation of Proteolytic Digests	12
3-6	Liquid chromatography - mass spectrometry analysis (LC−MS/MS)	13
3-7	Database search and Statistics analysis	14
3-8	Enzyme-Linked Immunosorbent Assay (ELISA)	15
3-9	Data evaluation and Statistics analysis	15
3-10	Machine Learning Methods	16
Chapter 4. Results	18
4-1	Clinical-pathologic characteristics	18
4-2	Candidate proteins select by the TMT labeling in combination with the liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis	18
4-3	Candidate proteins by the TMT labeling in difference cancer	19
4-4	Candidate biomarkers in lung cancer detection	19
4-5	Combination candidate biomarkers for detection of lung cancer	21
4-6	Combination candidate biomarkers for detection of early-stage lung cancer	22
4-7	Combination candidate biomarkers for evaluation of lung adenocarcinoma	23
4-8	Practical Machine Learning for Data Analysis	24
Chapter 5. Discussion	25
5-1	Lung cancer associated biomarkers in literatures	25
5-2	Candidate biomarker regulated in different cancer	26
5-3	Alterations of CRISP 3 and KAL in plasma of lung cancer patients	26
5-4	The patterns of different candidate proteins can be a useful tool in screen and monitoring diseases	27
5-5	Machine learning applications in cancer biomarkers prediction	28
Chapter 6. Conclusion	29
References	30
Tables	34
Table 1 The characteristics of the cancer-free volunteers and lung cancer	34
Table 2 List of candidate proteins identified by proteomic approaches and correlations with other diseases in literature	35
Table 3 ROC analysis of cancer biomarkers and combined variates	36
Table 4 Machine learning models used for early-stage lung cancer detection based on the proteomic biomarker features	37
Figures	38
Figure 1. The relative quantitation of candidate proteins by TMT Labeling	38
Figure 2. The relative quantitation of candidate proteins by TMT Labeling.	39
Figure 3. The relative quantitation of candidate proteins by TMT Labeling comparing in difference cancer.	40
Figure 4. The statistical results of Cysteine Rich Secretory Protein 3 level in plasma by ELISA.	41
Figure 5. The statistical results of Cysteine Rich Secretory Protein 3 level in plasma by ELISA.	42
Figure 6. The statistical results of Cysteine Rich Secretory Protein 3 level in plasma by ELISA.	43
Figure 7. The statistical results of Cysteine Rich Secretory Protein 3 level of different histological types in plasma by ELISA.	44
Figure 8. The ROC curve and diagnostic performance of Cysteine Rich Secretory Protein 3 level in plasma.	45
Figure 9. The statistical results of Kallistatin level in plasma by ELISA.	46
Figure 10. The statistical results of Kallistatin level in plasma by ELISA.	47
Figure 11. The statistical results of Kallistatin level in plasma by ELISA.	48
Figure 12. The statistical results of Kallistatin level of different histological types in plasma by ELISA.	49
Figure 13. The ROC curve and diagnostic performance of Kallistatin.	50
Figure 14. The statistical results of Semaphorin 3C level in plasma by ELISA.	51
Figure 15. The statistical results of Semaphorin 3C level in plasma by ELISA.	52
Figure 16. The statistical results of Semaphorin 3C level in plasma by ELISA.	53
Figure 18. The ROC curve and diagnostic performance of Semaphorin 3C.	55
Figure 19. The statistical results of Insulin-Like Growth Factor Binding Protein 2 level in plasma by ELISA.	56
Figure 20. The statistical results of Insulin-Like Growth Factor Binding Protein 2 level in plasma by ELISA.	57
Figure 21. The statistical results of Insulin-Like Growth Factor Binding Protein 2 level in plasma by ELISA.	58
Figure 22. The statistical results of Insulin-Like Growth Factor Binding Protein 2 level of different histological types in plasma by ELISA.	59
Figure 23. The ROC curve and diagnostic performance of Insulin-Like Growth Factor Binding Protein 2.	60
Figure 24. The statistical results of candidate proteins level in plasma by ELISA.	61
Figure 25. The statistical results of candidate proteins level in plasma by ELISA.	62
Figure 26. The statistical results of CRISP3/KAL ratio in lung cancer patients’ plasma by ELISA.	63
Figure 27. The statistical results of CRISP3/IGFBP2 ratio in lung cancer patients’ plasma by ELISA.	64
Figure 28. The statistical results of KAL/IGFBP2 ratio in lung cancer patients’ plasma by ELISA.	65
Figure 29. The statistical results of CRISP3/KAL ratio in early-stage lung cancer patients’ plasma by ELISA.	66
Figure 30. The statistical results of CRISP3/IGFBP2 ratio in early-stage lung cancer patients’ plasma by ELISA.	67
Figure 31. The statistical results of KAL/IGFBP2 ratio in early-stage lung cancer patients’ plasma by ELISA.	68
Figure 32. The statistical results of CRISP3/KAL ratio in adenocarcioma patients’ plasma by ELISA.	69
Figure 33. The statistical results of CRISP3/IGFBP2 ratio in adenocarcioma patients’ plasma by ELISA.	70
Figure 34. The statistical results of KAL/IGFBP2 ratio in adenocarcioma patients’ plasma by ELISA.	71
Figure 35. Scattergram based on support vector machine classification of early-stage lung cancer and normal in CRISP3 + KAL training datasets.	72
Figure 36. Confusion matrix based on support vector machine classification of early-stage lung cancer and normal in CRISP3 + KAL test datasets.	73
Figure 37. Scattergram based on support vector machine classification of early-stage lung cancer and normal in CRISP3 + IGFBP2 training datasets.	74
Figure 38. Confusion matrix based on support vector machine classification of early-stage lung cancer and normal in CRISP3 + IGFBP2 test datasets.	75
Figure 39. Scattergram based on support vector machine classification of early-stage lung cancer and normal in KAL + IGFBP2 training datasets.	76
Figure 40. Confusion matrix based on support vector machine classification of early-stage lung cancer and normal in KAL + IGFBP2 test datasets.	77
                                    

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