系統識別號 U0026-2108201420383200
論文名稱(中文) 利用定向質譜法認證及驗證肺腺癌之血液生物標記
論文名稱(英文) Qualification and verification of serological biomarker candidates for lung adenocarcinoma by targeted mass spectrometry
校院名稱 成功大學
系所名稱(中) 環境醫學研究所
系所名稱(英) Institute of Environmental and Occupational Health
學年度 102
學期 2
出版年 103
研究生(中文) 張孝任
研究生(英文) Hsiao-Jen Chang
學號 s76014065
學位類別 碩士
語文別 英文
論文頁數 69頁
口試委員 指導教授-廖寶琦
中文關鍵字 肺腺癌  定向質譜法  多重反應監測  認證  驗證  血液生物標記 
英文關鍵字 lung adenocarcinoma  targeted mass spectrometry  multiple reaction monitoring  qualification  verification  serological biomarkers 
中文摘要 肺癌名列世界癌症十大死因之首,其主要原因為缺乏有用的早期診斷工具及有效的晚期治療方法。尋找兼具敏感度及特異度之早期診斷肺癌的生物標記,已成為一項重要的課題,於肺癌防治層面提供了極大的效益。時至今日,已有許多以 「發現」為基礎之生物標記研究完成,大量的蛋白質被報導與癌症的進程相關,且具有成為癌症生物標記的潛力。然而,這些生物標記必須審慎的在血液中進行診斷癌症能力或預後癌症能力之確效,如此才能實際應用於臨床方面。定向質譜法為近年興起的蛋白質體分析分法,其主體為多重反應監測,它可以在一次的液相層析串聯質譜分析當中,測量樣本中數十上百之蛋白質,儼然成為現今生物標記確效的首要、可行之方法。於此研究中,將使用多重反應監測為基礎之定向質譜分析法,於血液中評估蛋白質候選者是否具診斷肺癌之能力。共有102個候選蛋白質被選入研究中,來源為實驗室的前段研究,以及相關文獻搜尋,並以420個胜肽及1249個胜肽-碎片離子對,於多重反應監測中來代表此102蛋白質,且於混和樣本實測後,偵測到78個候選蛋白質;亦在60個 (三期程與配對健康者) 個別樣本中,對78個蛋白質進行相對定量。分析定量結果,共得到9個具有統計差異之候選蛋白質。此外,將不同期程之5、3、2個候選蛋白質進行組合,發現組合生物標記對於肺癌病患之檢測敏感度增加,且AUC趨近於1.00。綜觀本研究之結果,於102個蛋白質中,驗證出較具潛力的蛋白質候選者、蛋白質組合,可繼續進行大量樣本之確效工作,以探討其於臨床應用或發展治療藥物之價值。
英文摘要 Lung cancer is the leading cause of cancer mortality worldwide. The main reasons of the high mortality of lung cancer are absence of the useful tools to diagnose lung cancer at early stage and lack of effective treatments for metastatic cancer. To find biomarkers that are sensitive and specific to the early-stage lung cancer become an important issue, which provides huge benefit for lung cancer prevention. Nowadays, there is plenty of biomarker discovery-based research finished, many proteins have been reported that they were associated with the cancer progression, and have the opportunities to sever as cancer markers. However, for the application concern, the diagnosis/prognosis performances of the protein candidates need to be validated in the blood. The recent emergence of targeted mass spectrometry based proteomic technology, (MRM) analysis, is able to test tens or even hundreds of protein candidates in one LC-MS/MS run, and already became a principal enabling method for biomarker validation. In this study, we attempted to conduct a MRM-based targeted analysis to evaluate the potential utility of a list of protein candidates for lung cancer diagnosis in the blood samples. A total of 1249 transitions of 420 peptides representing 102 protein candidates from our previous study and literature were first screened by MRM analysis in the polled plasma samples, remaining 78 proteins in the list. Relative quantification of these 78 proteins was further performed in individual plasma sample from patients in 3 stages and 30 paired healthy donors. Ultimately, 9 proteins were found to be able to distinguished patients from controls. Further combination of 5, 3, and 2 candidate marker proteins has improved sensitivity in discrepancy power as well as a merged AUC value of nearly 1.00 in stage I, II, III groups versus controls, respectively. Our result has highlighted several possible markers for lung adenocarcinoma and the proposed protein panels append further validation in a larger cohort for evaluating their potential use in clinical applications or development of therapeutics.
論文目次 摘要 II
Abstract III
致謝 IV
Contents V
Table list VI
Figure list VI
Abbreviations VII
1 Research background 1
1-1 Lung cancer 1
1-2 Cancer secretome 2
1-3 Blood-based proteomics 3
1-4 Approaches for detecting blood proteins 3
1-5 Targeted mass spectrometry in biomarker validation 4
2 Objectives 5
3 Materials and methods 6
3-1 Patients and Specimens 6
3-2 Preparation of Plasma for Mass Spectrometry 6
3-2-1 Pooling of plasma samples 6
3-2-2 Plasma depletion of albumin and IgG 6
3-2-3 In-solution digestion 7
3-3 Multiple reaction monitoring using triple quadrupole mass spectrometer 7
3-4 MRM data analysis 8
3-5 Statistical analysis 9
4 Results 10
4-1 Strategy for lung adenocarcinoma biomarker candidate verification by MRM 10
4-2 Detection of selected peptides and transitions in pooled samples by MRM analysis 11
4-3 Label-free analysis of 78 proteins in 60 plasma samples 15
4-4 Selection of biomarker candidates for diagnosing different stage of lung adenocarcinoma 17
5 Discussion 24
6 Conclusion 27
7 References 28
8 Appendix 33

Table list
Table 1. Stage distribution of non-small cell lung cancer in Taiwan 2
Table 2. The number of selected/detected surrogate peptides per protein candidate 12
Table 3. Overview of human subject data sets 15
Table 4. Groups of proteins for differential diagnosis of lung adenocarcinoma 20
Table 5. Blood concentrations of six differentially expressed proteins 26

Figure list
Figure 1. The distribution of blood proteome 3
Figure 2. Stepwise workflow to verify biomarkers for differential diagnosis of lung adenocarcinoma by MRM. 11
Figure 3. MRM traces of high, middle, and low abundance proteins 13
Figure 4. Profiling of the abundance of 78 proteins in pooled plasma 14
Figure 5. Normalization of extracted peak area of hemopexin peptide using the internal standard peptides 16
Figure 6. Interactive plots and ROC curves of 9 differentially expressed proteins 19
Figure 7. Venn diagram of the 9 differentially expressed protein candidates 21
Figure 8. Diagnostic performance of the 5-, 3-, 2-marker panel 23
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