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  • 學位論文

以前列腺癌為模式建立疾病相關標記蛋白質之LC-MS分析方法

Profiling novel urinary markers for prostate cancer by 2D HPLC-ESI-MS

指導教授 : 彭旭明
共同指導教授 : 韓肇中

摘要


攝護腺癌在台灣癌症死因中,已爬升至第七名,在美國更是高居第二名!近年來,台灣攝護腺癌發生率及死亡率急速升高,但仍然沒有一種真正有特異性的生物標記來偵測分辨攝護腺癌與攝護腺肥大。攝護腺特異抗原prostate specific antigen (PSA),是一種攝護腺產生的蛋白質,是目前唯一但並非為有效之標記蛋白,臨床現階段利用血液中攝護腺特異抗原的濃度來辦別,若攝護腺有病變則PSA數值會升高。臨床上有許多人因PSA升高,或其他證據疑似罹有前列腺癌,此時是否要作切片,常困擾泌尿科醫師及病人,若切片顯示為陰性,則是否要做第二次或第三次切片,也沒有一定之準則。若能利用非侵入性採集尿液檢體樣本,利用液相層析質譜儀,以前列腺癌為模式建立疾病相關標記蛋白質。利用online 2D system及offline 2D system等方法來相互比較,建立一個完整的前列腺癌的分析方法,進而開發ELISA Kit來定量尿中的蛋白質,此研究將為人類帶來極大的科學價值。 此論文主要利用二維online與offline高效層析串聯電噴灑質譜儀來建立尿液中的蛋白質資訊。實驗過程中,我們改變了流動相濃度梯度、鹽類濃度以及烘箱的溫度等等。在online的裝置裡,我們可以使儀器自動化操作得到大量的數據,但實驗結果所得到的結果,並不理想。雖然在offline的裝置,我們必須使用人力才能完成一系列的分析,雖然耗時費工,但得到的結果和靈敏度卻比online得到的好上許多!

並列摘要


Prostate cancer ranks the seventh cause of cancer-related death in Taiwan and the second in United States. The incidence and mortality rates of prostate cancer have been rising rapidly in the past decades. Currently, there are no accurate and specific biomarkers which could discriminate clinically relevant from clinically benign disease. The better indicators and progression are needed to avoid unnecessary treatment. Clinically, prostate-specific antigen (PSA) screening still seems the only way to diagnose the carcinoma of prostate, but lately researches showed that PSA is not prostate cancer specific. Finding novel biomarkers that are sensitive, specific and can be examined by non-invasive means are imperative. In this thesis, the on-line 2D LC-MS and off-line 2D LC-MS methods are the main approaches to unravel the protein profiling information from urine samples. In order to acquire the moderate experimental results and establish the complete urine sample analysis for identifying prostate cancer, we use these two approaches and tested the effects of modifying several parameters (oven temperature, mobile phase gradient, salt concentration and so on). In online cases, though it can be automatically operated, the information obtained turned out to be unsatisfactory. In offline cases, it demanded much manpower for sample preparation and operating the LC-MS system. The results in offline cases not only show higher intensity and sensitivity than online cases, but provide more useful information.

參考文獻


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