| 摘要: | 大腸直腸癌(colorectal cancer, CRC)是目前世界上致死率排行第三的癌症。目前，美國食品藥物管制局已批准的血液腫瘤標記檢測項目，包含了胚胎癌抗原(carcinoembryonic antigen, CEA)、CA19-9和CA125。然而，這些血液腫瘤標記多半被用於當作大腸直腸癌的預後生物標記，但他在診斷大腸直腸癌的靈敏度較低。因此，我們的研究目的是應用蛋白體學與代謝體學開發新的早期大腸直腸癌診斷臨床生物標記組。我們使用的質譜儀方法包含了nanoLC-MS/MS、UPLC-QTOF-MS、標記LC-MS/MS和穩定同位素標記MRM MS，並且使用機器學習演算法和邏輯式回歸(Logistic regression)分析早期大腸直腸癌患者、晚期大腸直腸癌患者和健康對照組的血漿樣本。我們的研究中，我們發現了356個胜肽、驗證了差異表達的6個胜肽，並且最終在286個血漿樣本(80 HCs與206 CRC)中，以半定量的方式測量三個WGA (Wheat germ agglutinin)結合胜肽。本研究中所發現的新穎生物標記PF454–62、ITIH4429–438與APOE198–207在大腸直腸癌的診斷中靈敏度為84.5%、特異性為97.5%、AUC為0.96。除此之外，我們在非標記性代謝體學的分析中，發現了1147個有差異的化合物，驗證了其中的2個極性化合物與3個非極性化合物，並且最終以非極性化合物的組合C16 dihydroceramide、 6,9,12,15,18,21-tetracosahexenoic acid、1-octadecyl(2Z)-2-butendioate，以隨機森林模型進行訓練與驗證，獲得了靈敏度85.7%、特異性87.0%、AUC為0.96。
Colorectal cancer (CRC) is currently the third leading cause of cancer related mortality in the world. U.S. Food and Drug Administration-approval circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125, were used as prognostic biomarker of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarker for early CRC diagnosis. The proteomic and metabolomic techniques were applied in our study. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, UPLC-QTOF-MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). Based on our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA) captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRC). The novel peptide biomarkers combination of PF454-62, ITIH4429-438, and APOE198-207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In addition, 1147 compounds were identified with untargeted metabolomic analysis, 2 polar compounds and 3 non-polar compounds were verified, and finally a combination of non-polar compounds including C16 dihydroceramide, 6,9,12,15,18,21-tetracosahexenoic acid, and 1-octadecyl(2Z)-2-butendioate to train and validate with random forest classifier and received a sensitivity of 85.7%, a specificity of 87.0%, and an AUC value of 0.956 for early-stage CRC. |
