乳癌進程中胞外體之蛋白質體分析及機制探討

Abstract

癌症已被世界衛生組織報導為第二大死因，且乳癌為在女性中最常見之癌症。正確的診斷及預後對於提高乳癌之存活率為關鍵。近年來，液態活檢(liquid biopsy)為精準醫學奠定了基礎。液態活檢存在於血液等各種體液中，具有多種動態生物分子如核酸及蛋白質並提供了許多有用的資訊。胞外體為液態活檢中常見的目標之一，胞外體為由細胞分泌出大小介於30至100奈米雙層脂質脂囊泡，內含許多運送物質如核酸、蛋白質、脂質及代謝物等。胞外體在腫瘤微環境中扮演重要的角色，包含血管新生、細胞遷移、細胞侵襲及轉移。因此，我們分析乳腺癌外泌體的蛋白質含量，以期找出潛在的生物標記和與癌症相關的機制。 為了要找出潛在的生物標誌，我們從癌症病患、良性腫瘤病患及正常受試者分離出胞外體並以液相層析串聯質譜儀(LC-MS/MS)分析蛋白質體。接著透過主成分分析(principal component analysis, PCA)驗證在差異表現蛋白質組的重要性。另外，差異表現之蛋白質進行了三種建模，分別為線性判別分析（LDA），廣義線性模型（GLM）和支援向量機（SVM）來預測潛在的生物標記。另一方面，對從細胞株分離出之胞外體之蛋白質體分析以進行機制探討。 功能性註解工具(IPA)和基因集富集分析(GSEA)顯示了乳腺癌發展過程中胞外體參與之生物學途徑。為了探討腫瘤胞外體蛋白質之作用及功能，將進行基因編輯及功能測定。在本研究中，我們發現了潛在的乳腺癌檢測生物標誌，並為參與乳腺癌發展的胞外體提供新的見解。

It has been reported that cancer is the second leading cause of death all over the world by world health organization (WHO). Besides, breast cancer is one of the most common cancer among women. In order to improve breast cancer outcomes and survival, an accuracy early detection and prognosis are critical. In recent years, liquid biopsies serve as a solid foundation for precision medicine. Liquid biopsies bear numerous biological molecules like nucleic acid, proteins, which providing valuable information. These molecules contain dynamic biomarkers that exist within a variety of body fluids including blood, which could be applied to detection. One of the most common targets for liquid biopsy is exosome. Exosomes are nanoparticles which commonly defined as vesicles ranging from 30 nm to 100 nm and secreted from cells. It has been well studied that exosomes contain varied cargos, such as nucleic acid, proteins, lipids, and metabolites with lipid bilayer enclosed. Exosomes with the cargos are important role of microenvironment which have influence on metastasis. Exosomes also affect cancer progression such as angiogenesis, cell migration, and invasion. Herein, we intend to analyze the protein content of exosomes from breast cancer to discover potential biomarkers and cancer-related mechanism. In order to discover potential biomarkers, exosomes from breast cancer patients, patients with benign tumor and normal participants were collected and then subjected for proteome analyses by LC-MS/MS. The sets of differentially expressed proteins were then examined by principal component analysis (PCA) to verify their significance. Moreover, three kinds of modeling methods, Linear Discriminant Analysis (LDA), General Linear Model (GLM), and Support Vector Machine (SVM) with forward selection were performed to predict potential biomarkers. On the other hand, exosomes from cell lines were collected and analyzed for the purpose of mechanistic studies. Functional annotation tools such as Ingenuity Pathway Analysis (IPA) and gene set enrichment analysis (GSEA) revealed biological pathways engaged by exosomes during breast cancer development. To explore the role and function of tumor-related exosomes, gene editing and functional assay will be performed. In our study, we aim to discover potential biomarkers for breast cancer detection and provide new insight of exosomal participated in breast cancer development.