探討選擇性剪切作為癌症生物標記之可能性

Abstract

選擇性剪切在多種癌症的發展上都扮演很重要的角色。此一調控機制參與幾乎所有腫瘤標誌性特徵的形成，例如癌細胞的無限制生長、對細胞凋亡的抵抗、血管新生、癌細胞轉移和規避免疫機制。儘管選擇性剪切非常重要，在癌症的轉錄體選擇性剪切相關研究仍然偏少。在本論文中，我將探索使用選擇性剪切的模式來做為區分正常及腫瘤組織以及預測癌症病患預後的可能性，我的結果顯示：一、選擇性剪切的表現量模式在區別腫瘤和正常組織的準確度優於基因的表現量模式，在某些癌症中外顯子的表現量模式也能夠達到相當高的區別準確率，除此之外，基於多個轉錄子或外顯子的表現量模式所組成的預測模型在分別正常和腫瘤組織上的準確度要高於單一轉錄子或外顯子；二、選擇性剪切的轉錄子組成的調控訊息傳導網絡模組僅有部分與基因組成的訊息傳導網絡模組相同，並且部分轉錄子模組在腫瘤組織與正常組織間有不同的共表現量模式；三、初步結果顯示選擇性剪切的模式可以用於區別預後較佳與較差的癌症病人。此外，運用多轉錄子表現量模式在預後的預測能力會較優於單一轉錄子，這些結果表示選擇性剪切具有發展為癌症診斷以及癌症治療生物標記之潛力。

Alternative splicing has been shown to play important roles in the development of multiple cancer types. This regulatory mechanism participates in virtually almost all of the hallmarks of cancer, including unlimited growth, anti-apoptosis, angiogenesis, metastasis, and immune evasion. Despite the importance of alternative splicing, transcriptome-wide splicing analyses have remained underrepresented in cancer researches. In this thesis, I explore the possibility of using splicing patterns as a biomarker for normal-tumor tissue differentiation and prognosis. My results indicate that (1) Splicing and exonic expression patterns could be used to differentiate tumor from normal tissues with high accuracies. Furthermore, classification models based on the combined expressions of multiple transcripts or exonic regions perform better than those based on expressions of single transcript/exonic region in tumor-normal differentiation; (2) Alternatively spliced transcripts form network modules that only partially overlap with those derived from gene networks. The expression patterns of some transcript modules differ considerably between tumor and normal tissues; (3) My preliminary results indicate that splicing patterns could be used to distinguish patients with good and poor prognosis. Again, the expressions of multiple transcripts yield better prognosis predictions than those of single transcripts. Collectively, these results suggest that alternative splicing could be further explored for its potential applications in cancer diagnostics and therapeutics.