Vulvar Intraepithelial Neoplasia 18.24 - 癌症細胞生化路徑網路的交互作用

Yolk Sac Tumor 18.25

二、癌症特徵網路與次網路

利用次序結構差異篩選出來的癌症相關基因(p-value <0.01)，我們直接將這些基因以及其產物蛋白質對應到人類基因調控網路以及蛋白質交互作用網路，並延伸其連結的第一

層，定義為癌症特徵基因調控網路與特徵蛋白質交互作用網路（附錄一）。例如下圖分別為

肺腺癌(

Lung Adenocarcinoma

)的特徵基因調控網路與特徵蛋白質交互作用網路：

肺腺癌特徵基因調控網路

肺腺癌特徵蛋白質交互作用網路

在肺腺癌特徵基因調控網路中，有兩個明顯的集散點(hub)，分別為 COL1A2 與 MMP1，

且都是被調控基因，因此可推論其表現易於正常細胞是癌化後的結果，而非致癌的原因。至於肺腺癌特徵蛋白質交互作用網路，因為沒有上下游關係，因此只能推論，在癌細胞中，這些交互作用較正常細胞來得強或弱。我們亦計算了這些網路的基本拓樸性質，下表是肺腺癌特徵生物網路的基本拓樸特性：

<Degree> <Betweenness> <Closeness>

肺腺癌特徵基因調控網路 1.25 1.25 0.15

肺腺癌特徵蛋白質交互作用網路 1.56 1.51 0.05

除了基本的拓樸特性外，針對這些特徵網路，我們利用 MLC 演算法(Enright et al., 2002) 來計算其可能存在的次網路。MLC 演算法是模擬流量在網路中的隨機流動過程，並產生出

顯著性。

四、癌症相關生化網路資料庫

我們總共分析了 18 大類型、涵蓋 143 種亞型的癌症，並建立其相關生化網路資料庫 (http://210.70.82.119/Cancer_Network/)，包含各類型癌症的特徵基因與其蛋白質產物、基因與蛋白質產物的註解、癌症特徵生物網路、生物次網路、生物次網路與癌症的關連性以及次網路之間可能的交互作用，這些資料在未來仍有待進一步分析與探討。

計畫成果自評

本計畫為兩年之計畫，在計畫進行過程中，教育學生具備一定之能力參與計畫，

是最困難的一環。本計畫的第一年主要在整理生物網路資料庫以及蒐集癌症相關的微陣列實驗數據，同時教育學生培養參與計畫的能力，第二年則開始進入計畫的主要部分--癌症相關生物網路探勘，由於人類相關的生物網路資料仍不豐富，因此，最後的結果仍有很大的發展空間，相信隨著公用資料庫的資料量增加，未來應能有很大的改善，

另一個問題是，癌症特徵基因的選取，以次序結構變異取代統計檢定，雖然可以得到較多的基因數，這在生物網路建立上是很有幫助的，但是，為了避免可能的偽陽性，

我們提高了篩選的標準(p-value < 0.01)。最後；我們利用 MLC 演算法定義出基因調控次網路以及蛋白質交互作用次網路，代謝次網路則依據 KEGG 的分類法，並篩選出癌症相關的次網路，關於次網路的交互作用，我們往往得到無交互作用的結果，可能原因是建立的網路並不夠完整以及人類生化網路（除了代謝網路外）基因數太少，未來應持續探討。

本計畫已初步完成癌症相關生物網物建立與分析，有待未來進一步探討，個人相信以綜觀所有類型癌症的角度來看待癌症，而非僅僅探討個別癌症，在未來應該能有一些新的發現。

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