系統生物學方法發現肝癌嶄新預後標記

肝癌是目前最惡性的癌症類型之一。肝癌生物標記的發現仍然緩慢，仍然需要開發更好的方法。最近的研究顯示建立在生物意義下的系統性的方法能夠較好的引導生物標記的發性。在細胞的層面使用蛋白質交互作用來解析癌症反應路徑網路是一個具潛力但仍不完善的方法。此研究結合計算生物學的模型建構和抗體原位雜交技術的生物驗證，成功地在兩個肝癌細胞株中測量出67筆內生性的蛋白質交互作用，這67筆蛋白質交互作用連接了21個癌症反應路徑，兩個肝癌細胞株分別是Huh7 (轉移能力最低的肝癌細胞株)、Mahlavu (轉移能力最高的肝癌細胞株)。因為這兩個細胞株的轉移能力有很大的差別，此蛋白質交互作用網路採用差異化網路生物學進行分析，此分析方法發現了一筆嶄新的交互作用現象， CRKL-FLT1，此交互作用在網路的拓樸結構最為重要，更多的生物功能性驗證顯示此交互作用的表現量不僅在肝癌細胞也和其他種類癌症的細胞轉移能力呈現高度正相關。經由實驗也證明將上述兩個蛋白質的基因從肝癌細胞中移除會大幅降低肝癌細胞的轉移能力，透過實驗也證明了CRKL或FLT1會參與被視為癌細胞具備侵襲和轉移能力之重要過程上皮-間質細胞轉換過程。為了確認此交互作用被用於預後標記的能力，192個肝癌樣本被使用於免疫組織化學法的實驗，實驗結果的統計分析顯示CRKL或CRKL-FLT1的表現與無疾病存活期和整體存活期有高度的正相關，顯示此交互作用可作為一個新的肝癌預後指標。此研究顯示整合性地研究疾病網路有助於發現新的生物標記，此方法具通用性也可使用在其他種類的癌症或複雜疾病。

關鍵字

系統生物學；計算生物學；肝癌；蛋白質交互作用；反應路徑；生物標記；預後標記

並列摘要

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The pace of discovery of HCC biomarkers seems to be slowing and that advanced discovery methods are needed. Recent studies shows systems approaches can guide the identification of biomarkers based on a deeper understanding of their underlying biology. Deciphering the network of signaling pathways in cancer via protein-protein interactions (PPIs) at the cellular level is a promising approach but remains incomplete. We used computational approaches to identify PPIs among interlinked pathways and an in situ proximity ligation assay to verify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). We then applied a differential network biology analysis and determined that the novel interaction, CRKL-FLT1, has a high centrality ranking. Further validation shows the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Moreover Knockdown of CRKL and FLT1 in HCC cells lead to a decrease in cell migration via ERK signaling and the epithelial-mesenchymal transition (EMT) process. Our immunohistochemical analysis shows high expression levels of CRKL and CRKL-FLT1 pair that strongly correlate with reduced disease-free and overall survival in HCC patient samples and a multivariate analysis further established CRKL and the CRKL-FLT1 as novel prognosis markers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers.

並列關鍵字

systems biology ； computational biology ； hepatocellular carcinoma ； protein-protein interactions ； pathways ； biomarker ； prognostic marker

參考文獻

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系統生物學方法發現肝癌嶄新預後標記

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