以Latent Semantic Topology方法探討癌症基因之調控機制

Discovering Genetic Networks from Metastatic Tumor Microarray Data via Latent Semantic Topology

羅育如(Yu-Ru Luo)

指導教授 ： 蔣以仁

共同指導教授 ： 張慧朗(Phei-Lang Chang)

臺北醫學大學/醫學院/醫學資訊研究所/碩士(2007年)

摘要

至今，癌症依然是一種絕症，仍然缺乏有效的治療藥物。而癌症最令人難以捉摸的特性是轉移(metastasis)和復發(relapse)。根據統計，癌細胞轉移所造成的死亡率約為90%。因此，如果能有效的找出有關控制轉移的基因及其調控網路，癌症的轉移應可望有效的被控制，進而降低死亡率。現今，普遍認為發現癌症轉移相關基因的轉錄調控機制，是一種研究癌症治療藥物的最佳方法。未來在臨床上也許能開發藥物以抑制轉移基因的表現，避免腫瘤轉移。屆時，我們認為癌症將會變成一種慢性疾病。

在本研究中，我們提出了一個潛在語義拓樸學的方法，這個方法可以用來萃取隱藏在生物晶片表現資料中，具有生物意義的基因調控網路；有別於先前的研究，此方法改善了傳統群集分析必須將基因強迫分類的規則，並利用圖形化的方式顯示分群結果。我們總共分析了6個生物晶片表現資料，包含33個良性癌細胞和35個轉移性癌細胞，共9516個基因探針。由此，我們可以發現許多癌症轉移共同調控的基因及可能與癌症轉移機制有關的基因交互作用網路。然而，更完整且正確的調控機制，則需要生物學家進一步實驗驗證。

關鍵字

癌症 ； 轉移 ； 潛在語義拓樸學 ； 生物晶片 ； 基因調控網路

並列摘要

So far, cancer is one of the most fatal diseases lacked of an effective medication or treatment globally. Relapse and metastasis are mystifying and unpredictable characteristics of cancer. According to the statistics, the main cause of death resulting from cancer metastasis is around 90%. If metastasis were successfully controlled, the death rate that caused by cancers would reduce. Today, to discover the transcription regulation of metastasis-related genes in human cancer is thought of the proper way to find a cure for cancers. In the coming future, the researchers may find the effective drugs that specific focus on metastasis growth, by the time we believe that cancer will become a chronic disease.

In our researches, we present a novel approach based on latent semantic topology to extract genetic network from gene expression data. Comparing to the classical clustering methods, our approach improves two events. First, it allows an overlapping between clusters, and second it is guided by the structure of the graph to define the number of clusters. We totally collect and analyze six public domain microarray data sets, comprising 9516 gene expression measurements from 33 benign tumor and 35 metastatic tumor samples. From this, we characterized a common transcriptional profile that is universally activated in most metastatic tumors relative to the benign tumors, likely reflecting essential transcriptional features of metastasis response. In addition, we present some examples of multiple gene interactions that represent potential molecular pathways that mediate the metastasis mechanism. These differential interactions can then be provided to biologists for further verification through biological experiments.

並列關鍵字

Cancer ； Metastasis ； Latent semantic topology ； Microarray ； Genetic network

參考文獻

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