癌症細胞生化路徑網路的交互作用
104
0
0
全文
(2) 行政院國家科學委員會補助專題研究計畫. █ 成 果 報 告 □ 期中進度報告. 癌症細胞生化路徑網路的交互作用. 計畫類別：█ 個別型計畫. □ 整合型計畫. 計畫編號：NSC 96-2221-E-468-012-MY2 執行期間：. 96. 年 8 月 1 日至. 98. 年 7 月 31 日. 計畫主持人：張培均 共同主持人： 計畫參與人員：蕭雅莉、石貴中、李崇鴻、陳怡君、林書韻、張宜婷. 成果報告類型(依經費核定清單規定繳交)：□精簡報告. █完整報告. 本成果報告包括以下應繳交之附件： □赴國外出差或研習心得報告一份 □赴大陸地區出差或研習心得報告一份 █出席國際學術會議心得報告及發表之論文各一份 □國際合作研究計畫國外研究報告書一份. 處理方式：除產學合作研究計畫、提升產業技術及人才培育研究計畫、 列管計畫及下列情形者外，得立即公開查詢 □涉及專利或其他智慧財產權，□一年█二年後可公開查詢 執行單位：亞洲大學生物資訊學系 中. 華. 民. 國. 九十八. 年. 十. 月. 三十一. 日.
(3) 中文摘要 關鍵詞：癌症，生化路徑網路，DNA 微陣列，基因次序結構，關連分析 細胞內的生化路徑網路是一體的，雖然可分為基因調控網路、訊號傳遞網路、蛋白質 交互作用網路以及代謝網路等等的類別，每個類別又可分出具有特定特徵或功能的次網 路，次網路之間是相連的，存在交互作用，甚至不同類別的生化路徑次網路也有關連存在， 然而，針對癌症細胞中這種交互作用的分析文獻，目前仍未見到。 本計畫在研究癌細胞中生化路徑次網路之間的交互作用，首先以目前已知的生化路徑 網路為基礎，重新整理並定義其次網路以及包含之基因，再利用不同細胞狀態具有不同的 基因網路次序結構，以DNA 微陣列數據來篩檢不同癌症細胞的特徵基因，最後利用關連分 析的方法，做生化路徑次網路與癌症細胞特徵基因的關連分析。. 2.
(4) 英文摘要 Keywords: cancer, pathway, DNA microarray, gene ordering structure, association analysis. There are many types of pathways in the cell cooperate to maintain the cellular function exactly, such as gene regulation pathways, signaling pathways, protein interactions, and metabolic pathways. Each pathway network consists of many sub-networks that are characterized by special biochemical function or topology property. These sub-networks are associated by each other and existing interaction. Some interactions may occur between different types of pathways. However, the research literature of such interactions in cancer cell has never seen at present. In this project, we studied the sub-network interactions in cancer cell. We intended to reorganize and annotate the sub-networks in pathways based on the public databases. Cancer related genes were screened according to the variations of gene ordering structures building by DNA microarray data in a tumor-versus-normal experiment. Association analysis was implement regarding cancer related genes and sub-network related genes to quantify the correlation.. 3.
(5) 研究目的 細胞的癌化是基因變異累積的結果，使得基因的表現或功能改變，進而細胞內整體基 因間調控亦發生改變。癌細胞具有正常細胞所沒有的許多能力，例如；癌細胞的生長與細 胞分裂是不受控制的，正常細胞在某些情況下會啟動自殺機制，癌細胞失去這種機制，癌 細胞會刺激血管增生以獲得養分，癌細胞可以不斷做細胞分裂，癌細胞可以侵犯不同組織 的其他器官，這些能力使癌症病人失去生命。 癌症相關基因是研究癌症的診斷、形成與治療的關鍵，近幾年來，基因微陣列技術被 大量應用於癌症相關基因的研究，癌症相關基因的篩檢則是根據癌組織與正常組織之間， 基因的表現量是否有顯著差異，以各種統計分析的方法來檢定，例如；倍數改變法 （Fold-Change）(Schena et al., 1995; DeRisi et al., 1997; Chen et al., 1997)、t-檢定法（t-test） 、 SAM(Tusher et al., 2001)以及 Mann-Whitney-Wilcoxon Rank Sum 檢定(Chen et al., 1997; Chambers et al., 1999)等方法，然而，這些方法找出來的癌症相關基因，在數目上通常偏低， 最近的相關文獻指出，癌症的產生是廣泛的基因變異的結果（Sjöblom et al., 2006）。我們 發展出利用基因表現的次序結構變異來做癌症分型的方法，並篩檢出癌症相關基因，結果 證明其在癌症分類的靈敏度優於其他方法（Liu et al., 2006） ，這表示次序結構變異的狀況， 更能顯示細胞狀態的變異，而篩檢出來的癌症相關基因（次序變異基因對）應更有代表性。. 4.
(6) 研究背景 DNA 微陣列技術可以同時偵測數千甚至上萬個基因的表現，如此強而有力的工具被廣 泛應用在癌症分類、癌症標記基因篩選、基因調控網路推論、功能基因體學等方面的研究 (Winzeler et al., 1999; Chen et al., 2001; Yu et al., 2004; Morley et al., 2004; Chen et al., 2005)，其中，基因調控網路的推論與分析方法的發展，主要以時間序列的 微陣列數據為分析對象，因為這樣的限制，這方面的研究目前以低等生物為主(Chen et al., 2004; Liao et al., 2005; Yugi et al.; 2005; Antonov et al., 2006 )，這些生物在 實驗操作上較容易。在癌症方面，癌症相關調控網路的推論，則大多必須結合其他類型的 實驗分析方法，針對局部性少數幾個基因或蛋白質間，就其可能的關係做推論(Katoh et. al., 2005; Bommert et al.,2006; Ryan et al., 2006)，以系統性的方式來估計與癌症 細胞相關的生化路徑網路，主要以統計上的關連性分析為主(Bussemaker et al., 2001; Haverty et al., 2004; Mlecnik et al., 2005; Liu et al., 2006)，原因在於，目前 的實驗技術並無法即時提供足夠的實驗數據，尤其是缺乏時間序列的數據，以作為癌症細 胞相關生化路徑網路的定量分析。 在探討癌症相關的細胞內生化路徑網時，通常細胞內生化路徑網路是已知的，這些網 路可以是基因調控網路、訊號傳遞網路、蛋白質交互作用網路以及代謝網路等。生物細胞 內生化路徑網的公用資料庫十分豐富(Bader et al., 2006)，其類型分佈如下表： Category. Number of databases. Protein–protein interactions. 79. Metabolic pathways. 43. Signaling pathways. 41. Pathway diagrams. 22. Transcription factors/gene regulatory networks. 20. Protein–compound interactions. 14. Genetic interaction networks. 5. Protein sequence focused. 12. Other. 11. 5.
(7) Unique total. 196. 以上這些公用資料庫，雖然都有詳細的註解，但是與人相關的部分共有 59 個資料庫，仍 需再進一步整合。 細胞內生化路徑網路是由許多次網路(sub-network)組成，每個次網路具有特定的特徵 或細胞功能，並且包含一群已知的特定基因，不同的癌症相關次網路可能不同，若以 Sm 表示第 m 個次網路的基因群，其中包含 k 個基因，並以 Gn 表示以微陣列篩檢癌症 n 所得 的癌症相關基因群，假設此微陣列總共包含 y 個基因，而細胞內全部基因數為 N，令 I=Sm ∩Gn 的基因數，則對 Sm 與 Gn 這兩群基因的分佈做關連分析(Haverty et al., 2004)，可計 算出 p-value： k. p − value = ∑ i=I. Ciy CkN−−i y CkN. p-value 越小，表示癌症 n 與次網路 m 有較高的關連性，類似的關連性分析亦可用費氏精 確檢定(Mlecnik et al., 2005)。關於癌症相關基因群的篩檢方法，主要是比較癌症細胞與 正常細胞的基因表現差異，做統計檢定，例如 t-檢定法（t-test）是最為常用的方法： x1 − x 2. Te = Sp. 1 1 + n1 n2. (n1 − 1) s12 + (n 2 − 1) s 22 Sp = n1 + n2 − 2 其中 n1 與 n2 為癌症細胞與正常細胞的個別實驗次數， x1 與 x 2 分別代表基因 x 在兩種細胞 狀態的平均表現量，t-檢定通常要求實驗數據呈現常態分佈，因此，原始基因表現量數據 須做對數轉換（xÆlog(x)），使接近常態分佈，若觀察值為 Te,obs，則 p-vale 為 Prob(|Te| >. Te,obs)，一般定義 p-vale 小於 0.05 的基因為癌細胞相關基因。 我們在發展癌症分型方法的過程中，提出了以基因網路的次序結構來來定義細胞狀態 並做癌症分型的方法(Liu et al., 2006)。當兩個基因 i 與 j 在 DNA 微陣列上的信號強度具有 顯著次序關係；即次序關係係數 rij 大於某一顯著性門檻值，則建立這兩個基因的連線：. 6.
(8) N. γi j =1− ∑ s =1. [ xs j − xsi ] +. N ( x max − x min ). ⎧ xs j − xsi [ xs j − xsi ] + = ⎨ 0 ⎩. xs j > xs i xs j ≤ xs i. 上式中 x s i 是實驗樣本s中基因i的表現信號強度；N 為樣本總數； x max 與 x min 則為實驗樣 本中最大與最小基因表現信號強度。次序結構網路是一個有向圖(directed graph)，連線i →j意味著基因i的信號強度比基因j來得低，我們定義i與j的連線為i流出而j流入，若i與j 的連線具雙向性，我們定義為對等狀態。所以當某一個基因有很高的由內向外連線，表 示這個基因有相對較低的表現信號強度；當某一個基因有很高的由外向內連線表示這個 基因有相對較高的表現信號強度。因為我們不知道此次序關係係數rij的分佈，因此，門 檻值的決定採用隨機取樣測試的過程來模擬樣本空間的分佈：(a) 隨機自各個實驗樣本 中選取一對基因構成一組基因對，重複此過程5000次；(b) 對於這些隨機選取的各組基 因對，分別計算次序關係係數 γ p (p = 1, 2, … , 5000)；(c) 由上述可以獲得一個次序關 係係數的分布，以這個次序關係係數分布的顯著水平1% (P < 0.01)做為建構次序結構網 路的門檻值。 利用次序結構來評估細胞狀態的效果十分顯著，這可以從用於癌症分型的高精確度 來證明(Liu et al., 2006)，若比較癌症細胞與正常細胞的基因次序結構差異，可以篩檢出 造成這些差異的相關基因，這些基因必定與癌症相關，這些基因的分佈是廣泛的，比由 t-檢定等方法(Schena et al., 1995; DeRisi et al., 1997; Chen et al., 1997; Chambers et al., 1999; Tusher et al., 2001)所篩檢出來的癌症相關基因，更有代表性。 細胞內的生化路徑網路是一體的，雖然，可分為基因調控網路、訊號傳遞網路、蛋 白質交互作用網路以及代謝網路等等的類別，每個類別又可分出具有特定特徵或功能的 次網路，然而，次網路之間是相連的，存在交互作用，甚至不同類別的生化路徑網路也 有關連存在，針對癌症細胞中這種交互作用或關連的分析文獻，目前仍未見到。 我們將以目前已知的生化路徑網路為基礎，重新整理並定義其次網路以及包含之 基因，再利用不同細胞狀態具有不同的基因網路次序結構，來篩選不同癌症細胞的特徵 基因，最後利用現有的關連分析方法，做生化路徑次網路與癌症細胞特徵基因的關連分 析，並發展分析方法來探討癌症相關生化路徑次網路間的彼此影響，以及生化路徑次網 7.
(9) 路內節點之間，與癌症相關的可能互動關係。. 8.
(10) 研究方法 一、細胞生化路徑網路的整合 目前與人類有關的的生化路徑網路資料庫，可分為七大類，涵蓋蛋白質交互作用、代 謝反應網路、信號傳遞網路、蛋白質-化合物交互作用網路以及基因調控網路，其資料庫名 稱如下列表： A. Protein-Protein Interactions BIND - Biomolecular Interaction Network Database BioGRID - General Repository for Interaction Datasets CA1Neuron - Pathways of the hippocampal CA1 neuron Details DIP - Database of Interacting Proteins DopaNet - DopaNet GPCR-PD - G protein-coupled receptors protein database HiMAP - Human Interactome Map HPID - Human Protein Interaction Database HumanPSD - Human Proteome Survey Database KinaseDB - Kinase Pathway Database MINT - Molecular Interactions Database OPHID - The Online Predicted Human Interaction Database PhosphoSite - Cell Signaling Technology's PhosphoSite Database PINdb - Proteins Interacting in the Nucleus database POINT - Prediction of Interactome PPID - Protein-Protein Interaction Database ProChart - ProChart database of signal transduction pathway information ProNet - Protein-protein Interaction Database PubGene - PubGene Ulysses - Projection of Protein Networks across Species HiMAP - Human Interactome Map Cancer Cell Map - The Cancer Cell Map. B. Metabolic Pathways GenMAPP - Gene MicroArray Pathway Profiler GOLD.db - Genomics of Lipid-associated Disorders MetaCore - MetaCore pathway database PathArt - Pathway Articulator Reactome - Reactome KnowledgeBase 9.
(11) Reactome - Reactome KnowledgeBase GenMAPP - Gene MicroArray Pathway Profiler PathArt - Pathway Articulator GOLD.db - Genomics of Lipid-associated Disorders MetaCore - MetaCore pathway database. C. Signaling Pathways CA1Neuron - Pathways of the hippocampal CA1 neuron Cancer Cell Map - The Cancer Cell Map GenMAPP - Gene MicroArray Pathway Profiler GOLD.db - Genomics of Lipid-associated Disorders Hedgehog - Hedgehog Signaling Pathway Database INOH - Integrating Network Objects with Hierarchies MetaCore - MetaCore pathway database PANTHER - PANTHER PathArt - Pathway Articulator Pathways Knowledge Base - Ingenuity Pathways Knowledge Base PhosphoSite - Cell Signaling Technology's PhosphoSite Database PID - CMAP Pathway Interaction Database Reactome - Reactome KnowledgeBase TRMP - Therapeutically Relevant Multiple Pathways Database. D. Pathway Diagrams BioCarta - BioCarta Pathway Diagrams Hedgehog - Hedgehog Signaling Pathway Database INOH - Integrating Network Objects with Hierarchies PID - CMAP Pathway Interaction Database TRMP - Therapeutically Relevant Multiple Pathways Database. E. Transcription Factors / Gene Regulatory Networks cisRED - Cis-regulatory element database ECRbase - Evolutionary conserved region database Hedgehog - Hedgehog Signaling Pathway Database HemoPDB - Hematopoiesis Promoter Database MAPPER - MAPPER microrna.org - Microrna.org target database TRED - Transcriptional Regulatory Element Database 10.
(12) F. Protein-Compound Interactions CTD - Comparative Toxicogenomics ORDB - Olfactory Receptor Database. G. Genetic Interaction Networks BIND - Biomolecular Interaction Network Database BioGRID - General Repository for Interaction Datasets. 以上這些資料庫有些同時含有不同類別之生化路徑網路資料，有些必須付費才能使 用，我們篩選了較完整的資料庫並整合成人類的基因調空網路以及蛋白質交互作用網路， 以人工單筆查詢的方式，逐一下載整理，原始資料中註解有誤或不一致的基因或蛋白質均 予刪除，最後得到如下圖所示的生物網路。. 人類基因調控網路. 11.
(13) 人類蛋白質交互作用網路 以上的網路是到目前為止，我們所整合出來最完整的生化路徑網路，若以三點以上成一子 群，則其中包含 56 個基因調控子網路以及 135 個蛋白質交互作用子網路，由於人類相關的 資料仍不夠豐富，真實的網路應該更為複雜。至於代謝反應的生物網路，則以 KEGG 的資 料庫為主，並針對其既有之分類為標準，作為後續癌症相關代謝網路分析的依據。. 二、微陣列數據資料庫來源 我們使用 Oncomine database 作為微陣列基因表現的數據來源(Rhodes et al., 2004)，這 是專門提供作為癌症基因體研究的資料庫，其中包含 18,740 個 DNA 微陣列實驗，約 58,000,000 筆以上的基因表現數據。Oncomine database 蒐集的這些微陣列資料並不提供直 接下載，但是有提供其來源可以作為蒐集資料的線索，其中包含 cDNA 微陣列數據與寡核 甘酸微陣列數據。相關的人類癌症微陣列實驗數據來源如下表：. Database. Web site. Reference. ArrayExpress. http://www.ebi.ac.uk/arrayexpress. Brazma et al., 2005. GeneNote. http://genecards.weizmann.ac.il/genenote/. Shmueli et al., 2003. 12.
(14) GEO. http://www.ncbi.nlm.nih.gov/geo/. HugeIndex. http://zlab.bu.edu/HugeSearch. Haverty et al., 2002. ITTACA. http://bioinfo.curie.fr/ittaca. Elfilali et al., 2006. LOLA. http://www.lola.gwu.edu/. PEPR. http://pepr.cnmcresearch.org. RefExA. http://www.lsbm.org/site_e/database/index.html. SOURCE. http://source.stanford.edu. SMD. http://genome-www.stanford.edu/microarray. Edgar et al., 2002. Chen et al., 2004. Diehn et al., 2003 Ball et al., 2005. 蒐集所得的微陣列數據均加以註解，註解內容包含基因的 GenBank accession numbers、 RefSeq ID、UniGene ID、Gene Ontology ID、所屬的生化路徑次網路以及基因功能描述等， 並將每一個 DNA 微陣列實驗的基因表現對應到生化路徑網路上。 根據 Oncomine database 這個資料庫提供的資訊，我門蒐集整理了 126 個微陣列實驗 資料集，同一組實驗資料集往往針對多種類型的癌症，總共涵蓋 18 種類型、143 種亞型的 癌症，如下列表：. #. Type. ＊. Platform. Subtype Infiltrating Bladder Urothelial Carcinoma. 1. ＊. Reference. Human Genome U133A Array. Dyrskjot et al.. Human Genome U133A Array. Dyrskjot et al.. Superficial Bladder Cancer. Human Genome U133A Array. Dyrskjot et al.. Anaplastic Astrocytoma. Human Genome U133 Plus 2.0 Array. Sun et al.. Anaplastic Oligoastrocytoma. Human Genome U133 Plus 2.0 Array. French et al.. Anaplastic Oligodendroglioma. Human Genome U133 Plus 2.0 Array. French et al.. Astrocytoma. Human Genome U95A-Av2 Array. Shai et al.. Atypical Teratoid/Rhabdoid Tumor. HumanGeneFL Array. Pomeroy et al.. Classic Medulloblastoma. HumanGeneFL Array. Pomeroy et al.. Desmoplastic Medulloblastoma. HumanGeneFL Array. Pomeroy et al.. Diffuse Astrocytoma. Human Genome U133 Plus 2.0 Array. Sun et al.. Glioblastoma. Human Genome U133 Plus 2.0 Array. Lee et al.. Meningioma. Human Cancer Biology Array. Watson et al.. Oligoastrocytoma. Liang. Liang et al.. Oligodendroglioma. Human Genome U133 Plus 2.0 Array. Sun et al.. Pilocytic Astrocytoma. Human Genome U95A-Av2 Array. Gutmann et al.. Primary Glioblastoma. Affymetrix GeneChip 100K SNP. Beroukhim et al.. Secondary Glioblastoma. Affymetrix GeneChip 100K SNP. Beroukhim et al.. Ductal Breast Carcinoma in Situ. Radvanyi. Radvanyi et al.. Bladder Cancer Stage 0is Bladder Urothelial Carcinoma. Brain and CNS 2 Cancer. 3. Breast Cancer. 13.
(15) 4. Ductal Breast Carcinoma. Human Genome U133 Plus 2.0 Array. Richardson et al.. Fibroadenoma. Sorlie. Sorlie et al.. Invasive Breast Carcinoma. Agilent Human Genome 44K. Finak et al.. Invasive Lobular Breast Carcinoma. Human Genome U133 Plus 2.0 Array. Turashvili et al.. Invasive Mixed Breast Carcinoma. Radvanyi. Radvanyi et al.. Lobular Breast Carcinoma. Zhao. Zhao et al.. Human Genome U133 Plus 2.0 Array. Pyeon et al.. Barrett's Esophagus. Hao Esophagus. Hao et al.. Cecum Adenocarcinoma. Human Genome U133 Plus 2.0 Array. Kaiser et al.. Colon Adenocarcinoma. GPL4811. Ki et al.. Colon Adenoma. Human Genome U133 Plus 2.0 Array. Sabates-Bellver et al.. Colon Carcinoma. Zou. Zou et al.. Colon Mucinous Adenocarcinoma. Human Genome U133 Plus 2.0 Array. Kaiser et al.. Colorectal Adenoma. GPL3408. Gaspar et al.. Graudens. Graudens et al.. Diffuse Gastric Adenocarcinoma. Chen. Chen et al.. Esophageal Adenocarcinoma. Hao. Hao et al.. Gastric Intestinal Type Adenocarcinoma. Chen. Chen et al.. Gastric Mixed Adenocarcinoma. Chen. Chen et al.. Rectal Adenocarcinoma. Human Genome U133 Plus 2.0 Array. Kaiser et al.. Rectal Adenoma. Human Genome U133 Plus 2.0 Array. Sabates-Bellver et al.. Rectal Mucinous Adenocarcinoma. Human Genome U133 Plus 2.0 Array. Kaiser et al.. Rectosigmoid Adenocarcinoma. Human Genome U133 Plus 2.0 Array. Kaiser et al.. Floor of the Mouth Carcinoma. Human Genome U133 Plus 2.0 Array. Pyeon et al.. Head and Neck Squamous Cell Carcinoma. Human Genome U133A Array. Ginos et al.. Hypopharyngeal Squamous Cell Carcinoma. Human Genome U133A Array. Schlingemann et al.. Oral Cavity Carcinoma. Human Genome U133 Plus 2.0 Array. Pyeon et al.. Oral Cavity Squamous Cell Carcinoma. Human Genome U133A Array. Toruner et al.. Oropharyngeal Carcinoma. Human Genome U133 Plus 2.0 Array. Pyeon et al.. Salivary Gland Adenoid Cystic Carcinoma. Human Genome U95A-Av2 Array. Frierson et al.. Thyroid Gland Carcinoma. Human Genome U95A-Av2 Array. Huang et al.. Tongue Carcinoma. Human Genome U133 Plus 2.0 Array. Pyeon et al.. Tongue Squamous Cell Carcinoma. Human Genome U133 Plus 2.0 Array. Ye et al.. Tonsillar Carcinoma. Human Genome U133 Plus 2.0 Array. Pyeon et al.. Chromophobe Renal Cell Carcinoma. Human Genome U133 Plus 2.0 Array. Yusenko et al.. Clear Cell Renal Cell Carcinoma. Human Genome U133 Plus 2.0 Array. Yusenko et al.. Clear Cell Sarcoma of the Kidney. Human Genome U133A Array. Cutcliffe et al.. Granular Renal Cell Carcinoma. Higgins. Higgins et al.. Hereditary Clear Cell Renal Cell Carcinoma. Human Genome U133A Array. Beroukhim et al.. Cervical Cancer. Gastrointestinal Colorectal Carcinoma 5 Cancer. Head and Neck 6 Cancer. 7. Kidney Cancer. 14.
(16) Non-Hereditary Clear Cell Renal Cell. 8. 9. 10. Carcinoma. Human Genome U133A Array. Beroukhim et al.. Papillary Renal Cell Carcinoma. Human Genome U133 Plus 2.0 Array. Yusenko et al.. Renal Wilms Tumor. Human Genome U133 Plus 2.0 Array. Yusenko et al.. Acute Adult T-Cell Leukemia/Lymphoma. Human Genome U133A Array. Choi et al.. Acute Myeloid Leukemia. Human Genome U133A Array. Valk et al.. B-Cell Acute Lymphoblastic Leukemia. Andersson. Andersson et al.. Chronic Adult T-Cell Leukemia/Lymphoma. Human Genome U133A Array. Choi et al.. Chronic Lymphocytic Leukemia. Human Genome U95A-Av2 Array. Haslinger et al.. Hairy Cell Leukemia. Human Genome U95A-Av2 Array. Basso et al.. T-Cell Acute Lymphoblastic Leukemia. Andersson. Andersson et al.. T-Cell Prolymphocytic Leukemia. Human Genome U133A Array. Durig et al.. Cirrhosis. Human Genome U133 Plus 2.0 Array. Wurmbach et al.. Focal Nodular Hyperplasia of the Liver. Chen. Chen et al.. Hepatocellular Adenoma. Chen. Chen et al.. Hepatocellular Carcinoma. Human Genome U133 Plus 2.0 Array. Wurmbach et al.. Liver Cell Dysplasia. Human Genome U133 Plus 2.0 Array. Wurmbach et al.. Large Cell Lung Carcinoma. Garber. Garber et al.. Lung Adenocarcinoma. Human Genome U133A Array. Su et al.. Lung Carcinoid Tumor. Human Genome U95A-Av2 Array. Bhattacharjee et al.. Small Cell Lung Carcinoma. Garber. Garber et al.. Squamous Cell Lung Carcinoma. Garber. Garber et al.. Lymphoma. Alizadeh. Alizadeh et al.. Burkitt's Lymphoma. Human Genome U95A-Av2 Array. Basso et al.. Centroblastic Lymphoma. Human Genome U95A-Av2 Array. Basso et al.. Cutaneous Follicular Lymphoma. Storz. Storz et al.. Diffuse Large B-Cell Lymphoma. Human Genome U95A-Av2 Array. Basso et al.. Follicular Lymphoma. Human Genome U95A-Av2 Array. Basso et al.. B-Cell Lymphoma. Alizadeh. Alizadeh et al.. Mantle Cell Lymphoma. Human Genome U95A-Av2 Array. Basso et al.. Marginal Zone B-Cell Lymphoma. Storz. Storz et al.. Primary Effusion Lymphoma. Human Genome U95A-Av2 Array. Basso et al.. Benign Melanocytic Skin Nevus. Human Genome U133A Array. Talantov et al.. Cutaneous Melanoma. Human Genome U133 Plus 2.0 Array. Riker et al.. Non-Neoplastic Nevus. Haqq. Haqq et al.. Significance. Human Genome U133 Plus 2.0 Array. Zhan et al.. Multiple Myeloma. HumanGeneFL Array. Zhan et al.. Leukemia. Liver Cancer. Lung Cancer. Activated B-Cell-Like Diffuse Large B-Cell. 11. Lymphoma. Germinal Center B-Cell-Like Diffuse Large. 12. Melanoma. Monoclonal Gammopathy of Undetermined 13. Myeloma. 15.
(17) Smoldering Myeloma. Human Genome U133 Plus 2.0 Array. Zhan et al.. Ovarian Adenocarcinoma. HumanGeneFL Array. Welsh et al.. Human Genome U95A-Av2 Array/Human Genome U95B Array/Human Genome U95C Array/Human Genome U95D Array/Human Genome U95E Array. Ovarian Clear Cell Adenocarcinoma. Lu et al.. Human Genome U95A-Av2 Array/Human Genome U95B Array/Human Genome U95C Array/Human Genome U95D Array/Human Genome U95E Array. Ovarian Endometrioid Adenocarcinoma 14. Ovarian Cancer. Lu et al.. Human Genome U95A-Av2 Array/Human Genome U95B Array/Human Genome U95C Array/Human Genome U95D Array/Human Genome U95E Array. Ovarian Mucinous Adenocarcinoma. Lu et al.. Human Genome U95A-Av2 Array/Human Genome U95B Array/Human Genome U95C Array/Human Genome U95D Array/Human Genome U95E Array. Ovarian Serous Adenocarcinoma. Lu et al. http://cancergenome.. Ovarian Serous Cystadenocarcinoma. Human Genome U133A Array. nih.gov/ Iacobuzio-Donahue. 15. Pancreatic Adenocarcinoma. Iacobuzio-Donahue. et al.. Pancreatic Carcinoma. Human Genome U133A Array. Segara et al.. Pancreatic Cancer. Human Genome U133A Array/Human Genome Pancreatic Ductal Adenocarcinoma. U133B Array. Ishikawa et al.. Pancreatic Intraepithelial Neoplasia. Buchholz Pancreas. Buchholz et al.. Pancreatitis. HumanGeneFL Array. Logsdon et al.. Benign Prostatic Hyperplasia. Tomlins. Tomlins et al.. Human Genome U133A Array/Human Genome 16. 17. 18. Prostate Cancer Prostate Adenocarcinoma. U133B Array. Vanaja et al.. Prostate Carcinoma. Human Genome U133 Plus 2.0 Array. Varambally et al.. Prostatic Intraepithelial Neoplasia. Tomlins. Tomlins et al.. Dedifferentiated Liposarcoma. Human Genome U133A Array. Detwiller et al.. Fibrosarcoma. Human Genome U133A Array. Detwiller et al.. Leiomyosarcoma. Human Genome U133A Array. Detwiller et al.. Malignant Fibrous Histiocytoma. Human Genome U133A Array. Detwiller et al.. Pleomorphic Liposarcoma. Human Genome U133A Array. Detwiller et al.. Round Cell Liposarcoma. Human Genome U133A Array. Detwiller et al.. Synovial Sarcoma. Human Genome U133A Array. Detwiller et al.. Uterine Corpus Leiomyosarcoma. HumanGeneFL Array. Quade et al.. Actinic (Solar) Keratosis. Human Genome U133A Array. Nindl et al.. Sarcoma. Other Cancer. 16.
(18) Adrenal Cortex Adenoma. Human Genome U95A-Av2 Array. Giordano et al.. Adrenal Cortex Carcinoma. Human Genome U95A-Av2 Array. Giordano et al.. Human Genome U133A Array/Human Genome Embryonal Carcinoma. U133B Array. Korkola et al.. Endometrial Endometrioid Adenocarcinoma. HumanGeneFL Array. Mutter et al.. Familial Parathyroid Hyperplasia. Human Genome U133A Array. Morrison et al.. Malignant Glioma. HumanGeneFL Array. Pomeroy et al.. Human Genome U133A Array/Human Genome Mixed Germ Cell Tumor. U133B Array. Korkola et al.. Non-Familial Multiple Gland Neoplasia. Human Genome U133A Array. Morrison et al.. Parathyroid Gland Adenoma. Human Genome U133A Array. Morrison et al.. Parathyroid Hyperplasia. Human Genome U133A Array. Morrison et al.. Pleural Malignant Mesothelioma. Human Genome U133A Array. Gordon et al.. Primitive Neuroectodermal Tumor. HumanGeneFL Array. Pomeroy et al.. Renal Oncocytoma. Human Genome U133 Plus 2.0 Array. Yusenko et al.. Human Genome U133A Array/Human Genome Seminoma. U133B Array. Korkola et al.. Skin Basal Cell Carcinoma. Human Genome U133 Plus 2.0 Array. Riker et al.. Skin Squamous Cell Carcinoma. Human Genome U133 Plus 2.0 Array. Riker et al.. Human Genome U133A Array/Human Genome Teratoma. U133B Array. Korkola et al.. Testicular Intratubular Germ Cell Neoplasia. Skotheim. Skotheim et al.. Testicular Seminoma. Skotheim. Skotheim et al.. Testicular Teratoma. Skotheim. Skotheim et al.. Testicular Yolk Sac Tumor. Skotheim. Skotheim et al.. Uterine Corpus Leiomyoma. HumanGeneFL Array. Quade et al.. Vulvar Intraepithelial Neoplasia. Human Genome U133 Plus 2.0 Array. Santegoets et al.. Human Genome U133A Array/Human Genome Yolk Sac Tumor ＊. U133B Array. Korkola et al.. 上表僅列出各類型癌症其中一個微陣列實驗與其參考資料。. 三、癌症相關基因的篩檢方法 我們以次序結構變異來篩檢癌症相關基因。當兩個基因 i 與 j 在 DNA 微陣列上的信 號強度具有顯著次序關係；即次序關係係數 rij 大於某一顯著性門檻值，則建立這兩個基 因的連線：. 17.
(19) N. γi j =1− ∑ s =1. [ xs j − xs i ] +. N ( xmax − xmin ). ⎧ xs j − xs i [ xs j − xs i ] + = ⎨ ⎩ 0. xs j > xs i xs j ≤ xs i. 上式中 x s i 是實驗樣本 s 中基因 i 的表現信號強度；N 為樣本總數； x max 與 x min 則為實驗樣 本中最大與最小基因表現信號強度。次序結構網路是一有向圖(directed graph)，連線 i → j 意味著基因 i 的信號強度比基因 j 來得低，所以當某一個基因有很高的由內向外連線，表 示這個基因有相對較低的表現信號強度；當某一個基因有很高的由外向內連線表示這個基 因有相對較高的表現信號強度。因為我們不知道此次序關係係數 rij 的分佈，因此，門檻值 的決定採用隨機取樣測試過程：(a) 隨機自各個實驗樣本中選取一對基因構成一組基因 對，重複此過程 5000 次；(b) 對於這些隨機選取的各組基因對，分別計算次序關係係數 γ p (p = 1, 2, … , 5000)；(c) 由上述可以獲得一個次序關係係數的分布，以這個次序關係係數 分布的顯著水平 1% (P < 0.01)做為建構次序結構網路的門檻值。 根據以上的演算法分別建立癌症以及與之相對應的正常細胞的次序結構網路，比較兩 個次序結構，其中有次序關係變異的連線與其兩端的基因，即為癌症相關基因。 我們所收集的微陣列資料集有些是同類型癌症但是實驗平台或環境不同，若相同基因 對在不同平台的實驗有不一致的次序關係變異，則分別針對 i → j 與 j →i 在癌細胞與正 常細胞的 r 比值，先轉換為 ln(r 比值)，再利用 meta-analysis (Whitehead et al., 1991)來檢定. 其是否顯著，並確認其次序關係變異是否存在，只要 i → j 與 j →i 其中之一的 ln(r 比值) 是顯著的，即認定其次序關係變異存在，其兩端的基因，即為癌症相關基因。. 四、癌症相關生化路徑次網路的關連性分析 利用次序關係變異所篩檢出來的癌症相關基因，對應到整個生物網路（包含基因調空 網路、蛋白質交互作用網路以及代謝網路），並延伸其連結至下一層，定義為癌症相關生 化網路，計算其拓樸性質並探討其可能存在的次網路。探討的基本拓樸性質如下： 1. Degree：自由度；是網路上節點的連線數量。這裡的連線數量在次序結構網路中， 指的是向外連線與向內連線的總和。 2. Betweenness：居間度；是度量某節點被其他任兩節點間，最短路徑通過之次數。 18.
(20) 3. Closeness：緊密度；是度量某節點至其他節點最短路徑的平均值。 若以 S 表示某個次網路的基因群，其中包含 k 個基因，以 G 表示以次序結構變異所 篩檢出來的癌症相關基因群，假設此微陣列總共包含 y 個基因，而細胞內全部基因數為 N， 令 I=S∩G 的基因數，則對 S 與 G 這兩群基因的分佈做關連分析(Haverty et al., 2004)，可 計算出 p-value：. Ciy CkN−−i y =∑ CkN i=I k. pval. p-value 越小，表示此癌症 G 與次網路 S 有較高的關連性，計算出 p-value 後，再計算出 Q-value (false discovery rate) (Storey et al., 2003)。 超幾何分佈(hypergeometric probability)被廣泛應用在評估兩個事件的相關度，我們亦 應用在評估不同類型癌症的相關性上，以瞭解不同癌症間的關連性，以建立癌症網路。. 19.
(21) 結果與討論 一、各類型癌症之間的相關性網路 我們利用超幾何分佈(hypergeometric probability)來評估各類型癌症之間的相關程度， 若兩種癌症的顯著相關基因數為 Nr 與 Nq，細胞內的基因總數為 N，且兩種類行癌症的特 徵基因交集數為 Nc，則兩類型癌症相關程度的 p-value 計算如下式：. Pval =. i C Nr C NNq−−Nri ∑ C Nq i = Nc N Nq. 則相關性可定義如下：. Weightedge = − ln( Pval ) 若兩癌症類型間的 Pval 小於 0.01 既建立其連結關係，Weightedge 即為此連結的權重，如下圖：. 結果顯示；同類型但是不同亞型的癌症的確較易聚在一起，但是有些不同類型的癌症 反而比同類型癌症更有相關性，這表示不同類型的癌症有可能具有相同的生物網路模 組或特徵基因，這表示其成癌的過程可能類似，在藥物治療上，可能也會有類似的反 應，甚至可推論；相同的癌症藥物，也許可以治療不同類型的癌症，這個現象仍有待 進一步加以探討。我們亦發現有兩小群的癌症沒有與其他癌症有顯著的關連，分別為 黑色素瘤與神經膠質母細胞瘤。圖中的編號與各類型癌症的對照表如下表： 20.
(22) Type. Bladder Cancer. Brain and CNS Cancer. Breast Cancer. Subtype. Index. Infiltrating Bladder Urothelial Carcinoma. 1.1. Stage 0is Bladder Urothelial Carcinoma. 1.2. Superficial Bladder Cancer. 1.3. Anaplastic Astrocytoma. 2.1. Anaplastic Oligoastrocytoma. 2.2. Anaplastic Oligodendroglioma. 2.3. Astrocytoma. 2.4. Atypical Teratoid/Rhabdoid Tumor. 2.5. Classic Medulloblastoma. 2.6. Desmoplastic Medulloblastoma. 2.7. Diffuse Astrocytoma. 2.8. Glioblastoma. 2.9. Meningioma. 2.10. Oligoastrocytoma. 2.11. Oligodendroglioma. 2.12. Pilocytic Astrocytoma. 2.13. Primary Glioblastoma. 2.14. Secondary Glioblastoma. 2.15. Ductal Breast Carcinoma in Situ. 3.1. Ductal Breast Carcinoma. 3.2. Fibroadenoma. 3.3. Invasive Breast Carcinoma. 3.4. Invasive Lobular Breast Carcinoma. 3.5. Invasive Mixed Breast Carcinoma. 3.6. Lobular Breast Carcinoma. 3.7. Cervical Cancer. 4 Barrett's Esophagus. 5.1. Cecum Adenocarcinoma. 5.2. Colon Adenocarcinoma. 5.3. Colon Adenoma. 5.4. Colon Carcinoma. 5.5. Colon Mucinous Adenocarcinoma. 5.6. Colorectal Adenoma. 5.7. Colorectal Carcinoma. 5.8. Diffuse Gastric Adenocarcinoma. 5.9. Esophageal Adenocarcinoma. 5.10. Gastric Intestinal Type Adenocarcinoma. 5.11. Gastric Mixed Adenocarcinoma. 5.12. Gastrointestinal Cancer. 21.
(23) Head and Neck Cancer. Rectal Adenocarcinoma. 5.13. Rectal Adenoma. 5.14. Rectal Mucinous Adenocarcinoma. 5.15. Rectosigmoid Adenocarcinoma. 5.16. Floor of the Mouth Carcinoma. 6.1. Head and Neck Squamous Cell Carcinoma. 6.2. Hypopharyngeal Squamous Cell Carcinoma. 6.3. Oral Cavity Carcinoma. 6.4. Oral Cavity Squamous Cell Carcinoma. 6.5. Oropharyngeal Carcinoma. 6.6. Salivary Gland Adenoid Cystic Carcinoma. 6.7. Thyroid Gland Carcinoma. 6.8. Tongue Carcinoma. 6.9. Tongue Squamous Cell Carcinoma. 6.10. Tonsillar Carcinoma. 6.11. Chromophobe Renal Cell Carcinoma. 7.1. Clear Cell Renal Cell Carcinoma. 7.2. Clear Cell Sarcoma of the Kidney. 7.3. Granular Renal Cell Carcinoma. 7.4. Hereditary Clear Cell Renal Cell Carcinoma. 7.5. Non-Hereditary Clear Cell Renal Cell Carcinoma. 7.6. Papillary Renal Cell Carcinoma. 7.7. Renal Wilms Tumor. 7.8. Acute Adult T-Cell Leukemia/Lymphoma. 8.1. Acute Myeloid Leukemia. 8.2. B-Cell Acute Lymphoblastic Leukemia. 8.3. Chronic Adult T-Cell Leukemia/Lymphoma. 8.4. Chronic Lymphocytic Leukemia. 8.5. Hairy Cell Leukemia. 8.6. T-Cell Acute Lymphoblastic Leukemia. 8.7. T-Cell Prolymphocytic Leukemia. 8.8. Cirrhosis. 9.1. Focal Nodular Hyperplasia of the Liver. 9.2. Hepatocellular Adenoma. 9.3. Hepatocellular Carcinoma. 9.4. Liver Cell Dysplasia. 9.5. Large Cell Lung Carcinoma. 10.1. Lung Adenocarcinoma. 10.2. Lung Carcinoid Tumor. 10.3. Small Cell Lung Carcinoma. 10.4. Kidney Cancer. Leukemia. Liver Cancer. Lung Cancer. 22.
(24) Lymphoma. Squamous Cell Lung Carcinoma. 10.5. Activated B-Cell-Like Diffuse Large B-Cell Lymphoma. 11.1. Burkitt's Lymphoma. 11.2. Centroblastic Lymphoma. 11.3. Cutaneous Follicular Lymphoma. 11.4. Diffuse Large B-Cell Lymphoma. 11.5. Follicular Lymphoma. 11.6. Germinal Center B-Cell-Like Diffuse Large B-Cell. Melanoma. Myeloma. Lymphoma. 11.7. Mantle Cell Lymphoma. 11.8. Marginal Zone B-Cell Lymphoma. 11.9. Primary Effusion Lymphoma. 11.10. Benign Melanocytic Skin Nevus. 12.1. Cutaneous Melanoma. 12.2. Non-Neoplastic Nevus. 12.3. Monoclonal Gammopathy of Undetermined Significance. 13.1. Multiple Myeloma. 13.2. Smoldering Myeloma. 13.3. Ovarian Adenocarcinoma. 14.1. Ovarian Clear Cell Adenocarcinoma. 14.2. Ovarian Endometrioid Adenocarcinoma. 14.3. Ovarian Mucinous Adenocarcinoma. 14.4. Ovarian Serous Adenocarcinoma. 14.5. Ovarian Serous Cystadenocarcinoma. 14.6. Pancreatic Adenocarcinoma. 15.1. Pancreatic Carcinoma. 15.2. Pancreatic Ductal Adenocarcinoma. 15.3. Pancreatic Intraepithelial Neoplasia. 15.4. Pancreatitis. 15.5. Benign Prostatic Hyperplasia. 16.1. Prostate Adenocarcinoma. 16.2. Prostate Carcinoma. 16.3. Prostatic Intraepithelial Neoplasia. 16.4. Dedifferentiated Liposarcoma. 17.1. Fibrosarcoma. 17.2. Leiomyosarcoma. 17.3. Malignant Fibrous Histiocytoma. 17.4. Pleomorphic Liposarcoma. 17.5. Round Cell Liposarcoma. 17.6. Synovial Sarcoma. 17.7. Ovarian Cancer. Pancreatic Cancer. Prostate Cancer. Sarcoma. 23.
(25) Other Cancer. Uterine Corpus Leiomyosarcoma. 17.8. Actinic (Solar) Keratosis. 18.1. Adrenal Cortex Adenoma. 18.2. Adrenal Cortex Carcinoma. 18.3. Embryonal Carcinoma. 18.4. Endometrial Endometrioid Adenocarcinoma. 18.5. Familial Parathyroid Hyperplasia. 18.6. Malignant Glioma. 18.7. Mixed Germ Cell Tumor. 18.8. Non-Familial Multiple Gland Neoplasia. 18.9. Parathyroid Gland Adenoma. 18.10. Parathyroid Hyperplasia. 18.11. Pleural Malignant Mesothelioma. 18.12. Primitive Neuroectodermal Tumor. 18.13. Renal Oncocytoma. 18.14. Seminoma. 18.15. Skin Basal Cell Carcinoma. 18.16. Skin Squamous Cell Carcinoma. 18.17. Teratoma. 18.18. Testicular Intratubular Germ Cell Neoplasia. 18.19. Testicular Seminoma. 18.20. Testicular Teratoma. 18.21. Testicular Yolk Sac Tumor. 18.22. Uterine Corpus Leiomyoma. 18.23. Vulvar Intraepithelial Neoplasia. 18.24. 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：. 24.
(26) 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： 25.
(27) <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 可能存在的次網路結構，這些次網路可能是癌症基因調控模組或者是蛋白質交互作用模 組。以肺腺癌特徵蛋白質交互作用網路為例，其次網路結構如下：. 三、癌症特徵次網路與癌症的關連性 若以 S 表示某個次網路的基因群，其中包含 k 個基因，以 G 表示以次序結構變異所 篩檢出來的癌症相關基因群，假設此微陣列總共包含 y 個基因，而細胞內全部基因數為 N， 令 I=S∩G 的基因數，則對 S 與 G 這兩群基因的分佈做關連分析(Haverty et al., 2004)，可 計算出 p-value： k. pval = ∑ i=I. Ciy CkN−−i y CkN. pvalue 越小，表示此癌症 G 與次網路 S 有較高的關連性，我們定義其關連強度為 1-pvalue 我們計算了癌症相關的次網路以及其關連強度，結果發現其關連度往往不具統計上的 26.
(28) 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。. 27.
(29) 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。. 28.
(30) 參考文獻. Aaboe, M., Birkenkamp-Demtroder, K., Wiuf, C., Sørensen, F.B., Thykjaer, T., Sauter, G., Jensen, K.M., Dyrskjøt, L., and Ørntoft, T. (2006) SOX4 expression in bladder carcinoma: clinical aspects and in vitro functional characterization. Cancer Res., 66, 3434-3442. Albert, R. and Barabasi, A.L. (2005) Statistical mechanics of complex networks. Reviews of modern physics, 74, 47-97. Andersson, A., Ritz, C., Lindgren, D., Edén, P., Lassen, C., Heldrup, J., Olofsson, T., Råde, J., and Fontes, M., Porwit-Macdonald, A., Behrendtz, M., Höglund, M., Johansson, B., and Fioretos, T. (2007) Microarray-based classification of a consecutive series of 121 childhood acute leukemias: prediction of leukemic and genetic subtype as well as of minimal residual disease status. Leukemia, 21, 1198-203. Antonov, A., Tetko, I.V., and Mewes, H.W. (2006) A systematic approach to infer biological relevance and biases of gene network structures. Nucleic Acids Research, 34, e6 Bader, G.D., Cary, M.P., and Sander, C. (2006) Pathguide: a Pathway Resource List. Nucleic Acids Research, 34, D504-D506. Ball, C.A., Demeter, J., Gollub, G., Hebert, J.M., Hernandez-Boussard, T., Jin, H., Nitzberg, M., Wymore, F., Brown, P.O.1,, Sherlock, G. (2005) The Stanford Microarray Database accommodates additional microarray platforms and data formats. Nucleic Acids Research, 33, D580-D582. Basso, K., Margolin, A.A., Stolovitzky, G., Klein, U., Dalla-Favera, R., and Califano, A. (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet., 37, 382-90. Beroukhim, R., Getz, G., Nghiemphu, L., Barretina, J., Hsueh, T., Linhart, D., Vivanco, I., Lee, J.C., Huang, J.H., Alexander, S., Du, J., Kau, T., Thomas, R.K., Shah, K., Soto, H., Perner, S., Prensner, J., Debiasi, R.M., Demichelis, F., Hatton, C., Rubin, M.A., Garraway, L.A., Nelson, S.F., Liau, L., Mischel, P.S., Cloughesy, T.F., Meyerson, M., Golub, T.A., Lander, E.S., Mellinghoff, I.K., and Sellers, W.R. (2007) Proc Natl Acad Sci U S A, 104, 20007-20012. Beroukhim, R., Brunet, J.P., Di Napoli, A., Mertz, K.D., Seeley, A., Pires, M.M., Linhart, D., Worrell, R.A., Moch, H., Rubin, M.A., Sellers, W.R., Meyerson, M., Linehan, W.M., Kaelin, W.G. Jr., and Signoretti, S. (2009) Patterns of gene expression and copy-number alterations in von-hippel lindau disease-associated and sporadic clear cell carcinoma of the kidney. Cancer Res., 69, 4674-4681. 29.
(31) Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, R., Gillette, M., Loda, M., Weber, G., Mark, E.J., Lander, E.S., Wong, W., Johnson, B.E., Golub, T.R., Sugarbaker, D.J., and Meyerson, M. (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A, 98, 13790-13795. Bommert, K., Bargou, R.C., and Stuhmer, T. (2006) Signalling and survival pathways in multiple myeloma. Eur. J. Cancer, 42, 1574-80 Buchholz, M., Braun, M., Heidenblut, A., Kestler. H.A., Klöppel, G., Schmiegel, W., Hahn, S.A., Lüttges, J., and Gress, T.M. (2005) Transcriptome analysis of microdissected pancreatic intraepithelial neoplastic lesions. Oncogene, 24, 6626-6636. Bussemaker, H.J., Li, H., and Siggia, E.D. (2001) Regulatory element detection using correlation with expression. Nature Genet., 27, 167-171. Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., Vilo, J., Abeygunawardena, N., Holloway, E., Kapushesky, M., Kemmeren, P., Garcia Lara, G., Oezcimen, A., Sansone, S., Rocca-Serra, P. (2005) ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Research, 33, D553-D555. Bussemaker, H.J., Li, H., and Siggia, E.D. (2001) Regulatory element detection using correlation with expression. Nature Genet., 27, 167-171. Chambers, A., Angulo, A., Amaratunga, D., Guo, H., Jiang, Y., Wan, J.S., Bittner, A., Frueh, K., Jackson, M.R., Peterson, P.A., Erlander, M.G., and Ghazal, P. (1999) DNA microarrays of the complex human cytomegalovirus genome: Profiling kinetic class with drug sensitive viral gene expression. J. Virol., 73, 5757-5766. Chen, H.C., Lee, H.C., Lin, T.Y., Li, W.H., and Chen, B.S. (2004) Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle. Bioinformatics, 20, 1914-1927. Chen, J.J., Lin, Y.C., Yao, P.L., Yuan, A., Chen, H.Y., Shun, C.T., Tsai, M.F., Chen, C.H. and Yang, P.C. (2005) Tumor-associated macrophages: the double-edged sword in cancer progression. J. Clin. Oncol. 23, 953-964. Chen, J.J.W., Peck, K., Hong, T.M., Yang, S.C., Sher, Y.P., Shih, J.Y., Wu, R., Wu, C.W. and Yang, P.C. (2001) Global analysis of gene expression in invasion by a lung cancer model. Cancer Res. 61, 5223-5230.. 30.
(32) Chen, J., Zhao, P., Massaro, D., Clerch, L.B., Almon, R.R., DuBois, D.C., Jusko, W.J., and Hoffman, E.P. (2004) The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface. Nucleic Acids Research, 32, D578-D581. Chen, X., Leung, S.Y., Yuen, S.T., Chu, K.M., Ji, J., Li, R., Chan, A.S., Law, S., Troyanskaya, O.G., Wong, J., So, S., Botstein, D., and Brown, P.O. (2003) Variation in gene expression patterns in human gastric cancers. Mol Biol Cell, 14, 3208-3215. Chen, X., Cheung, S.T., So, S., Fan, S.T., Barry, C., Higgins, J., Lai, K.M., Ji, J., Dudoit, S., Ng, I.O., Van De Rijn, M., Botstein, D., and Brown, P.O. (2002) Gene expression patterns in human liver cancers. Mol Biol Cell, 13, 1929-1939. Chen, Y., Dougherty, E.D., and Bittner, M.L. (1997) Ratio-based decision and the quantitative analysis of cDNA microarray images. J. Biomed. Opt., 2, 364-374 Choi, Y.L,, Tsukasaki, K., O'Neill, M.C., Yamada, Y., Onimaru, Y., Matsumoto, K., Ohashi, J., Yamashita, Y., Tsutsumi, S., Kaneda, R., Takada, S., Aburatani, H., Kamihira, S., Nakamura, T., Tomonaga, M., and Mano, H. (2007) A genomic analysis of adult T-cell leukemia. Oncogene, 26, 1245-1255. Cutcliffe, C., Kersey, D., Huang, C.C., Zeng, Y., Walterhouse, D., and Perlman, E.J. (2005) Clear cell sarcoma of the kidney: up-regulation of neural markers with activation of the sonic hedgehog and Akt pathways. Clin Cancer Res., 11, 7986-7994. Diehn, M., Sherlock, G., Binkley, G., Jin, H., Hernandez-Boussard, T., Cherry, J.M., Brown, P.O., Alizadeh, A.A. (2003) SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Research, 31, 219-223. DeRisi, J.L., Iyer, V.R., and Brown, P.O. (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278, 680-686. Detwiller, K.Y., Fernando, N.T., Segal, N.H., Ryeom, S.W., D'Amore, P.A., and Yoon, S.S. (2005) Analysis of hypoxia-related gene expression in sarcomas and effect of hypoxia on RNA interference of vascular endothelial cell growth factor A. Cancer Res., 65, 5881-5889. Dorogovtsev, S. N. and Mendes, J. F. F. (2002) Evolution of networks. Adv. Phys., 51, 1079–1187. Dürig, J., Bug, S., Klein-Hitpass, L., Boes, T., Jöns, T., Martin-Subero, J.I., Harder, L., Baudis, M., Dührsen, U., and Siebert, R. (2007) Combined single nucleotide polymorphism-based genomic mapping and global gene expression profiling identifies novel chromosomal imbalances, 31.
(33) mechanisms and candidate genes important in the pathogenesis of T-cell prolymphocytic leukemia with inv(14)(q11q32). Leukemia, 21, 2153-2163. Dyrskjøt, L., Kruhøffer, M., Thykjaer, T., Marcussen, N., Jensen, J.L., Møller, K., and Ørntoft, T.F. (2004) Gene expression in the urinary bladder: a common carcinoma in situ gene expression signature exists disregarding histopathological classification. Cancer Res., 64, 4040-4048. Edgar, R., Domrachev, M., and Lash, A.E. (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30, 207-210. Elfilali, A., Lair, S., Verbeke, C., La Rosa, P., Radvanyi, F. and Barillot, E. (2006) ITTACA: a new database for integrated tumor transcriptome array and clinical data analysis. Nucleic Acids Research, 34, D613-D616. Enright, A.J., Van Dongen, S., and Ouzounis, C.A. (2002) An efficient algorithm for large-scale detection of protein families, Nucleic Acids Research, 30, 1575-1584.. Finak, G., Bertos, N., Pepin, F., Sadekova, S., Souleimanova, M., Zhao, H., Chen, H., Omeroglu, G., Meterissian, S., Omeroglu, A., Hallett, M., and Park, M. (2008) Stromal gene expression predicts clinical outcome in breast cancer. Nat Med., 14, 518-527. French, P.J., Swagemakers, S.M., Nagel, J.H., Kouwenhoven, M.C., Brouwer, E., van der Spek, P., Luider, T,M,, Kros, J.M., van den Bent, M.J., and Sillevis Smitt, P.A. (2005) Gene expression profiles associated with treatment response in oligodendrogliomas. Cancer Res., 65, 11335-11344. Frierson, H.F., Jr, El-Naggar, A.K., Welsh, J.B., Sapinoso, L.M., Su, A.I., Cheng, J., Saku, T., Moskaluk, C.A., Hampton, G.M. (2002) Large scale molecular analysis identifies genes with altered expression in salivary adenoid cystic carcinoma. Am J Pathol., 161, 1315-1323. Garber, M.E., Troyanskaya, O.G., Schluens, K., Petersen, S., Thaesler, Z., Pacyna-Gengelbach, M., van de Rijn, M., Rosen, G.D., Perou, C.M., Whyte, R.I., Altman, R.B., Brown, P.O., Botstein, D., and Petersen, I. (2001) Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci U S A, 98, 13784-13789. Gaspar, C., Cardoso, J., Franken, P., Molenaar, L., Morreau, H., Möslein, G., Sampson, J., Boer, J.M., de Menezes, R.X., and Fodde, R. (2008) Cross-species comparison of human and mouse intestinal polyps reveals conserved mechanisms in adenomatous polyposis coli (APC)-driven tumorigenesis. Am J Pathol., 172, 1363-80. Gisiger, T. (2001) Scale invariance in biology: coincidence or footprint of a universal mechanism? 32.
(34) Biol. Rev., 76, 161-209. Ginos, M.A., Page, G.P., Michalowicz, B.S., Patel, K.J., Volker, S.E., Pambuccian, S.E., Ondrey, F.G., Adams, G.L., and Gaffney, P.M. (2004) Identification of a gene expression signature associated with recurrent disease in squamous cell carcinoma of the head and neck. Cancer Res., 64, 55-63. Giordano, T.J., Thomas, D.G., Kuick, R., Lizyness, M., Misek, D.E., Smith, A.L., Sanders, D., Aljundi, R.T., Gauger, P.G., Thompson, N.W., Taylor, J.M., and Hanash, S.M. (2003) Distinct transcriptional profiles of adrenocortical tumors uncovered by DNA microarray analysis. Am J Pathol., 162, 521-531. Gordon, G.J., et al. (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research, 62, 4963-4967. Gordon, G.J., Rockwell, G.N., Jensen, R.V., Rheinwald, J.G., Glickman, J.N., Aronson, J.P., Pottorf, B.J., Nitz, M.D., Richards, W.G., Sugarbaker, D.J., and Bueno, R. (2005) Identification of novel candidate oncogenes and tumor suppressors in malignant pleural mesothelioma using large-scale transcriptional profiling. Am J Pathol., 166, 1827-1840. Graudens, E., Boulanger, V., Mollard, C., Mariage-Samson, R., Barlet, X., Grémy, G., Couillault, C., Lajémi, M., Piatier-Tonneau, D., Zaborski, P., Eveno, E., Auffray, C., and Imbeaud, S. (2006) Deciphering cellular states of innate tumor drug responses. Genome Biol., 7, R19. Gutmann, D.H., Hedrick, N.M., Li, J., Nagarajan, R., Perry, A., and Watson, M.A. (2002) Comparative gene expression profile analysis of neurofibromatosis 1-associated and sporadic pilocytic astrocytomas. Cancer Res., 62, 2085-2091. Hao, Y., Triadafilopoulos, G., Sahbaie, P., Young, H.S., Omary, M.B., and Lowe, A.W. (2006) Gene expression profiling reveals stromal genes expressed in common between Barrett's esophagus and adenocarcinoma. Gastroenterology, 131, 925-933. Haqq, C., Nosrati, M., Sudilovsky, D., Crothers, J., Khodabakhsh, D., Pulliam, B.L., Federman, S., Miller, J.R., Allen, R.E., Singer, M.I., Leong, S.P,, Ljung, B.M., Sagebiel, R.W., and Kashani-Sabet, M. (2005) The gene expression signatures of melanoma progression. Proc Natl Acad Sci U S A, 102, 6092-6097. Haslinger, C., Schweifer, N., Stilgenbauer, S., Döhner, H., Lichter, P., Kraut, N., Stratowa, C., and Abseher, R. (2004) Microarray gene expression profiling of B-cell chronic lymphocytic leukemia subgroups defined by genomic aberrations and VH mutation status. J Clin Oncol., 22, 3937-3949. 33.
(35) Haverty, P.M., Hansen, U., and Weng, Z. (2004) Computational inference of transcriptional regulatory networks from expression proÆling and transcription factor binding site identification. Nucleic Acids Research, 32, 179-188. Haverty, P., Weng, Z., Best, N., Auerback, K., Hsiao, L., Jensen, R., Gullans, S. (2002) HugeIndex: a database with visualization tools for high-density oligonucleotide array data from normal human tissues. Nucleic Acids Research, 30, 214-217 Huang, Y., Prasad, M., Lemon, W.J., Hampel, H., Wright, F.A., Kornacker, K., LiVolsi, V., Frankel, W., Kloos, R.T., Eng, C., Pellegata, N.S., and de la Chapelle, A. (2001) Gene expression in papillary thyroid carcinoma reveals highly consistent profiles. Proc Natl Acad Sci U S A, 98, 15044-15049. Iacobuzio-Donahue, C.A., Maitra, A., Olsen, M., Lowe, A.W., van Heek, N.T., Rosty, C., Walter, K., Sato, N., Parker, A., Ashfaq, R., Jaffee, E., Ryu, B., Jones, J., Eshleman, J.R., Yeo, C.J., Cameron, J.L., Kern, S.E., Hruban, R.H., Brown, P.O., and Goggins, M. (2003) Exploration of global gene expression patterns in pancreatic adenocarcinoma using cDNA microarrays. Am J Pathol., 162, 1151-1162. Ishikawa, M., Yoshida, K., Yamashita, Y., Ota, J., Takada, S., Kisanuki, H., Koinuma, K., Choi, Y.L., Kaneda, R., Iwao, T., Tamada, K., Sugano, K., and Mano, H. (2005) Experimental trial for diagnosis of pancreatic ductal carcinoma based on gene expression profiles of pancreatic ductal cells. Cancer Sci., 96, 387-393. Jeong, H., Mason, S.P., Barabasi, A.L., and Oltvai, Z.N. (2001) Lethality and centrality in protein networks. Nature, 411, 41–41. Kaiser, S., Park, Y.K., Franklin, J.L., Halberg, R.B., Yu, M., Jessen, W.J., Freudenberg, J., Chen, X., Haigis, K., Jegga, A.G., Kong, S., Sakthivel, B., Xu, H., Reichling, T., Azhar, M., Boivin, G.P., Roberts, R.B., Bissahoyo, A.C., Gonzales, F., Bloom, G.C., Eschrich, S., Carter, S.L., Aronow, J.E., Kleimeyer, J., Kleimeyer, M., Ramaswamy, V., Settle, S.H., Boone, B., Levy, S., Graff, J.M., Doetschman, T., Groden, J., Dove, W.F., Threadgill, D.W., Yeatman, T.J., Coffey, R.J. and Jr, Aronow, B.J. (2007) Transcriptional recapitulation and subversion of embryonic colon development by mouse colon tumor models and human colon cancer. Genome Biol., 8, R131. Katoh, Y., and Katoh, M. (2005) Hedgehog signaling pathway and gastric cancer. Cancer Biol. Ther., 4, 1050-1054. Keller, J., Gader, P., and Hocaoglu, A.K. (2000) Fuzzy Integrals in Image Processing and Recognition. Fuzzy Measures and Integrals: Theory and Applications, pp. 435-466. 34.
(36) Ki, D.H., Jeung, H.C., Park, C.H., Kang, S.H., Lee, G.Y., Lee, W.S., Kim, N.K., Chung, H.C., and Rha, S.Y. (2007) Whole genome analysis for liver metastasis gene signatures in colorectal cancer. Int J Cancer, 121, 2005-2012. Korkola, J.E., Houldsworth, J., Chadalavada, R.S., Olshen, A.B., Dobrzynski, D., Reuter, V.E., Bosl, G.J., and Chaganti, R.S. (2006) Down-regulation of stem cell genes, including those in a 200-kb gene cluster at 12p13.31, is associated with in vivo differentiation of human male germ cell tumors. Cancer Res., 66, 820-827. Lee, J., Kotliarova, S., Kotliarov, Y., Li, A., Su, Q., Donin, N.M., Pastorino, S., Purow, B.W., Christopher, N., Zhang, W., Park, J.K., and Fine, H.A. (2006) Tumor stem cells derived from glioblastomas cultured in bFGF and EGF more closely mirror the phenotype and genotype of primary tumors than do serum-cultured cell lines. Cancer Cell, 9, 391-403. Liang, Y., Diehn, M., Watson, N., Bollen, A.W., Aldape, K.D., Nicholas, M.K., Lamborn, K.R., Berger, M.S., Botstein, D., Brown, P.O., and Israel, M.A. (2005) Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. Proc Natl Acad Sci U S A, 102, 5814-5819. Liao, C., Boscolo, R., Yang, Y.L., Tran, L.M., Sabatti, C., and Roychowdhury, V.P. (2005) Network component analysis: Reconstruction of regulatory signals in biological systems. Proc. Nat. Acad. Sci., 100, 15522–15527. Liu, C.C., Chen, W.S, Lin, C.C., Liu, H.S., Yang, P.C., Chang, P.C., and Chen, J.W. (2006) Topology-based cancer classification and related pathway mining using microarray data. Nucleic Acids Research, 34, 4069-4080. Logsdon, C.D., Simeone, D.M., Binkley, C., Arumugam, T., Greenson, J.K., Giordano, T.J., Misek, D.E., Kuick, R., and Hanash, S. (2003) Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer. Cancer Res., 63, 2649-2657. Lu, K.H., Patterson, A.P., Wang, L., Marquez, R.T., Atkinson, E.N., Baggerly, K.A., Ramoth, L.R., Rosen, D.G., Liu, J., Hellstrom, I., Smith, D., Hartmann, L., Fishman, D., Berchuck, A., Schmandt, R., Whitaker, R., Gershenson, D.M., Mills, G.B., Bast. R.C. (2004) Selection of potential markers for epithelial ovarian cancer with gene expression arrays and recursive descent partition analysis. Clin Cancer Res., 10, 3291-3300. Mlecnik, B., Scheideler, M., Hackl, H., Hartler, J., Sanchez-Cabo, F., and Trajanoski, Z. (2005) PathwayExplorer: web service for visualizing high-throughput expression data on biological 35.
(37) pathways. Nucleic Acids Research, 33, W633-W637. Morrison, C., Farrar, W., Kneile, J., Williams, N., Liu-Stratton, Y., Bakaletz, A., Aldred, M.A., and Eng, C. (2004) Molecular classification of parathyroid neoplasia by gene expression profiling. Am J Pathol., 165, 565-576. Mutter, G.L., Baak, J.P., Fitzgerald, J.T., Gray, R., Neuberg, D., Kust, G.A., Gentleman, R., Gullans, S.R., Wei, L.J., and Wilcox, M. (2001) Global expression changes of constitutive and hormonally regulated genes during endometrial neoplastic transformation. Gynecol Oncol., 83, 177-185. Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., Sturla, L.M., Angelo, M., McLaughlin, M.E., Kim, J.Y., Goumnerova, L.C., Black, P.M., Lau, C., Allen, J.C., Zagzag, D., Olson, J.M., Curran, T., Wetmore, C., Biegel, J.A., Poggio, T., Mukherjee, S., Rifkin, R., Califano, A., Stolovitzky, G., Louis, D.N., Mesirov, J.P., Lander, E.S., and Golub, T.R. (2002) Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 415, 436-442. Popescu, M., Keller, J.M., and Mitchell, J.A. (2006) Fuzzy Measures on the Gene Ontology for Gene Product Similarity. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 3, 263-274. Pyeon, D., Newton, M.A., Lambert, P.F., den Boon, J.A., Sengupta, S., Marsit, C.J., Woodworth, C.D., Connor, J.P., Haugen, T.H., Smith, E.M., Kelsey, K.T., Turek, L.P., and Ahlquist, P. (2007) Fundamental differences in cell cycle deregulation in human papillomavirus-positive and human papillomavirus-negative head/neck and cervical cancers. Cancer Res., 67, 4605-4619. Quade, B.J., Wang, T.Y., Sornberger, K., Dal Cin, P., Mutter, G.L., and Morton, C.C. (2004) Molecular pathogenesis of uterine smooth muscle tumors from transcriptional profiling. Genes Chromosomes Cancer, 40, 97-108. Radvanyi, L., Singh-Sandhu, D., Gallichan, S., Lovitt, C., Pedyczak, A., Mallo, G., Gish, K., Kwok, K., Hanna, W., Zubovits, J., Armes, J., Venter, D., Hakimi, J., Shortreed, J., Donovan, M., Parrington, M., Dunn, P., Oomen, R., Tartaglia, J., and Berinstein, N.L. (2005) The gene associated with trichorhinophalangeal syndrome in humans is overexpressed in breast cancer. Proc Natl Acad Sci U S A, 102, 11005-11010. Rhodes, D.R. et al. (2004) OCOMINE: a cancer microarray database and integrated data mining platform. Neoplasia, 6, 1-6. Rhodes, D.R., Kalyana-Sundaram, S., Mahavisno, V., Barrette, T.R., Ghosh, D., and Chinnaiyan, A.M. (2005) Mining for regulatory programs in the cancer transcriptome. Nature Genet., 37, 36.
(38) 579-583. Richardson, A.L., Wang, Z.C., De Nicolo, A., Lu, X., Brown, M., Miron, A., Liao, X., Iglehart, J.D., Livingston, D.M., and Ganesan, S. (2006) X chromosomal abnormalities in basal-like human breast cancer. Cancer Cell, 9, 121-132. Riker, A.I., Enkemann, S.A., Fodstad, O., Liu, S., Ren, S., Morris, C., Xi, Y., Howell, P., Metge, B., Samant, R.S., Shevde, L.A., Li, W., Eschrich, S., Daud, A., Ju, J., and Matta, J. (2008) The gene expression profiles of primary and metastatic melanoma yields a transition point of tumor progression and metastasis. BMC Med Genomics, 28, 1-13. Ryan, P.E., Davies, G.C., Nau, M.M., and Lipkowitz, S. (2006) Regulating the regulator: negative regulation of Cbl ubiquitin ligases. Trends Biochem Sci., 31, 79-88. Sabates-Bellver, J., Van der Flier, L.G., de Palo, M., Cattaneo, E., Maake, C., Rehrauer, H., Laczko, E., Kurowski, M.A., Bujnicki, J.M., Menigatti, M., Luz, J., Ranalli, T.V., Gomes, V., Pastorelli, A., Faggiani, R., Anti, M., Jiricny, J., Clevers, H., and Marra, G. (2007) Transcriptome profile of human colorectal adenomas. Mol Cancer Res., 5, 1263-1275. Santegoets, L.A., Seters, M., Helmerhorst, T.J., Heijmans-Antonissen, C., Hanifi-Moghaddam, P., Ewing, P.C., van Ijcken, W.F., van der Spek, P.J., van der Meijden, W.I., and Blok, L.J. (2007) HPV related VIN: highly proliferative and diminished responsiveness to extracellular signals. Int J Cancer, 121, 759-766. Schena, M., Shalon, D., Davis, R.W., and Brown, P.O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270, 467-470. Schlingemann, J., Habtemichael, N., Ittrich, C., Toedt, G., Kramer, H., Hambek, M., Knecht, R., Lichter, P., Stauber, R., and Hahn, M. (2005) Patient-based cross-platform comparison of oligonucleotide microarray expression profiles. Lab Invest., 85, 1024-1039. Segara, D., Biankin, A.V., Kench, J.G., Langusch, C.C., Dawson, A.C., Skalicky, D.A., Gotley, D.C., Coleman, M.J., Sutherland, R.L., and Henshall, S.M. (2005) Expression of HOXB2, a retinoic acid signaling target in pancreatic cancer and pancreatic intraepithelial neoplasia. Clin Cancer Res., 11, 3587-3596. Shai, R., Shi, T., Kremen, T.J., Horvath, S., Liau, L.M., Cloughesy, T.F., Mischel, P.S., and Nelson, S.F. (2003) Gene expression profiling identifies molecular subtypes of gliomas. Oncogene, 22, 4918-4923. Shmueli, O., Shmoish, M., Rosen, N., Benjamin-Rodrig, H., Yanai, I., Ophir, R., Shklar, M., 37.
(39) Almashanu, L., Madi, A., Sirota, A., Kats, P., Chalifa-Caspi, V., Safran, M., Lancet, D. (2003) Human Gene-Centric Databases at the Weizmann Institute of Science: GeneCards, UDB, CroW 21 and HORDE. Nucleic Acids Research, 31, 142-146. Sjöblom et al., (2006) The consensus coding sequences of human breast and colorectal cancers. Science, 314, 268-274. Skotheim, R.I., Lind, G.E., Monni, O., Nesland, J.M., Abeler, V.M., Fosså, S.D., Duale, N., Brunborg, G., Kallioniemi, O., Andrews, P.W., and Lothe, R.A. (2005) Differentiation of human embryonal carcinomas in vitro and in vivo reveals expression profiles relevant to normal development. Cancer Res., 65, 5588-5598. Sørlie, T., Perou, C.M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Thorsen, T., Quist, H., Matese, J.C., Brown, P.O., Botstein, D., Eystein Lønning, P., and Børresen-Dale, A.L. (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A, 98, 10869-10874. Storey,J.D., and Tibshirani,R. (2003) Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA, 100, 9440–9445. Strogatz, S.H. (2001) Exploring complex networks. Nature, 410, 268–276. Storz, M.N., van de Rijn, M., Kim, Y.H., Mraz-Gernhard, S., Hoppe, R.T., and Kohler, S. (2003) Gene expression profiles of cutaneous B cell lymphoma. J Invest Dermatol., 120, 865-870. Su, L.J., Chang, C.W., Wu, Y.C., Chen, K.C., Lin, C.J., Liang, S.C., Lin, C.H., Whang-Peng, J., Hsu, S.L., Chen, C.H., and Huang, C.Y. (2007) Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme. BMC Genomics, 1, 140. Sun, L., Hui, A.M., Su, Q., Vortmeyer, A., Kotliarov, Y., Pastorino, S., Passaniti, A., Menon, J., Walling, J., Bailey, R., Rosenblum, M., Mikkelsen, T., and Fine, H.A. (2006) Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell, 9, 287-300. Talantov, D., Mazumder, A., Yu, J.X., Briggs, T., Jiang, Y., Backus, J., Atkins, D., and Wang, Y. (2005) Novel genes associated with malignant melanoma but not benign melanocytic lesions. Clin Cancer Res., 11, 7234-7242. Tomlins, S.A., Mehra, R., Rhodes, D.R., Cao, X., Wang, L., Dhanasekaran, S.M., Kalyana-Sundaram, S., Wei, J.T., Rubin, M.A., Pienta, K.J., Shah, R.B., and Chinnaiyan, A.M. 38.
(40) (2007) Integrative molecular concept modeling of prostate cancer progression. Nat Genet., 39, 41-51. Toruner, G.A., Ulger, C., Alkan, M., Galante, A.T., Rinaggio, J., Wilk, R., Tian, B., Soteropoulos, P., Hameed, M.R., Schwalb, M.N., and Dermody, J.J. (2004) Association between gene expression profile and tumor invasion in oral squamous cell carcinoma. Cancer Genet Cytogenet., 154, 27-35. Turashvili, G., Bouchal, J., Baumforth, K., Wei, W., Dziechciarkova, M., Ehrmann, J., Klein, J., Fridman, E., Skarda, J., Srovnal, J., Hajduch, M., Murray, P., and Kolar, Z. (2007) Novel markers for differentiation of lobular and ductal invasive breast carcinomas by laser microdissection and microarray analysis. BMC Cancer, 27, 7-55. Tusher, V.G., Tibshirani, R., and Chu, G. (2001) Significance analysis of microarrays applied to ionizing radiation respone. Proc. Nat. Acad. Sci., 98, 5116-5121. Valk, P.J., Verhaak, R.G., Beijen, M.A., Erpelinck, C.A., Barjesteh van Waalwijk van Doorn-Khosrovani, S., Boer, J.M., Beverloo, H.B., Moorhouse, M.J., van der Spek, P.J., Löwenberg, B., and Delwel, R. (2004) Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med., 350, 1617-1628. Vanaja, D.K., Cheville, J.C., Iturria, S.J., and Young, C.Y. (2003) Transcriptional silencing of zinc finger protein 185 identified by expression profiling is associated with prostate cancer progression. Cancer Res., 63, 3877-3882. Watson, M.A., Gutmann, D.H., Peterson, K., Chicoine, M.R., Kleinschmidt-DeMasters, B.K., Brown, H.G., and Perry, A. (2002) Molecular characterization of human meningiomas by gene expression profiling using high-density oligonucleotide microarrays. Am J Pathol., 161, 665-672. Whitehead, A., and Whitehead, J. (1991) A general parametric approach to the meta-analysis of randomized clinical trials. Statistics in Medicine, 10, 1665-1677. Winzeler, E.A., et al. (1999) Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901-6. Wurmbach, E., Chen, Y.B., Khitrov, G., Zhang, W., Roayaie, S., Schwartz, M., Fiel, I., Thung, S., Mazzaferro, V., Bruix, J., Bottinger, E., Friedman, S., Waxman, S., and Llovet, J.M. (2007) Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology, 45, 938-947. Ye, H., Yu, T., Temam, S., Ziober, B.L., Wang, J., Schwartz, J.L., Mao, L., Wong, D.T., and Zhou, 39.
(41) X. (2008) Transcriptomic dissection of tongue squamous cell carcinoma. BMC Genomics, 6, 69. Yeoh, E.-J., et al. (2002) Classification, subtype discovery, and prediction of outcome in pediatric lymphoblastic leukemia by gene expression profiling. Cancer Cell, 1, 133–143. Yugi, K., Nakayama, Y., Kojima, S., Kitayama, T., and Tomita, M. (2005) A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks. BMC Bioinformatics, 6, 299-315 Yusenko, M.V., Kuiper, R.P., Boethe, T., Ljungberg, B., van Kessel, A.G., and Kovacs, G. (2009) High-resolution DNA copy number and gene expression analyses distinguish chromophobe renal cell carcinomas and renal oncocytomas. BMC Cancer, 9, 152. Zhan, F., Hardin, J., Kordsmeier, B., Bumm, K., Zheng, M., Tian, E., Sanderson, R., Yang, Y., Wilson, C., Zangari, M., Anaissie, E., Morris, C., Muwalla, F., van Rhee, F., Fassas, A., Crowley, J., Tricot, G., Barlogie, B., and Shaughnessy, J. (2002) Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. Blood, 99, 1745-1757. Zhan, F., Barlogie, B., Arzoumanian, V., Huang, Y., Williams, D.R., Hollmig, K., Pineda-Roman, M., Tricot, G., van Rhee, F., Zangari, M., Dhodapkar, M., and Shaughnessy, J.D. (2007) Gene-expression signature of benign monoclonal gammopathy evident in multiple myeloma is linked to good prognosis. Blood, 109, 1692-1700. Zhao, H., Langerød, A., Ji, Y., Nowels, K.W., Nesland, J.M., Tibshirani, R., Bukholm, I.K., Kåresen, R., Botstein, D., Børresen-Dale, A.L., and Jeffrey, S.S. (2004) Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. Mol Biol Cell, 15, 2523-2536. Zou, T.T., Selaru, F.M., Xu, Y., Shustova, V., Yin, J., Mori, Y., Shibata, D., Sato, F., Wang, S., Olaru, A., Deacu, E., Liu, T.C., Abraham, J.M., and Meltzer, S.J. (2002) Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon. Oncogene, 21, 4855-4862.. 40.
(42) 附錄一、常見癌症的特徵基因調控網路與特徵蛋白質交互作用網路. Type:Bladder Cancer. Subtype:Infiltrating Bladder Urothelial Carcinoma. 41.
(43) Type:Bladder Cancer. Subtype:Stage 0is Bladder Urothelial Carcinoma. 42.
(44) Type:Bladder Cancer. Subtype:Superficial Bladder Cancer. 43.
(45) Type:Brain and CNS Cancer. Subtype:Anaplastic Astrocytoma. 44.
(46) Type:Brain and CNS Cancer. Subtype:Anaplastic Oligoastrocytoma. 45.
(47) Type:Brain and CNS Cancer. Subtype:Anaplastic Oligodendroglioma. 46.
(48) Type:Brain and CNS Cancer. 47. Subtype:Astrocytoma.
(49) Type:Brain and CNS Cancer. Subtype:Atypical Teratoid/Rhabdoid Tumor. 48.
(50) Type:Brain and CNS Cancer. Subtype:Classic Medulloblastoma. 49.
(51) Type:Brain and CNS Cancer. Subtype:Desmoplastic Medulloblastoma. 50.
(52) Type:Brain and CNS Cancer. Subtype:Diffuse Astrocytoma. 51.
(53) Type:Brain and CNS Cancer. 52. Subtype:Glioblastoma.
(54) Type:Brain and CNS Cancer. 53. Subtype:Meningioma.
(55) Type:Brain and CNS Cancer. Subtype:Oligoastrocytoma. 54.
(56) Type:Brain and CNS Cancer. Subtype:Oligodendroglioma. 55.
(57) Type:Brain and CNS Cancer. Subtype:Pilocytic Astrocytoma. 56.
(58) Type:Brain and CNS Cancer. Subtype:Primary Glioblastoma. 57.
(59) Type:Brain and CNS Cancer. Subtype:Secondary Glioblastoma. 58.
(60) Type:Breast Cancer. Subtype:Ductal Breast Carcinoma in Situ. 59.
(61) Type:Breast Cancer. Subtype:Ductal Breast Carcinoma. 60.
(62) Type:Breast Cancer. Subtype:Fibroadenoma. 61.
(63) Type:Breast Cancer. Subtype:Invasive Breast Carcinoma. 62.
(64) Type:Breast Cancer. Subtype:Invasive Lobular Breast Carcinoma. 63.
(65) Type:Breast Cancer. Subtype:Invasive Mixed Breast Carcinoma. 64.
(66) Type:Breast Cancer. Subtype:Lobular Breast Carcinoma. 65.
(67) Type:Leukemia. Subtype:Acute Adult T-Cell Leukemia/Lymphoma. 66.
(68) Type:Leukemia. Subtype:Acute Myeloid Leukemia. 67.
(69) Type:Leukemia. Subtype:B-Cell Acute Lymphoblastic Leukemia. 68.
(70) Type:Leukemia. Subtype:Chronic Adult T-Cell Leukemia/Lymphoma. 69.
(71) Type:Leukemia. Subtype:Chronic Lymphocytic Leukemia. 70.
(72) Type:Leukemia. Subtype:Hairy Cell Leukemia. 71.
(73) Type:Leukemia. Subtype:T-Cell Acute Lymphoblastic Leukemia. 72.
(74) Type:Leukemia. Subtype:T-Cell Prolymphocytic Leukemia. 73.
(75) Type:Liver Cancer. Subtype:Cirrhosis. 74.
(76) Type:Liver Cancer. Subtype:Focal Nodular Hyperplasia of the Liver. 75.
(77) Type:Liver Cancer. Subtype:Hepatocellular Adenoma. 76.
(78) Type:Liver Cancer. Subtype:Hepatocellular Carcinoma. 77.
(79) Type:Liver Cancer. Subtype:Liver Cell Dysplasia. 78.
(80) Type:Lung Cancer. Subtype:Large Cell Lung Carcinoma. 79.
(81) Type:Lung Cancer. Subtype:Lung Adenocarcinoma. 80.
(82) Type:Lung Cancer. Subtype:Lung Carcinoid Tumor. 81.
相關文件
The major qualitative benefits identified include: (1) increase of the firms intellectual assets—during the process of organizational knowledge creation, all participants
This research is to integrate PID type fuzzy controller with the Dynamic Sliding Mode Control (DSMC) to make the system more robust to the dead-band as well as the hysteresis
This paper integrates the mechatronics such as: a balance with stylus probe, force actuator, LVT, LVDT, load cell, personal computer, as well as XYZ-stages into a contact-
This project integrates class storage, order batching and routing to do the best planning, and try to compare the performance of routing policy of the Particle Swarm
由於本計畫之主要目的在於依據 ITeS 傳遞模式建構 IPTV 之服務品質評估量表，並藉由決
As for current situation and characteristics of coastal area in Hisn-Chu City, the coefficients of every objective function are derived, and the objective functions of
Subsequently, the relationship study about quality management culture, quality consciousness, service behavior and two type performances (subjective performance and relative
Ogus, A.,2001, Regulatory Institutions and Structure, working paper No.4, Centre on Regulation and Competition, Institute for Development Policy and Management, University
