本文選題：衰老基因　切入點：癌基因　出處：《電子科技大學》2011年博士論文　論文類型：學位論文

【摘要】：衰老和癌是密切相關的生命現(xiàn)象:癌的發(fā)生率隨衰老而上升。尋找衰老基因和癌基因是闡述兩者關系的基礎,也是當前該領域的重要工作。因為濕實驗篩選衰老基因和癌基因非常費時費力,如何分析高通量生物技術產生的數據并發(fā)展算法來幫助生物學家減少實驗工作量就顯得十分重要。在本工作中,我們首次系統(tǒng)地分析了衰老基因的特征,在此基礎上,提出了一個預測衰老基因的算法;進一步,我們通過生物實驗證實了一些預測的結果;針對最新的癌樣本突變組數據,我們發(fā)展了一個簡單高效的癌基因預測算法;對于當前很多癌基因功能注釋比較粗糙的問題,我們發(fā)展了一個預測癌基因精細功能的算法。最后,基于蛋白質相互作用網絡,我們分析并發(fā)現(xiàn)了衰老基因和癌基因的一些共同特征,這些特征較好地解釋了衰老和癌的密切關系。 詳細地,本文主要包括以下五部分內容: 1.衰老基因特征分析與預測。與非衰老基因相比較,我們系統(tǒng)地分析了衰老基因的特征。發(fā)現(xiàn)衰老基因傾向于(1)有更長的基因和蛋白序列,(2)和其它基因有更高的表達相關性,(3)在某些功能和表形上明顯地聚集,(4)更高的序列保守性,(5)位于蛋白質相互作用網絡的中心等網絡拓撲特征�；谶@些特征,我們發(fā)展了一個基于支持向量機的衰老基因預測算法。利用該算法,以高于0.85的精確率預測了243個新的衰老基因。進一步,我們評估了各個特征對預測結果的貢獻,衰老基因所富集的功能貢獻最大而其所富集的表型次之。 2.實驗驗證預測的衰老基因。對于預測的衰老基因,從文獻和實驗兩個角度進行了證實。文獻角度,一些預測的基因已被其它實驗室證明參與壽命調節(jié),例如,vps-34被算法預測為衰老基因,有研究組報道vps-34對于飲食限制引起的壽命延長是必須的。實驗角度,從預測的衰老基因列表中,我們選擇并沉默了7個基因觀察其對壽命的影響,發(fā)現(xiàn)沉默基因B0025.1或者F58F12.1的表達可以延長daf-2突變體的壽命而沉默F(xiàn)54C9.1則縮短了daf-2突變體的壽命。 3.癌基因的特征分析與預測。針對高通量的癌基因組突變譜數據,我們提出了一個基于癌功能類預測癌基因的算法。結合癌功能類與基因的非同義突變個數,我們的算法在預測的準確性上要明顯優(yōu)于前人基于選擇壓力和非同義突變個數的算法。最后,應用算法,我們將46個注釋到癌功能類并且非同義突變個數至少為3的激酶基因預測為癌基因,這對進一步的生物實驗可能有幫助。 4.癌基因精細功能預測。目前,癌基因功能和癌信號通路知識是有限的且粗糙的,成為癌機制研究的一個瓶頸。具體地,很多癌基因只是注釋到GO數據庫的高層功能類。在這里,我們開發(fā)了一個基于功能特異的蛋白質相互作用網絡的高效算法來尋找癌基因的細致功能。通過利用蛋白質已知的功能知識,193個癌基因被預測到了更細致的功能類。進一步,我們選擇并定義了一組癌功能類,應用算法,221個基因被預測到了這組功能類,提高了功能特異的相互作用子網的連通性,使得對癌功能類的認識更為清晰和完整。 5.衰老基因和癌基因的關系。針對衰老和癌表型上的密切關系,利用已有的衰老基因、癌基因和蛋白質相互作用數據,從三方面進行了分析,我們發(fā)現(xiàn)(1)衰老基因和癌基因集合高度重合,很多衰老基因本身就是癌基因;(2)在蛋白質相互作用網絡上,衰老基因和癌基因有著相似的網絡拓撲特征,都傾向位于網絡的中心,對網絡全局起著調控作用。(3)衰老基因和癌基因它們自己以及之間傾向直接的相互作用。這些結果都較好的解釋了衰老和癌的密切關系。 綜上,在分析總結衰老基因和癌基因特征的基礎上,我們分別提出了預測衰老基因、癌基因和癌基因精細功能的算法,并對衰老基因和癌基因的共同特征進行了分析。一些預測的結果已經被實驗證實。這將推動衰老和癌的研究。
[Abstract]:Aging and cancer is closely related to the phenomenon of life: cancer incidence increased with aging. In search of aging genes and oncogenes is discussed the relationship between the two, is an important work in the field at present. Because the wet screening experiment of aging genes and oncogenes is very time-consuming, how to analyze the high flux generated data and biological technology the development of algorithms to help biologists seem to decrease the workload is very important. In this work, we systematically analyzed the characteristics of aging gene, on this basis, this paper proposes a prediction algorithm of aging gene; further, we confirmed some prediction results through biological experiments; the latest cancer samples mutation group the data, we develop a simple and efficient algorithm for the prediction of cancer gene; gene function of many cancer notes is relatively rough, we develop a prediction of cancer Finally, based on the protein interaction network, we analyzed and discovered some common characteristics of aging genes and oncogenes, which better explained the close relationship between aging and cancer.
In detail, this article mainly includes the following five parts:
Analysis and prediction of genetic characteristics of 1. aging. Compared with the non aging gene, we systematically analyzed the characteristics of aging genes. Aging genes tend to (1) gene and protein sequences longer, (2) and other related gene expression is high, (3) in some function and on the table obviously the accumulation of sequence conservation (4) higher, (5) is located in the center of the network topology characteristics of the protein interaction network. Based on these features, we developed an algorithm to predict aging gene based on support vector machine. Using this algorithm, with high accuracy to 0.85 of the predicted 243 new aging gene. Further, we evaluated the contribution of each feature on the prediction results, the aging gene enriched functional contribution and the enrichment of the phenotype.
2. aging gene prediction. Experimental verification for aging gene prediction, from two aspects of literature and experiment were confirmed. The literature point of view, some of the predicted genes have been shown to be involved in regulating other laboratory life, for example, vps-34 algorithm prediction for aging gene, the study group reported that vps-34 is necessary for prolonged dietary restriction caused by life. From the point of view of experimental aging gene prediction list, we selected 7 genes silence and to observe its influence on life, found that silence gene expression of B0025.1 or F58F12.1 can prolong the life of DAF-2 mutant and DAF-2 mutant F54C9.1 silence will shorten life.
Characteristic analysis and prediction of 3. cancer genes. The high-throughput cancer genome mutation spectrum data, we propose an algorithm to predict cancer oncogene function. Based on combination of non synonymous mutations and cancer class number, our algorithm in prediction accuracy is superior to the previous selection based on pressure and the number of non synonymous mutation algorithm. Finally, application of the algorithm, we will have 46 notes to the cancer function class and non synonymous mutations in a number of at least 3 kinase gene prediction for cancer gene, which may be helpful for further biological experiments.
Prediction of 4. cancer gene fine function. At present, gene function and signal pathway of cancer knowledge is limited and rough, become a bottleneck mechanism of cancer research. In particular, many cancer genes are annotated to high-level functional class GO database. Here, we developed an efficient algorithm for functional specificity of protein interactions the role of the network based on the detailed function to find cancer genes. By using knowledge of known protein functions, 193 genes were predicted to cancer features more detailed. Further, we select and define a set of cancer function category, application method, 221 genes were predicted by this set of features, improve the interaction network connectivity function specific, the understanding of cancer function is more clear and complete.
The relationship between the 5. aging genes and oncogenes. According to the close relationship between aging and cancer phenotype, using aging gene the gene and protein interaction data were analyzed from three aspects, we found that (1) aging genes and oncogenes set a high degree of coincidence, many aging gene itself is oncogene; (2) in protein interaction networks, aging genes and oncogenes have similar characteristics of network topology, is located in the center of the network tend, play a role in regulation of the global network. (3) tend to direct interaction of aging genes and oncogenes and their own. These results can explain the close relationship between aging and cancer.
緇間笂,鍦ㄥ垎鏋愭，

本文編號：1645523