【摘要】：研究背景鑒定腫瘤驅動基因(cancer driver genes)一直都是腫瘤學研究的熱點,目前已經有多種基于腫瘤基因組學的工具被開發(fā),其中dJ/dS方法從不同角度鑒定了新的腫瘤驅動基因。dJ/dS僅考慮了基因外顯子(exon)和內含子(intron)的剪接位點(junction site,J)突變情況,若J位點發(fā)生突變,則pre-mRNA剪接過程失敗,進而無法產生正常的成熟mRNA,從而造成基因功能喪失(loss of function),最后誘導細胞癌變�；谶@個思路,該方法借鑒了遺傳學工具dN/dS的計算原理,用 Junction mutation(J)和 Synonymous mutation(S)觀察值之比(obs_JS)除以期望值之比(exp_JS),即dJ/dS=obs_JS/exp_JS,若dJ/dS1,則認為有正選擇效應;若dJ/dS1則認為有純化選擇作用;如果dJ/dS=1則認為無選擇壓力。我們認為腫瘤發(fā)生過程中驅動基因受到了正選擇作用。相比其他方法,dJ/dS更好地控制了背景突變率(background mutation rate,BMR),提高了方法的靈敏度。但仍存在兩個不足之處:一、dJ/dS在考慮J突變時只計算了發(fā)生在splice donor(GT)和splice acceptor(AG)四個位點上的突變,而有研究表明這四個位點臨近的區(qū)域發(fā)生突變也會引起剪接失敗,導致疾病發(fā)生,因此將剪接位點周圍的區(qū)域納入J的計算會更加準確;二、dJ/dS在計算突變譜(mutation spectrum)時僅考慮了 12種突變類型,但有文獻報道緊鄰突變位點的堿基N(ATCG)可能極大地影響突變,所以突變譜采用96種類型計算更科學。因此我們在本研究中對dJ/dS的上述兩點不足進行改進,開發(fā)dJ/dS2.0版本,并結合TCGA(The Cancer Genome Atlas)33種實體瘤的數(shù)據(jù)對腫瘤驅動基因重新分析預測。驅動基因在腫瘤中的功能研究也是一項非常重要的課題。癌變過程十分復雜,癌基因和抑癌基因在癌變過程中起重要的調控作用,多種因素可以影響癌基因和抑癌基因的突變,有研究報道人種和性別是影響基因突變的重要因素,而且部分基因突變會使得腫瘤預后更差,我們將以肝癌為研究模型來探討驅動基因在肝癌中的作用。肝癌是世界上第六位常見的惡性腫瘤,死亡率高居第三位,每年有超過70萬新發(fā)病例,且呈逐年增加趨勢。手術切除是目前早期肝癌最有效的治療手段,但術后五年復發(fā)率超過50%。近年隨著測序技術的快速發(fā)展,肝癌基因組學研究取得了很大進步,為篩選臨床診療及預后的基因標志物提供了新的技術手段。本研究擬從兩個方面開展:①改進dJ/dS方法并用于鑒定腫瘤驅動基因;②以肝癌為研究模型,結合dJ/dS2.0、dT/dS和MutSig2.0的預測結果,初步分析驅動基因在肝癌中的功能。研究目的1.改進dJ/dS算法,開發(fā)dJ/dS2.0版本,并將之用于dJ/dS2.0腫瘤驅動基因的預測;2.初步研究驅動基因在肝癌中的功能。研究方法(一)dJ/dS算法的改進1.dJ/dS2.所用數(shù)據(jù):本次研究納入TCGA的33種實體瘤的數(shù)據(jù)。2.dJ/dS2.0的計算方法a)計算突變譜:以不受選擇壓力影響的四倍簡并位點上的突變計算每種腫瘤的突變譜,同時采用96種突變類型表示突變譜,即考慮突變位點前后的堿基,如 NCNNTN,N 代表 ATCG;b)J突變的計算原則:在原dJ/dS方法J的基礎上還納入了緊鄰剪接位點的外顯子上3個堿基和內含子上6個堿基,共11個突變位點;c)S突變的計算原則:納入每個基因上所有同義突變;d)計算期望JS比值(exp_JS):采用上一步得到的突變譜分別計算每個基因的J和S的期望值(expectation,exp),然后用J的期望值比上S的期望值,即exp_JS = exp_J/exp_S;e)計算實際觀察JS比值(obs_JS):從TCGA外顯子測序數(shù)據(jù)中分別統(tǒng)計J和S的觀察值(observation,obs),然后用J的觀察值比上S的觀察值,即obs_JS=obs_J/obs_S;f)計算dJ/dS比值:用實際觀察的JS比值除以期望的JS比值得到dJ/dS比值,即dJ/dS obs_J S/exp_J S;(二)驅動基因在肝癌中的功能研究1.肝癌數(shù)據(jù)來源:TCGA和ICGC;2.驅動基因和通路的鑒定:綜合dJ/dS2.0,dT/dS和MutSig2.0三種方法在肝癌數(shù)據(jù)中的鑒定結果,并利用得到的驅動基因進行通路分析(pathway analysis);3.驅動基因的功能分析:結合肝癌臨床資料,分析驅動基因和通路在人種和性別間的突變差異,以及對肝癌預后的影響。(三)統(tǒng)計學分析1.dJ/dS2.0統(tǒng)計分析a)p和FDR計算:使用二項分布檢驗計算出每個基因對應的p值,同時用Benjamini-Hochberg 方法控制 FDR(false discovery rate);當基因的dJ/dS1且FDR小于0.05時,認為是驅動基因;b)GO功能分析:使用GOrilla進行GO功能聚類分析,FDR小于0.05認為有統(tǒng)計學意義。2.驅動基因功能的統(tǒng)計分析a)KEGG功能分析:利用DAVID運行KEGG功能分析,FDR0.1認為有統(tǒng)計學意義;b)使用fisher確切概率法比較驅動基因及通路在人種和性別間的分布;p0.05認為有統(tǒng)計學意義;c)生存分析:通過Kaplan-Meier生存曲線比較驅動基因對預后的影響,使用log-rank檢驗計算p值;用Cox比例風險回歸模型進行多因素分析;p0.05認為有統(tǒng)計學意義。研究結果(一)dJ/dS2.01.33種腫瘤的突變譜通過在剪接區(qū)域增加分析位點和拆分突變譜的方式來改進dJ/dS后,開發(fā)dJ/dS2.0版本,將之應用于TCGA33種腫瘤中可以得到更為精準的突變譜。a)高頻突變類型CT是大多腫瘤的主要突變類型,其中CpGTpG的突變率最高,但在皮膚黑色素瘤中突變率最高的是CpCCpT和TpCTpT。b)腎癌亞型的突變特點腎嫌色細胞癌突變類型主要以CpGTpG為主,腎透明細胞癌以CCGCAG、CCGCTG和GCGGTG為主,而腎乳頭狀細胞癌在腎透明細胞癌突變類型的基礎上,還包括了 CCACAA和CCCCAC。2.驅動基因除了腎上腺皮質癌、膽管癌、FPPP、腎嫌色細胞癌、彌漫性大B細胞淋巴瘤、子宮癌肉瘤和睪丸生殖細胞瘤這7種腫瘤沒有鑒定出結果,dJ/dS2.0在26種鑒定出驅動基因,值得注意的是子宮內膜癌出現(xiàn)多達344個基因,我們懷疑TCGA數(shù)據(jù)可能有異�；虿糠只蛟谧訉m內膜癌中更容易發(fā)生剪接區(qū)域突變,造成dJ/dS2.0不敏感而導致過多假陽性,因此我們移除了子宮內膜癌的鑒定結果,只用余下25種腫瘤的結果進行后續(xù)的分析。a)25種腫瘤的驅動基因dJ/dS2.0在25種實體瘤中總共鑒定出643個非冗余的腫瘤驅動基因,除了73個被CGC注釋為驅動基因外,另有570個(88.8%)是新預測出來的驅動基因;b)GO功能分析將570個驅動基因進行GO聚類分析,結果顯示有很多基因聚集在與發(fā)展和維持多細胞性相關的 GO term 上(GO:0044243,multicellular organismal catabolic process,p=2.53E-12,FDR=1.52E-8)。(二)驅動基因在肝癌中的功能分析1.肝癌的驅動基因為了更全面地分析驅動基因的功能,我們結合了 TCGA和ICGC中肝癌的數(shù)據(jù)來增加樣本量,在應用dJ/dS2.0的同時,也使用了分別由我們實驗室課題組開發(fā)的dT/dS和TCGA開發(fā)的MutSig2.0方法,三種方法共鑒定出89個驅動基因,經KEGG pathway分析發(fā)現(xiàn)這89個基因富集于10條與病毒感染或腫瘤相關的信號通路上;2.肝癌驅動基因突變類型的特點對比發(fā)現(xiàn),肝癌各驅動基因的突變類型構成比差異較大,在突變頻率較高的CTNNB1、TP53和RB1基因中,非同義突變所占比超過95%,其中CTNNB1和TP53以錯義突變?yōu)橹?而插入缺失、錯義突變、剪接區(qū)域突變及無義突變共存于RB1中;3.人種和性別影響驅動基因和通路突變的分布基因TP53和RB1及通路hsa05161和hsa05203在亞裔中更容易突變,男性中的CTNNB1、ALB、PIK3CA、BAP1基因和hsa05200通路發(fā)生率高于女性;4.生存分析經單因素篩選和多因素分析,基因KCNB2(p=0.025),KCNJ12(p=0.015),RB1(p=0.038)和 TP53(p=0.01)及乙肝感染通路 hsa05161(p=0.006)若發(fā)生突變則患者中位生存時間縮短,死亡風險比增加。結論第一章:dJ/dS2.01.改進dJ/dS算法,提高了準確性;2.由dJ/dS2.0鑒定的驅動基因參與多細胞性的發(fā)展和維持環(huán)節(jié);第二章:驅動基因在肝癌中的功能分析1.肝癌患者的性別和人種會影響驅動基因突變的分布;2.驅動基因KCNB2、KCNJ12、RB1、TP53和乙肝感染通路hsa05161突變是肝癌預后的不良因素,可能是臨床上潛在的基因標志物。
[Abstract]:Cancer driver genes (tumor driven gene genes) has always been a hot spot in oncology research. At present, a variety of tools based on tumor genomics have been developed, in which the dJ/dS method identified the new tumor driven gene.DJ/dS from the splice site of the exon (exon) and Chi Ko (intron) from different angles (J). Unction site, J) mutation, if the J site mutation, then the pre-mRNA splicing process failed, and then can not produce normal mature mRNA, resulting in the loss of gene function (loss of function), and finally induced cell cancerization. Based on this idea, the method uses the principle of the calculation of dN/dS of the legacy tool dN/dS, and uses Junction mutation and Junction. The ratio of ymous mutation (S) observation value (obs_JS) is divided by the ratio of expected values (exp_JS), that is, dJ/dS=obs_JS/exp_JS, if dJ/dS1, there is a positive selection effect; if dJ/dS1 believes that there is a purification selection effect; if dJ/dS=1 believes that there is no selective pressure. We think that the driving gene is positive in the carcinogenesis. Compared to other methods, D, D. J/dS better controls the background mutation rate (background mutation rate, BMR) and improves the sensitivity of the method. However, there are still two shortcomings: first, dJ/dS only calculates the mutation on the splice donor (GT) and splice acceptor (rate) when the J mutation is considered, and there is a study showing that the four loci are near the mutation. It also causes splicing failure, causing disease to occur, so it is more accurate to incorporate the area around the splice site into J; two, dJ/dS only considers 12 mutation types when calculating the mutation spectrum (mutation spectrum), but the base N (ATCG) that is reported to be adjacent to the mutation site may greatly affect the mutation, so 96 kinds of mutation spectrum are used. Type calculation is more scientific. Therefore, in this study, we improve the above two points of dJ/dS, develop the dJ/dS2.0 version, and combine the data of 33 solid tumors of the TCGA (The Cancer Genome Atlas) to reanalyze the tumor driving genes. It is very complicated that oncogene and tumor suppressor gene play an important regulatory role in the process of carcinogenesis. Many factors can affect the mutation of oncogene and tumor suppressor gene. There is a research report that human species and sex are important factors affecting gene mutation, and some gene mutations will make the tumor be worse after the tumor. We will discuss the model of liver cancer as a study model. Liver cancer is the sixth most common malignant tumor in the world, with a high mortality rate of third, more than 70 million new cases each year, and increasing year by year. Surgical resection is the most effective treatment for early liver cancer, but the recurrence rate of five years after surgery is more than 50%. in recent years, with the rapid development of sequencing technology. Great progress has been made in the study of hepatoma genomics, which provides new technical means for screening gene markers for clinical diagnosis and prognosis. This study is intended to be carried out in two aspects: (1) improving the dJ/dS method and identifying the tumor driven genes; (2) hepatocellular carcinoma as a research model, combined with the prediction results of dJ/ dS2.0, dT/dS and MutSig2.0, preliminary analysis The function of driving genes in liver cancer. Purpose 1. to improve the dJ/dS algorithm, to develop the dJ/dS2.0 version, and to apply it to the prediction of dJ/dS2.0 tumor driven genes; 2. to study the function of the driving genes in the liver cancer. Research method (1) the data of the improved 1.dJ/dS2. for the dJ/dS algorithm: This study included the data.2.dJ/ of 33 kinds of solid tumors of TCGA. DS2.0 calculation method a) calculation of mutation spectrum: the mutation spectrum of each tumor is calculated at four times of degenerate loci without the influence of selected pressure. At the same time, 96 mutation types are used to express the mutation spectrum, that is, the base of the mutation site, such as NCNNTN, N representing ATCG; b) J process, is calculated on the basis of the original dJ/dS method J 3 bases and 6 bases on the exons adjacent to the splice site, a total of 11 mutation sites, and a total of 11 mutation sites; c) S mutation calculation principle: integrating all the synonymous mutations on each gene; d) calculating the expected JS ratio (exp_JS): the expectation of each gene's J and S (expectation, exp) by the previous mutation spectrum, and then J, respectively, are used for J. The expectation value is compared with the expected value of the upper S, that is, exp_JS = exp_J/exp_S; E) to calculate the actual JS ratio (obs_JS): the observed values of J and S (observation, OBS) from the TCGA exons sequencing data are calculated, and then the ratio of the actual observed values is divided by the observed values of J. The ratio obtained dJ/dS ratio, dJ/dS obs_J S/exp_J S; (two) the function of the driving genes in the liver cancer 1. the source of liver cancer data: TCGA and ICGC; the identification of 2. driving genes and pathways: comprehensive dJ/dS2.0, dT/dS and MutSig2.0 three methods in the identification of liver cancer data, and using the obtained driving genes for pathway analysis (pathway analy) SIS): functional analysis of 3. driving genes: combined with the clinical data of liver cancer, analysis of the mutation difference between the driving genes and pathways in human and sex, and the effect on the prognosis of liver cancer. (three) statistical analysis of 1.dJ/dS2.0 statistical analysis a) P and FDR calculation: using two distribution tests to calculate the corresponding p value of each gene, and use Benjamini-Hochberg at the same time. The method controls FDR (false discovery rate); when the gene is dJ/dS1 and FDR is less than 0.05, it is considered to be the driving gene; b) GO functional analysis: GO function clustering analysis using GOrilla, FDR less than 0.05 Statistical significance; b) using the Fisher exact probability method to compare the distribution of driving genes and pathways between human and sex; P0.05 considered statistically significant; c) survival analysis: using the Kaplan-Meier survival curve to drive the gene to the prognosis of the gene, using the log-rank test to calculate the p value, and using the Cox proportional risk regression model for multifactor analysis; P 0.05 (0.05) the results (1) the mutation spectrum of the dJ/dS2.01.33 tumor is improved by increasing the analysis site and splitting the mutation spectrum in the splicing region, developing the dJ/dS2.0 version and applying it to the TCGA33 tumor to get a more accurate mutation spectrum.A) the high frequency mutation type CT is the majority of the tumors. In the case of mutation, the mutation rate of CpGTpG is the highest, but the highest mutation rate in the skin melanoma is CpCCpT and TpCTpT.b). The mutation of renal carcinoma subtypes is characterized mainly by CpGTpG, and the renal clear cell carcinoma is mainly CCGCAG, CCGCTG and GCGGTG, while the renal papillary cell carcinoma is in the renal clear cell carcinoma mutation type. On the basis of the CCACAA and CCCCAC.2. driving genes, 7 kinds of tumors, including adrenocortical cancer, cholangiocarcinoma, FPPP, renal chromophobe cell carcinoma, diffuse large B cell lymphoma, uterine carcinosarcoma and testicular germ cell tumor, were not identified, and dJ/dS2.0 was identified in 26 of the driving genes. It is worth noting that endometrial cancer appears much more. Up to 344 genes, we suspect that TCGA data may have abnormal or part of the gene in endometrial cancer more prone to splicing region mutation, resulting in dJ/dS2.0 insensitivity and lead to excessive false positive. Therefore, we remove the identification of endometrial cancer and use only the results of the remaining 25 tumors for subsequent analysis of.A) the driving of 25 kinds of tumors. The gene dJ/dS2.0 identified 643 non redundant tumor driven genes in 25 solid tumors. In addition to 73 CGC annotated as driving genes, another 570 (88.8%) was a newly predicted drive gene; b) GO functional analysis carried out GO clustering analysis of 570 driving genes. The results showed that many genes were aggregated in development and maintenance. Cytoplasmic related GO term (GO:0044243, multicellular organismal catabolic process, p=2.53E-12, FDR=1.52E-8). (two) functional analysis of the driving genes in liver cancer 1. the driving genes of the liver cancer in order to more comprehensively analyze the function of the driving genes, we combine the data of the liver cancer in TCGA and ICGC to increase the sample size, in the application of dJ/dS2. 0 at the same time, we also used the MutSig2.0 method developed by our lab group dT/dS and TCGA respectively. Three methods were used to identify 89 driving genes. The 89 genes were found to be enriched in 10 signal pathways related to virus infection or tumor by KEGG pathway analysis. The characteristics of the mutation types of the 2. liver cancer driving genes were compared. At present, the mutation types of liver cancer drive genes are different in proportion. In CTNNB1, TP53 and RB1 genes with higher mutation frequency, the proportion of non synonymous mutations is more than 95%, of which CTNNB1 and TP53 are mainly missense mutations, while insertion loss, missense mutation, splicing region mutation and nonsense mutation coexist in RB1; 3. and sex influence drivers Distribution genes TP53 and RB1 and pathway hsa05161 and hsa05203 are more likely to be mutated in Asian descent. The incidence of CTNNB1, ALB, PIK3CA, BAP1 and hsa05200 pathways in men is higher than that of women; 4. survival analysis, by single factor screening and multifactor analysis, gene KCNB2 (p=0.025), KCNJ12 (KCNJ12), etc., and If the hepatitis B infection pathway hsa05161 (p=0.006) mutation occurs, the median survival time of the patients is shortened and the mortality risk is increased. Conclusion the first chapter: dJ/dS2.01. improves the dJ/dS algorithm and improves the accuracy; 2. the driving genes identified by dJ/dS2.0 are involved in the development of multicellular and maintenance rings; and the second chapter: functional analysis of the driving gene in liver cancer 1. The sex and ethnicity of the patients with liver cancer may affect the distribution of the driving gene mutation; the 2. driven gene KCNB2, KCNJ12, RB1, TP53 and the hsa05161 mutation in the hepatitis B infection pathway are adverse factors for the prognosis of liver cancer, which may be a potentially clinical marker.
【學位授予單位】：南方醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R735.7

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