本文選題：胰腺星狀細胞 + 胰腺癌��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：第一部分靜止幾活化胰腺星狀細胞差異表達基因的篩選及生物信息學(xué)分析目的:胰腺癌具有發(fā)現(xiàn)晚、進展快、可手術(shù)比例小、高耐藥率以及高病死率的特點,被稱為“癌癥之王”,而其顯著病理特點為:以PSC為中心,ECM主導(dǎo)的胰腺癌組織纖維結(jié)締組織生成。隨著基因技術(shù)、信息技術(shù)的飛速發(fā)展,大數(shù)據(jù)時代的到來,生物信息學(xué)這一門跨學(xué)科手段成為人們從宏觀角度解讀疾病的又一利器。因此我們采用這一手段,篩選靜止及活化狀態(tài)下PSC的差異表達基因并對其進行生物信息學(xué)分析。方法:選取北京協(xié)和醫(yī)院8例經(jīng)病理確診為胰腺導(dǎo)管腺癌患者的癌組織原代培養(yǎng)PSC。連續(xù)5天每天加入ATRA避光培養(yǎng)獲得靜止狀態(tài)PSC,分別提取靜止及活化狀態(tài)下PSC基因組進行測序分析,從中篩選出DEG,利用包括GO分析和KEGG生物學(xué)通路富集分析在內(nèi)的生物信息學(xué)方法,進一步探索DEG間的生物關(guān)聯(lián)及作用。結(jié)果:從33289條基因中篩選出192個DEG,其中上調(diào)表達基因109個,下調(diào)表達基因83個,其中排在前三位的下調(diào)基因是:HR、RP11-624L4.1和FGF9。GO富集分析生物過程BP部分排在前2位的是:細胞外結(jié)構(gòu)組織和細胞外基質(zhì)組織;CC部分排在前3位的是:細胞外基質(zhì)、蛋白質(zhì)細胞外基質(zhì)和間質(zhì)基質(zhì);MF部分排在前3位的是硫化物結(jié)合、肝素結(jié)合和生長因子。KEGG通路分析占首位的是TGF-β通路。將BP、CC、MF三個部分,按P值進行從小到大排序可發(fā)現(xiàn)細胞外基質(zhì)和生長因子在PSC活化過程中最為關(guān)鍵。統(tǒng)計各類別中所包含基因,按照基因出現(xiàn)頻率進行排序,出現(xiàn)頻率≥4的共有18個基因,結(jié)合DEG篩選結(jié)果,前三位DEG中,僅FGF9在GO富集分析結(jié)果中同樣較高頻率出現(xiàn)。結(jié)論:PSC靜止及活化狀態(tài)間的轉(zhuǎn)化由多種基因調(diào)控,當(dāng)PSC轉(zhuǎn)為靜止狀態(tài)后,以細胞外基質(zhì)、間質(zhì)、生長因子等相關(guān)成分基因下調(diào)為主,提示基質(zhì)在PSC活化中可能發(fā)揮了重要作用。選定FGF9作為擬驗證的候選基因。第二部分FGF9及其相關(guān)受體表達的RT-q PCR驗證目的:測序結(jié)果客觀且龐大,如果不做進一步的分析驗證則反而會使大量的數(shù)據(jù)成為“數(shù)據(jù)垃圾”,因此完成生物信息學(xué)分析后,我們根據(jù)第一部分實驗的結(jié)果,結(jié)合文獻和既往研究進行分析,從中篩選出在眾多DEG中占相對主導(dǎo)地位的基因進行驗證分析。而從上一部分我們已經(jīng)得到FGF9是PSC活化的關(guān)鍵基因,因此對FGF9及其相關(guān)受體基因進行驗證。方法:培養(yǎng)PSC,用ATRA進行去活化,分別提取活化PSC和去活化PSC中的RNA,進行反轉(zhuǎn)錄獲得c DNA,用q PCR進行候選基因驗證,候選基因選擇:FGF9及其密切相關(guān)的受體FGFR2c、FGFR3b和FGFR3c,確定第一部分篩選結(jié)果,進而為進一步探究DEG功能和機制奠定基礎(chǔ)。結(jié)果:FGF9、FGFR3b和FGFR3c的表達在ATRA去活化組較活化組顯著下調(diào),FGFR2c表達在ATRA去活化組較活化組顯著上調(diào)。結(jié)論:FGF9能夠促進PSC活化,FGFR3b和FGFR3c可能存在協(xié)同作用,FGFR2c在PSC活化中的作用仍不明確。
[Abstract]:Part I screening of differentially expressed genes in stationary activated pancreatic stellate cells and bioinformatics analysis objective: pancreatic cancer is characterized by late discovery, rapid progression, low operative rate, high drug resistance and high mortality.It is called the "king of cancer", and its prominent pathological characteristics are: connective tissue formation of pancreatic cancer tissue dominated by PSC.With the rapid development of gene technology and information technology and the arrival of big data's era, bioinformatics, as an interdisciplinary means, has become another powerful tool for people to interpret diseases from a macro perspective.So we used this method to screen the differentially expressed genes of PSC in stationary and activated state and to analyze them by bioinformatics.Methods: primary culture of PSCs was performed in 8 patients with pancreatic ductal adenocarcinoma diagnosed pathologically in Peking Union Hospital.For 5 consecutive days, the stationary PSC was obtained by adding ATRA in the dark culture every day, and the PSC genome was sequenced and analyzed in the inactive and activated state, respectively.The bioinformatics methods including go analysis and KEGG biological pathway enrichment analysis were used to further explore the biological association and function between DEG.Results: 192 DEG genes were screened from 33289 genes, of which 109 were up-regulated and 83 were down-regulated.Among them, the down-regulated genes in the first three places were: 1 / HRN RP11-624L4.1 and FGF9.GO enrichment analysis. The BP part of the biological process ranked first 2: extracellular structure tissue and extracellular matrix tissue (CC) were in the top three positions: extracellular matrix.The protein extracellular matrix (ECM) and interstitial matrix (MF) in the top 3 were sulfide-binding, while heparin binding and growth factor.KEGG pathway were the most important in TGF- 尾 pathway.It was found that extracellular matrix (ECM) and growth factor (GF) were the most critical in the activation of PSC by ranking the three parts of BPCCMF from small to large according to P value.According to the frequency of gene occurrences, there were 18 genes with frequency 鈮，

本文編號：1760596