發(fā)布時(shí)間：2018-05-23 19:20

  本文選題：長鏈非編碼RNA + 預(yù)后分析模型�。� 參考：《華東師范大學(xué)》2017年碩士論文

【摘要】：膀胱癌是全球第九大常見的惡性腫瘤。膀胱尿路上皮癌(Bladder Urothelial Carcinoma,BLCA)是膀胱癌中最常見的病理類型。大多數(shù)膀胱癌病人都是在晚期才被診斷出來,并且5年生存率只有50～60%。因此,挖掘新型膀胱癌生物標(biāo)記對于膀胱癌病人的早期診斷、治療以及預(yù)后具有十分重要的意義。近年來,大量研究表明長鏈非編碼RNA(long non-coding RNA,lncRNA)在癌癥的發(fā)生、發(fā)展過程中起著十分關(guān)鍵的作用。但現(xiàn)如今基于lncRNA表達(dá)水平的,用于預(yù)測膀胱癌病人預(yù)后生存的分析模型尚未被研究。在本研究中,我們從癌癥基因組圖譜計(jì)劃(The Cancer Genome Atlas,TCGA)的數(shù)據(jù)庫中收集了 234個(gè)膀胱癌病人的lncRNA的表達(dá)數(shù)據(jù)和臨床信息數(shù)據(jù),對其進(jìn)行了綜合分析研究。首先把整個(gè)數(shù)據(jù)集隨機(jī)分為訓(xùn)練集和測試集。然后在訓(xùn)練集中,利用單因素Cox回歸分析,挖掘出4個(gè)與膀胱癌病人生存顯著相關(guān)的預(yù)后lncRNA。隨后利用這4個(gè)lncRNA構(gòu)建了一個(gè)可以有效預(yù)測膀胱癌病人預(yù)后生存的分析模型。利用這個(gè)模型可以把訓(xùn)練集中膀胱癌病人顯著的分為高風(fēng)險(xiǎn)組和低風(fēng)險(xiǎn)組,并且此預(yù)后分析模型的預(yù)測能力在測試集和整個(gè)數(shù)據(jù)集中得到了進(jìn)一步驗(yàn)證。然后利用多因素Cox回歸分析和分層生存分析證明了由這4個(gè)lncRNA構(gòu)建的膀胱癌預(yù)后分析模型是獨(dú)立于其它臨床變量的,包括病人年齡,病人性別,癌癥時(shí)期和癌癥亞型。最后對這4個(gè)預(yù)后lncRNA進(jìn)行了 GO功能富集分析和KEGG通路分析,揭示了它們可能參與了已知與膀胱癌發(fā)生相關(guān)的生物學(xué)過程和通路。綜上所述,我們的研究結(jié)果表明,由這4個(gè)lncRNA構(gòu)建的膀胱癌預(yù)后分析模型可以作為預(yù)測膀胱癌病人預(yù)后生存的新型生物標(biāo)記。
[Abstract]:Bladder cancer is the ninth most common malignant tumor in the world. Bladder Urothelial carcinoma (BLCA) is the most common pathological type of bladder cancer. Most bladder cancer patients are diagnosed at an advanced stage, and the 5-year survival rate is only 50 to 60. Therefore, it is very important to excavate new biomarkers for early diagnosis, treatment and prognosis of bladder cancer patients. In recent years, a large number of studies have shown that long chain noncoding RNA(long non-coding RNAs (LNRNAs) play a key role in the carcinogenesis and development of cancer. However, analysis models based on lncRNA expression level to predict survival of bladder cancer patients have not been studied. In this study, we collected 234 bladder cancer patients' lncRNA expression data and clinical information data from the Cancer Genome Atlas TCGA database of the Cancer Genome Map Project. First, the whole data set is randomly divided into training set and test set. Then, in the training concentration, the single factor Cox regression analysis was used to find out four prognostic factors, lncRNA, which were significantly related to the survival of bladder cancer patients. Then, using these four lncRNA, an analytical model was constructed to predict the survival of bladder cancer patients. Using this model, patients with bladder cancer can be significantly divided into high risk group and low risk group, and the predictive ability of this prognostic analysis model has been further verified in the test set and the whole data set. Then multivariate Cox regression analysis and stratified survival analysis showed that the prognostic analysis models of bladder cancer constructed by these four lncRNA were independent of other clinical variables, including patient age, patient sex, cancer stage and cancer subtype. Finally, go functional enrichment analysis and KEGG pathway analysis of the four prognostic lncRNA showed that they may be involved in the known biological processes and pathways related to the development of bladder cancer. In conclusion, our results suggest that the prognostic analysis models constructed by the four lncRNA can be used as a new biomarker to predict the survival of bladder cancer patients.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R737.14
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本文編號：1926018