本文選題：食管基底細胞樣鱗癌 + 生物信息學　； 參考：《河北醫(yī)科大學》2017年碩士論文

【摘要】：目的:食管癌是我國的十大特色腫瘤之一,同時我省處于食管癌高發(fā)地帶,且多數(shù)患者在就診時已經錯過了最佳的治療階段,雖然在對食管癌的病理及治療手段研究有大宗的報道,但對其中一種較為罕見且惡性度較高的食管癌亞型:食管基底細胞樣鱗癌(basaloid squamous cell carcinoma of the esophagus,BSCCE)的研究卻受限于其罕見性,而鮮有報道,目前的研究表明BSCCE的最佳治療手段是手術治療,然而其遠期生存率并不理想,為了進一步研究BSCCE的病理特征,挖掘可能存在的治療靶點,從而提高臨床治療后的遠期生存率,本研究利用生物信息學方法和基因芯片手段,對BSCCE的組織樣本基因水平表達情況進行分析,探討其可能的病理學過程和腫瘤免疫逃逸機制,同時對BSCCE獨特的腫瘤微環(huán)境及其臨床指標在對其遠期預后的病理基礎進行討論,以期為臨床工作中更好的治療BSCCE提供一定的理論基礎。方法:本實驗利用高通量的生物信息學手段,將BSCCE組織樣本與正常組織樣本的基因表達情況進行對比分析,通過樣本送檢獲得原始的基因芯片數(shù)據(jù),利用R語言軟件(R 3.2.3)完成質量控制,數(shù)據(jù)歸一化和降噪處理,自主獲取真實可靠的BSCCE組織樣本與同體正常組織樣本的基因差異表達情況(log|FC|≥2,P0.01),利用DAVID在線分析系統(tǒng)對差異表達基因進行常規(guī)的基因本體論(gene ontology,GO)分析和京都基因與基因組百科全書(kyoto encyclopedia of genes and genomes,KEGG)信號通路分析以及差異基因疾病分析(Disease)獲取對BSCCE組織病理情況相關的分析結果,利用STRING-OL工具對差異表達基因的可信度(可信度≥0.4)進行篩選,在盡力降低假陽性可能的情況下獲取差異表達基因的蛋白質相互作用分析(Protein-protein interaction,PPI),并進一步利用Cytoscape軟件對篩選后的PPI結果制作相應的基因相互作用網狀圖,并進一步篩選核心表達的差異基因。利用以上分析進一步深入理解BSCCE的病理學狀態(tài),對其獨特的是中性粒細胞侵入原因及惡性遠期預后進行了一定分析。結果:BSCCE組織相對于正常組織的上調差異表達基因共489個,下調差異表達基因922個(log|FC|≥2,P0.01)。1 GO分析:BSCCE組織相對于正常組織的上調表達基因GO分析結果共238條,選取可信度較高(P0.01,FDR0.01)的19條進行分析,為了進一步分析BSCCE可能的病理改變或腫瘤微環(huán)境,將GO分析中細胞外或細胞基質等作為主要的分析對象,其結果顯示上下調表達的差異基因中與細胞外或細胞外基質相關的分析條目的可信度與參與基因數(shù)都遠遠超過其他GO條目(尤其在下調差異基因部分),其中上調表達的差異基因中GO分析結果為:GO:0030574-collagen catabolic process,16個參與基因,GO:0030198-extracellular matrix organization 24個參與基因,GO:0005578-proteinaceous extracellular matrix 25個參與基因,下調表達的差異基因中GO分析結果為:GO:0070062-extracellular exosome 117個參與基因,GO:0005615-extracellular space 89個參與基因,GO:0005576-extracellular region 90個參與基因,對上調的GO分析條目中所包含的差異基因進行進一步分析,發(fā)現(xiàn)了幾個在BSCCE腫瘤組織中擁有腫瘤標志物潛力或對遠期臨床預后會有較大幫助的基因,其中包括:ITGB4、COL5A1、LAMB3等13個基因。2 KEGG分析:在這一分析中提取了幾個可能與腫瘤病理及免疫微環(huán)境有重要關聯(lián)的信號通路,其中上調表達的差異基因分析結果為:p53signaling pathway,Toll-like receptor signaling pathway等。下調表達的差異基因分析結果包括:Drug metabolism-cytochrome P450 pathway,MAPK signaling pathway,Calcium signaling pathway,Cytokine-cytokine receptor interaction pathway。結果篩選出LAMB3、LAMC2、MFAP2等11個基因。3疾病相關分析:進一步對可能與腫瘤病理相關的605027-Lymphoma,non-Hodgkin,somatic分析結果及其參與基因RAD54B,RAD54L進行了重點分析。4核心差異基因篩選及分析:在利用STRING-OL工具完成高可信度PPI分析后,篩選其中相互作用數(shù)目≥20的差異表達基因作為核心差異基因共30個,其中上調的24個,下調的6個,并對其進行了逐個的分析,篩選獲得TOP2A、CDC20、CDC6等15個基因。結論:我們的分析結果共篩選腫瘤相關基因25個,其中GO分析獲得ITGB4、LAMB3、ITGA6、TGFBI、LAMC2、MFAP2,膠原蛋白家族COL5A1、COL7A1、COL1A1、COL1A2、膠原蛋白酶家族(MMP1、3、9)共13個基因;KEGG分析獲得LAMB3、LAMC2、MFAP2、膠原蛋白酶家族(MMP1、3、9)、TOP2A、CDC20、CDC6、CDC25A、CYP1B1共11個基因;PPI分析的結果提示我們TOP2A、CDC20、CDC6、CDC25A、CYP1B1、COL1A1、COL1A2、MMP9、AURKA、CCNA2、BUB1、CDCA5、CCNE1、MCM2、NEK2共15個基因,這些基因在BSCCE的腫瘤病理指標,遠期生存率預測診斷以及腫瘤基因標靶治療等研究中值得進一步深入研究。
[Abstract]:Objective: esophageal cancer is one of the ten most distinctive tumors in China. At the same time, our province is in the high incidence area of esophageal cancer, and most patients have missed the best treatment stage. Although there are a large number of reports on the pathology and treatment of esophageal cancer, one of the more rare and high malignant subtypes of esophageal cancer: food. The study of basaloid squamous cell carcinoma of the esophagus, BSCCE is limited by its rare nature, but rarely reported. The present study shows that the best treatment for BSCCE is surgical treatment. However, the long-term survival rate is not ideal. In order to study the pathological features of BSCCE, the possible treatment can be found. To improve the long-term survival rate of the clinical treatment, this study uses bioinformatics and gene chip methods to analyze the expression of gene level in the tissue samples of BSCCE, to explore the possible pathological process and the mechanism of tumor immune escape. At the same time, the unique tumor microenvironment and its clinical indicators of BSCCE are also discussed. The pathological basis of the long-term prognosis is discussed in order to provide a theoretical basis for the better treatment of BSCCE in clinical work. Methods: this experiment uses high throughput bioinformatics to analyze the gene expression of BSCCE tissue samples and normal tissue samples, and obtain the original gene chip by sample inspection. Data, using the R language software (R 3.2.3) to complete the quality control, data normalization and noise reduction processing, independently obtain the true and reliable BSCCE tissue samples and the gene differential expression of the normal tissue samples (log|FC| > 2, P0.01), and use the DAVID online analysis system to carry out the conventional gene Ontology (gene ontology, G) for the differentially expressed genes. O) analysis and the analysis of Kyoto Encyclopedia of genes and genomes, KEGG) signal pathway analysis and differential gene disease analysis (Disease) to obtain the analysis of the pathology of BSCCE tissue, using STRING-OL tool to screen the reliability of differentially expressed genes (reliability > 0.4), and try to do the best. The protein interaction analysis of differentially expressed genes (Protein-protein interaction, PPI) was obtained when the false positive was reduced, and the Cytoscape software was used to make the corresponding gene interaction network map of the selected PPI results, and further screening the differentially expressed genes in the core. In understanding the pathological state of BSCCE, a specific analysis of the causes of neutrophils invasion and malignant long-term prognosis was made. Results: there were 489 differentially expressed genes up regulation of BSCCE tissues relative to normal tissues, and 922 down regulated differentially expressed genes (log|FC| > 2, P0.01).1 GO analysis: up regulation of BSCCE tissues relative to normal tissues In order to further analyze the possible pathological changes of BSCCE or the microenvironment of the tumor, the GO analysis results of the expression gene GO were analyzed. In order to further analyze the possible pathological changes of BSCCE or the microenvironment of the tumor, the extracellular or cellular matrix in the GO analysis was used as the main analysis object. The results showed that the differentially expressed genes were obviously down to the cells or cells. The reliability and number of involved genes involved in the matrix related analysis were far more than the other GO entries (especially in the down-regulation of differential genes), in which the GO analysis results of the differentially expressed genes were GO:0030574-collagen catabolic process, 16 participating genes, GO: 0030198-extracellular matrix organization 24 participating genes, G, G. O:0005578-proteinaceous extracellular matrix 25 participates in genes, and the GO analysis results of down regulated differentially expressed genes are: GO:0070062-extracellular exosome 117 participating genes, GO:0005615-extracellular space 89 participating genes, GO:0005576-extracellular region 90 participating genes, and package of GO analysis items up to up Further analysis of the differentially expressed genes found several genes that have the potential for tumor markers in BSCCE tumor tissues or are of great help to the long-term clinical prognosis, including 13 genes, such as ITGB4, COL5A1, LAMB3, and.2 KEGG analysis. In this analysis, several factors may be important for tumor pathology and immune microenvironment. P53signaling pathway, Toll-like receptor signaling pathway and so on. The results of differential gene analysis of down regulated expression include: Drug metabolism-cytochrome P450 pathway, MAPK signaling. Action pathway. results screened 11 genes related to.3 disease related to LAMB3, LAMC2, MFAP2 and other genes: 605027-Lymphoma, non-Hodgkin, somatic analysis and RAD54B, which may be associated with tumor pathology, and RAD54L on the screening and analysis of.4 core differential genes. After PPI analysis, a total of 30 differentially expressed genes with the number of more than 20 of the interaction were selected as the core differentially expressed genes, including 24 up and 6 down regulated genes, and 15 genes were screened for TOP2A, CDC20, and CDC6. Conclusion: our analysis results were screened for a total of 25 tumor related genes, of which GO analysis obtained I TGB4, LAMB3, ITGA6, TGFBI, LAMC2, MFAP2, collagen family COL5A1, COL7A1, COL1A1, COL1A2, collagenase family (MMP1,3,9) a total of 13 genes. COL1A1, COL1A2, MMP9, AURKA, CCNA2, BUB1, CDCA5, CCNE1, MCM2, NEK2 are 15 genes. These genes are worthy of further study in BSCCE tumor pathological indicators, forward survival predictive diagnosis and tumor gene target therapy.

【學位授予單位】：河北醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R735.1

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本文編號：1845725