食管基底细胞样鳞癌的基因芯片数据分析

发布时间：2018-05-05 02:14

本文选题：食管基底细胞样鳞癌 + 生物信息学　；参考：《河北医科大学》2017年硕士论文

【摘要】：目的:食管癌是我国的十大特色肿瘤之一,同时我省处于食管癌高发地带,且多数患者在就诊时已经错过了最佳的治疗阶段,虽然在对食管癌的病理及治疗手段研究有大宗的报道,但对其中一种较为罕见且恶性度较高的食管癌亚型:食管基底细胞样鳞癌(basaloid squamous cell carcinoma of the esophagus,BSCCE)的研究却受限于其罕见性,而鲜有报道,目前的研究表明BSCCE的最佳治疗手段是手术治疗,然而其远期生存率并不理想,为了进一步研究BSCCE的病理特征,挖掘可能存在的治疗靶点,从而提高临床治疗后的远期生存率,本研究利用生物信息学方法和基因芯片手段,对BSCCE的组织样本基因水平表达情况进行分析,探讨其可能的病理学过程和肿瘤免疫逃逸机制,同时对BSCCE独特的肿瘤微环境及其临床指标在对其远期预后的病理基础进行讨论,以期为临床工作中更好的治疗BSCCE提供一定的理论基础。方法:本实验利用高通量的生物信息学手段,将BSCCE组织样本与正常组织样本的基因表达情况进行对比分析,通过样本送检获得原始的基因芯片数据,利用R语言软件(R 3.2.3)完成质量控制,数据归一化和降噪处理,自主获取真实可靠的BSCCE组织样本与同体正常组织样本的基因差异表达情况(log|FC|≥2,P0.01),利用DAVID在线分析系统对差异表达基因进行常规的基因本体论(gene ontology,GO)分析和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)信号通路分析以及差异基因疾病分析(Disease)获取对BSCCE组织病理情况相关的分析结果,利用STRING-OL工具对差异表达基因的可信度(可信度≥0.4)进行筛选,在尽力降低假阳性可能的情况下获取差异表达基因的蛋白质相互作用分析(Protein-protein interaction,PPI),并进一步利用Cytoscape软件对筛选后的PPI结果制作相应的基因相互作用网状图,并进一步筛选核心表达的差异基因。利用以上分析进一步深入理解BSCCE的病理学状态,对其独特的是中性粒细胞侵入原因及恶性远期预后进行了一定分析。结果:BSCCE组织相对于正常组织的上调差异表达基因共489个,下调差异表达基因922个(log|FC|≥2,P0.01)。1 GO分析:BSCCE组织相对于正常组织的上调表达基因GO分析结果共238条,选取可信度较高(P0.01,FDR0.01)的19条进行分析,为了进一步分析BSCCE可能的病理改变或肿瘤微环境,将GO分析中细胞外或细胞基质等作为主要的分析对象,其结果显示上下调表达的差异基因中与细胞外或细胞外基质相关的分析条目的可信度与参与基因数都远远超过其他GO条目(尤其在下调差异基因部分),其中上调表达的差异基因中GO分析结果为:GO:0030574-collagen catabolic process,16个参与基因,GO:0030198-extracellular matrix organization 24个参与基因,GO:0005578-proteinaceous extracellular matrix 25个参与基因,下调表达的差异基因中GO分析结果为:GO:0070062-extracellular exosome 117个参与基因,GO:0005615-extracellular space 89个参与基因,GO:0005576-extracellular region 90个参与基因,对上调的GO分析条目中所包含的差异基因进行进一步分析,发现了几个在BSCCE肿瘤组织中拥有肿瘤标志物潜力或对远期临床预后会有较大帮助的基因,其中包括:ITGB4、COL5A1、LAMB3等13个基因。2 KEGG分析:在这一分析中提取了几个可能与肿瘤病理及免疫微环境有重要关联的信号通路,其中上调表达的差异基因分析结果为:p53signaling pathway,Toll-like receptor signaling pathway等。下调表达的差异基因分析结果包括:Drug metabolism-cytochrome P450 pathway,MAPK signaling pathway,Calcium signaling pathway,Cytokine-cytokine receptor interaction pathway。结果筛选出LAMB3、LAMC2、MFAP2等11个基因。3疾病相关分析:进一步对可能与肿瘤病理相关的605027-Lymphoma,non-Hodgkin,somatic分析结果及其参与基因RAD54B,RAD54L进行了重点分析。4核心差异基因筛选及分析:在利用STRING-OL工具完成高可信度PPI分析后,筛选其中相互作用数目≥20的差异表达基因作为核心差异基因共30个,其中上调的24个,下调的6个,并对其进行了逐个的分析,筛选获得TOP2A、CDC20、CDC6等15个基因。结论:我们的分析结果共筛选肿瘤相关基因25个,其中GO分析获得ITGB4、LAMB3、ITGA6、TGFBI、LAMC2、MFAP2,胶原蛋白家族COL5A1、COL7A1、COL1A1、COL1A2、胶原蛋白酶家族(MMP1、3、9)共13个基因;KEGG分析获得LAMB3、LAMC2、MFAP2、胶原蛋白酶家族(MMP1、3、9)、TOP2A、CDC20、CDC6、CDC25A、CYP1B1共11个基因;PPI分析的结果提示我们TOP2A、CDC20、CDC6、CDC25A、CYP1B1、COL1A1、COL1A2、MMP9、AURKA、CCNA2、BUB1、CDCA5、CCNE1、MCM2、NEK2共15个基因,这些基因在BSCCE的肿瘤病理指标,远期生存率预测诊断以及肿瘤基因标靶治疗等研究中值得进一步深入研究。
[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.

【学位授予单位】：河北医科大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R735.1

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