本文選題：大腸癌 + 代謝組學　； 參考：《吉林大學》2017年碩士論文

【摘要】：目的:尋找有無淋巴結轉移的大腸癌患者血清中差異性小分子代謝產物,探索代謝產物對不同淋巴結轉移狀態(tài)大腸癌的區(qū)分能力;初步探索小分子代謝產物對大腸癌淋巴結轉移狀態(tài)預測的生物學機制,為代謝產物對大腸癌淋巴結轉移狀態(tài)的預測提供理論依據(jù);基于代謝產物和臨床病理資料用邏輯回歸建立大腸癌淋巴結轉移預測模型。方法:1、運用色譜質譜儀器(LC-MS)對上清液進行檢測,得到不同淋巴結分期患者代謝產物的表達豐度值,尋找具有差異性的代謝產物。2、根據(jù)篩選出的差異性代謝產物對單獨樣本進行分組,應用免疫組化染色(Immunohistochemistry,IHC)技術檢測各組癌組織中VEGF-C、VEGF-D表達,癌旁組織淋巴管密度(LVD),應用酶聯(lián)免疫吸附法(ELISA)測定各組樣本血清中VEGF-C、VEGF-D含量。3、用多因素邏輯回歸篩統(tǒng)計方法篩選出大腸癌淋巴結轉移的獨立危險因素,并建立大腸癌淋巴結轉移邏輯回歸模型。4、代謝組學數(shù)據(jù)運用主成分分析、t檢驗、判別分析、聚類分析等方法對數(shù)據(jù)進行統(tǒng)計分析,免疫組化數(shù)據(jù)應用統(tǒng)計軟件SPSS17.0對數(shù)據(jù)進行統(tǒng)計分析。結果:1、通過應用高效液相色譜-質譜技術分別對50例T3N0M0和T3N1-3M0期大腸癌患者血清中代謝產物進行測定,經統(tǒng)計學分析,找到6種差異性代謝產物,這6種差異性代謝產物能很好判別大腸癌淋巴結轉移狀態(tài)。2、基于6種差異性代謝產物對56例單獨T3N0-3M0期大腸癌患者血清樣本進行分組,可大致將樣本分為A、B兩組,B組中淋巴結轉移陽性樣本顯著多于A組(P=0.000),B組樣本癌組織中的VEGF-C(P=0.007)、D(P=0.014)水平和癌旁組織淋巴管密度(P=0.001)均顯著高于A組,A、B兩組樣本血清中VEGF-C(P=0.339)和VEGF-D(P=0.451)濃度差異無統(tǒng)計學意義,A組患者總的生存率顯著高于B組(P=0.04)。3、將6種差異性代謝產物和臨床病理資料通過多因素邏輯回歸篩選出大腸癌淋巴結轉移的6種獨立危險因素,并根據(jù)獨立危險因素建立風險評分模型,該模型對大腸癌淋巴結轉移具有良好預測能力。結論:1.不同淋巴結轉移狀態(tài)大腸癌患者血清中存在差異性代謝產物;2.代謝產物可以對大腸癌患者不同淋巴結轉移狀態(tài)進行較好區(qū)分;3.對大腸癌淋巴結轉移狀態(tài)有區(qū)分能力的代謝產物與癌組織中VEGF-C、D的表達和癌旁組織新生淋巴管具有相關性;4.根據(jù)臨床病理資料和代謝產物用邏輯回歸選出Tyramine、Docosahexaenoic acid、Calcitroic acid、Glucosylsphingosine、Lyso PE(24:1(15Z)/0:0)、Ki-67六個獨立危險因素;5.根據(jù)六種獨立危險因素所建立的Logistic回歸模型對大腸癌淋巴結轉移狀態(tài)具有良好預測能力。
[Abstract]:Objective: to explore the differential small molecule metabolites in the serum of colorectal cancer patients with or without lymph node metastasis, and to explore the ability of the metabolites to differentiate colorectal cancer with different lymph node metastases. To explore the biological mechanism of small molecular metabolites in predicting lymph node metastasis status of colorectal cancer, and to provide theoretical basis for predicting lymph node metastasis status of colorectal cancer by metabolites. Based on metabolites and clinicopathological data, a predictive model for lymph node metastasis of colorectal cancer was established by logical regression. Methods: 1. The supernatant was detected by the chromatographic mass spectrometer (LC-MS), and the expression abundance of metabolites in patients with different lymph node stages was obtained. To find the different metabolite. 2. To group the individual samples according to the different metabolites, and to detect the VEGF-D expression in the tissues of each group by immunohistochemical staining. Lymphatic vessel density (LVD) in adjacent tissues was determined by Elisa. Serum VEGF-CnVEGF-D was determined by Elisa. The independent risk factors for lymph node metastasis of colorectal carcinoma were screened by multivariate logistic regression screening. The logistic regression model of lymph node metastasis of colorectal cancer was established. The main component analysis (PCA), discriminant analysis and cluster analysis were used to analyze the data of metabonomics. Immunohistochemical data were analyzed by statistical software SPSS17.0. Results by using high performance liquid chromatography-mass spectrometry (HPLC / MS), the metabolites in serum of 50 patients with colorectal cancer in T3N0M0 and T3N1-3M0 stage were determined, and six different metabolites were found by statistical analysis. These six different metabolites can distinguish the lymph node metastasis status of colorectal cancer. Based on the six different metabolites, 56 serum samples of patients with T3N0-3M0 stage colorectal cancer were divided into two groups. It can be roughly divided into two groups: the positive samples of lymph node metastasis in group B are significantly higher than those in group A (P = 0.000) and the levels of VEGF-CnP 0.007 (P _ (0.014)) and the density of lymphatic vessels in adjacent tissues (P _ (0.001)) are significantly higher than those in group A (group A) and group A (AB) (0.339) and VEGF-DU (0.451)). The overall survival rate of patients in group A was significantly higher than that in group B (P 0.04). Six independent risk factors for lymph node metastasis of colorectal cancer were screened by multivariate logistic regression based on 6 different metabolites and clinicopathological data. Risk scoring model was established according to independent risk factors. The model has good predictive ability for lymph node metastasis of colorectal cancer. Conclusion 1. There are different metabolites in the serum of colorectal cancer patients with different lymph node metastases. Metabolites can distinguish different lymph node metastasis status of colorectal cancer patients. The metabolites which have the ability to distinguish the metastatic status of colorectal cancer are related to the expression of VEGF-CfD and the new lymphatic vessels in the adjacent tissues of colorectal carcinoma. Based on the clinicopathological data and metabolites, six independent risk factors of Tyramineus Docosahexaenoic acido Calcitroic acido Glucosylsphingosine Lyso 24: 1 / 0: 0% Ki-67 were selected. The Logistic regression model based on six independent risk factors can predict lymph node metastasis of colorectal cancer.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R735.34

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