本文選題：磁共振彌散加權(quán)成像　切入點(diǎn)：表觀彌散系數(shù)　出處：《天津醫(yī)科大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：第一部分DW-MRI紋理分析聯(lián)合血清miR-1290建立預(yù)測(cè)模型目的:研究基于磁共振彌散加權(quán)成像(Diffusion-weighted magnetic resonance imaging,DW-MRI)的表觀彌散系數(shù)(apparent diffusion coefficients,ADC)圖像紋理分析方法,聯(lián)合血清microRNA-1290(mi R-1290),建立高效預(yù)測(cè)食管鱗癌(esophageal squamous cell carcinoma,ESCC)放化療敏感性的模型,指導(dǎo)ESCC個(gè)體化治療。方法:43例病理證實(shí)的ESCC患者接受根治性同步放化療,治療結(jié)束1月后根據(jù)RECIST標(biāo)準(zhǔn)評(píng)價(jià)療效。治療前采集DW-MRI圖像及進(jìn)行血清miR-1290含量檢測(cè)。對(duì)腫瘤各層面靶區(qū)進(jìn)行勾畫,并進(jìn)行三維(3D)圖像重建。分別對(duì)最大層面靶區(qū)及重建的圖像進(jìn)行2D和3D的紋理分析。紋理參數(shù)的提取方法選用直方圖、強(qiáng)度-尺度-區(qū)域矩陣(intensity-size-zone matrix)、灰度共生矩陣(gray level co-occurrence matrix,GLCM)、灰度梯度共生矩陣(gray level gradient co-occurrence matrix,GLGCM)四種紋理分析方法。根據(jù)療效評(píng)價(jià)結(jié)果,選用Mann-Whitney U Test篩選在敏感組和抗拒組中有統(tǒng)計(jì)學(xué)差異的參數(shù),應(yīng)用主成分分析(principal component analysis,PCA)進(jìn)行參數(shù)降維,最后將降維后的成分利用人工神經(jīng)網(wǎng)絡(luò)(artificial neural network,ANN)及k最近鄰(k-nearest neighbor,k-NN)方法建立預(yù)測(cè)模型,利用交叉驗(yàn)證(cross-validation)及四格表法(McNemar’s test)進(jìn)行模型驗(yàn)證。同時(shí)利用降維后紋理參數(shù)聯(lián)合患者血清miR-1290含量,利用上述建模方法,最終建立預(yù)測(cè)模型并進(jìn)行校驗(yàn)。結(jié)果:根據(jù)RECIST標(biāo)準(zhǔn)判定患者治療療效,29例患者為敏感組(CR+PR),14例患者為(SD+PD)。通過Mann-Whitney U Test篩選,2D紋理參數(shù)中15個(gè)參數(shù)能夠區(qū)分敏感組與抗拒組,3D紋理參數(shù)中18個(gè)紋理參數(shù)能夠區(qū)分敏感組與抗拒組。PCA降維后,2D紋理分析利用ANN及k-NN建模準(zhǔn)確率為65.1%及67.4%,3D紋理分析利用ANN及k-NN建模準(zhǔn)確率為76.7%及79.1%。單獨(dú)miR-1290預(yù)測(cè)ESCC放化療敏感性的準(zhǔn)確率69.8%。最后,利用紋理參數(shù)PCA降維后3種主成分聯(lián)合血清miR-1290,通過ANN及k-NN再次建立預(yù)測(cè)模型,最終準(zhǔn)確率達(dá)90.7%及93%。結(jié)論:基于DWI-ADC圖像3D紋理模型在預(yù)測(cè)放化療敏感性方面較2D準(zhǔn)確性高。3D紋理模型聯(lián)合血清miR-1290可以建立較高預(yù)測(cè)效能的ESCC放化療敏感性預(yù)測(cè)模型。第二部分ESCC中miR-1290靶向NFIX調(diào)控機(jī)制的研究目的:通過體內(nèi)及體外細(xì)胞實(shí)驗(yàn)探討mi R-1290在ESCC中的作用機(jī)制。方法:在體內(nèi)實(shí)驗(yàn)中,利用實(shí)時(shí)定量PCR(qRT-PCR)和蛋白質(zhì)提取和免疫印跡試驗(yàn)(Western-blot)檢測(cè)40例ESCC腫瘤及瘤旁組織中miR-1290和NFIX的表達(dá),分析二者之間的關(guān)系。在體外細(xì)胞實(shí)驗(yàn)中,針對(duì)ECA-109及KYSE-410細(xì)胞系,轉(zhuǎn)染miR-1290mimics、inhibitor、NFIX vector、NFIX siRNA等過表達(dá)或降表達(dá)質(zhì)粒,調(diào)控miR-1290、NFIX的表達(dá);集落形成實(shí)驗(yàn)檢測(cè)細(xì)胞增值;流式細(xì)胞儀分析細(xì)胞周期;Transwell實(shí)驗(yàn)檢測(cè)細(xì)胞侵襲和遷移;qRT-PCR檢測(cè)miR-1290和NFIX mRNA;Western-blot免疫印跡法檢測(cè)NFIX蛋白水平。結(jié)果:miR-1290在ESCC組織中明顯升高,miR-1290在腫瘤與瘤旁組織中含量有明顯差異。NFIX在ESCC組織中明顯降低,miR-1290與NFIX含量呈明顯負(fù)相關(guān)。miR-1290與手術(shù)患者T分期、TNM分期呈明顯相關(guān),p值0.05。雙熒光素酶實(shí)驗(yàn)證實(shí)miR-1290直接作用于NFIX mRNA的3’-UTR端,miR-1290通過降解mRNA影響NFIX表達(dá)水平。集落形成實(shí)驗(yàn)證明mi R-1290通過靶向NFIX促進(jìn)ESCC細(xì)胞增值,細(xì)胞周期實(shí)驗(yàn)證明miR-1290通過靶向NFIX增加ESCC細(xì)胞S和G2/M期比例。Transwell實(shí)驗(yàn)證明miR-1290通過靶向NFIX促進(jìn)ESCC細(xì)胞遷移及侵襲。結(jié)論:ESCC患者中,miR-1290與患者T分期、TNM分期明顯相關(guān),miR-1290可通過降解NFIX mRNA影響NFIX蛋白表達(dá);miR-1290通過靶向NFIX促進(jìn)ESCC細(xì)胞增值、侵襲和遷移。
[Abstract]:The first part of the DW-MRI texture analysis combined with serum miR-1290 model objective: To study the magnetic resonance diffusion weighted imaging (Diffusion-weighted magnetic resonance based on imaging, DW-MRI) of apparent diffusion coefficient (apparent diffusion, coefficients, ADC) image texture analysis method, combined with serum microRNA-1290 (MI R-1290), the establishment of efficient prediction of esophageal squamous cell carcinoma (esophageal squamous cell carcinoma ESCC), chemotherapy sensitivity model, ESCC guide individualized treatment. Methods: 43 cases of pathologically confirmed ESCC patients underwent radical radiotherapy and chemotherapy, the curative effect after treatment ended in January according to the RECIST criteria. Before treatment, DW-MRI image acquisition and detection of serum miR-1290. The outline of each level and tumor target area. The three-dimensional (3D) image reconstruction. The image analysis respectively on the maximum level of target area and reconstruction of the 2D and 3D texture texture parameters. The extraction methods of histogram, intensity scale matrix (intensity-size-zone matrix) - regional, gray level co-occurrence matrix (gray level co-occurrence matrix, GLCM), gray gradient co-occurrence matrix (gray level gradient co-occurrence matrix, GLGCM) four kinds of texture analysis method. According to the clinical evaluation results, choose Mann-Whitney U Test have significant difference in the parameters selection sensitive group and resistant group, using principal component analysis (principal component, analysis, PCA) of parameter reduction, finally the reduced dimensionality of the components by using artificial neural network (artificial neural network, ANN) and K (k-nearest neighbor, k-NN nearest neighbor) method to establish prediction model, using cross validation (cross-validation) and four table method (McNemar s test) to validate the model. At the same time using low dimensional texture parameters combined with the content of miR-1290 in serum, using the above modeling method, The final prediction model is established and validated. Results: according to the RECIST criteria patients, 29 patients with sensitive group (CR+PR), 14 patients (SD+PD). The Mann-Whitney U Test screening, 15 parameters of 2D texture parameters can distinguish in sensitive group and resistant group, 18 texture 3D texture parameters parameters to distinguish between sensitive group and resistant group.PCA dimensionality reduction, 2D texture analysis using ANN and k-NN modeling accuracy is 65.1% and 67.4%, 3D texture analysis using ANN and k-NN modeling accuracy is 76.7% and 79.1%. alone miR-1290 prediction ESCC chemotherapy sensitivity accuracy 69.8%. finally, using texture parameters after the dimensionality reduction of PCA 3 the main component of combined serum miR-1290, ANN and k-NN through again to establish prediction model, the final accuracy of 90.7% and 93%. conclusion: DWI-ADC 3D texture model in predicting the chemotherapy sensitivity than 2D.3D texture model based on high accuracy Serum miR-1290 can establish a prediction of high performance ESCC chemotherapy sensitivity prediction model. The second part of the ESCC miR-1290 NFIX targeting objective regulation mechanism: To investigate the mechanism of MI R-1290 in ESCC cells by in vivo and in vitro experiments. Methods: in vivo experiments, using real time quantitative PCR (qRT-PCR) and protein extraction and Western blot (Western-blot) expression of miR-1290 and NFIX were detected in 40 patients with ESCC tumors and tumor tissues, to analyze the relationship between the two. In vitro experiments, for ECA-109 and KYSE-410 cells transfected with miR-1290mimics, inhibitor, NFIX, vector, NFIX and siRNA over expression or reduced expression plasmid, the regulation of miR-1290, NFIX the expression; colony forming cell proliferation assay; cell cycle was analyzed by flow cytometry; Transwell assay, cell migration and invasion; qRT-PCR miR-1290 and NFIX mRNA detection; W Detection of NFIX estern-blot protein level by Western blot. Results: miR-1290 was significantly higher in ESCC tissues, the content of miR-1290 in tumor and tumor adjacent tissues were significantly different.NFIX decreased significantly in ESCC tissues,.MiR-1290 were negatively correlated with T in patients with surgical stage miR-1290 and NFIX content was significantly related to TNM stage, the p value of 0.05. luciferase experiments confirmed that the direct effect of miR-1290 on NFIX mRNA -UTR 3 'end, the level of miR-1290 by influencing the expression of NFIX mRNA degradation. Colony formation assay demonstrated that MI R-1290 can promote the proliferation of ESCC cells by targeting NFIX, cell cycle experiments demonstrated that miR-1290 targeted by NFIX S and ESCC cells increased the proportion of G2/M phase.Transwell experiments show that miR-1290 promotes the migration and the invasion of ESCC cells by targeting NFIX. Conclusion: ESCC patients, miR-1290 patients with T staging, TNM staging was significantly related, miR-1290 can influence the degradation of NFI through NFIX mRNA The expression of X protein; miR-1290 promotes the proliferation, invasion and migration of ESCC cells by targeting NFIX.

【學(xué)位授予單位】：天津醫(yī)科大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：R735.1

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