摘要
肺癌是世界上病死率第一位的恶性肿瘤,虽然目前外科手术及联合治疗有了很大的进展,但欧美国家肺癌的5年生存率仍然约15%。在肺癌患者中,小细胞肺癌(small cell lung cancer, SCLC)约占15%,非小细胞肺癌(non-small cell lung cancer, NSCLC)约占80%,NSCLC主要包括腺癌(adenocarcinoma lung cancer)及鳞癌(squamous-cell lung cancer)两大类型。
肺癌的治疗方案因不同病理亚型而不同,早期NSCLC手术治疗后的5年生存率为40%,而大部分患者就诊时已处于进展期,已失去了外科手术和多学科根治的最佳时机,导致进展期肺癌的5年生存率仅为8个月。近年来,分子靶向治疗被认为是特异性最高的肺癌个体化治疗方案,但这就要求对肺癌的准确分型。但临床诊断过程中观察者之间的经验差异会导致肺癌的诊断缺乏特异性及准确性。肺癌的个体化治疗应根据肿瘤本身的生物学特性。因此,特异的肺癌分子表型研究有助于更准确地对肺癌进行分子分型、靶向治疗及预后判断。
miRNA (microRNA)是长度在18-25个左右核苷酸的内源性非编码小分子RNA。miRNA在进化上高度保守。miRNA具有多种生物学功能,对生长发育、生理功能尤其在恶性肿瘤的发生发展等过程产生重大影响。与mRNA相比,miRNA在体外更稳定,在体内代谢周期更长,因而更适合作为肿瘤标记物。
目前,绝大多数对miRNA表达进行的研究所用的都是整块的肿瘤组织,其杂交信号可以来自肺癌细胞,也可以来自肺癌组织中的炎症细胞、间质细胞甚至正常肺组织细胞,而这些非目的细胞的比例在不同肺癌组织中可以存在很大的差异,从而造成了结果的不可靠。激光捕获显微切割技术(Laser capture microdissection, LCM)成功地解决了分子生物学研究中组织异质性的问题,即可以迅速、准确地选择并捕获单一种类的靶细胞,甚至于单个细胞。目前而言,该技术已经被运用到了DNA分析和基因表达的研究中。然而该技术尚未在对实体肿瘤的miRNA生物标记物的发现中得以广泛运用。
本课题LCM技术获取纯净肿瘤细胞,与全基因miRNA检测相结合,探寻不同亚型肺癌的miRNA分子标记物。用石蜡标本对上述有表达差异的miRNA进行进一步的验证,旨在明确其在不同亚型肺癌分子诊断中的临床应用。
因为大多数miRNA是组织特异性,在体内长期存在并且在体外能够稳定保存。这就为寻找细胞、组织或循环中肿瘤细胞特异的miRNA标志物,为肿瘤的诊断、预后推测及靶向治疗提供新的理论依据。最新研究发现肿瘤组织来源的循环中的肿瘤细胞miRNA量对肿瘤的诊断是足够的。因此检测循环中的肿瘤细胞来源的miRNA将有可能成为肿瘤早期诊断的重要手段。到目前为止,肿瘤特异性的标志物尚有很大的局限,临床应用中血清中很多肿瘤指标(如CEA、CA199、CA125等)会出现很高的假阳性；同样血清中很多肿瘤指标(如AFP、NSE等)会出现假阴性。因此,较高频率的假阳性或假阴性导致血清中的肿瘤标志物作为临床肿瘤筛选的明显地局限性。因此,有必要寻找一种基于血循环的肿瘤分子标志物,提高它们的敏感性及特异性,以利于肿瘤的早期诊断及早期治疗,以降低肿瘤的死亡率。
近期关于肺癌miRNA的研究已有一些初步进展。并且发现几个miRNA与肺癌的预后密切相关。研究者也试图寻找血细胞或血清中的肺癌特异的miRNA,研究发现非小细胞肺癌患者血清中的miR-25及miR-223明显升高,且很容易用定量RT-PCR方法检测到。但是目前为止尚无其他肺癌血循环中miRNA肿瘤标志物的更深入的研究。
前期研究显示通过检测血浆或血清中的miRNA以诊断疾病是可行的、稳定的及具有可重复性的。因此,有必要也有可能寻找血循环中的特异的肿瘤标志物,以期建立一种无创、快速、稳定且经济的肺癌诊断方法。
本实验对不同亚型肺癌血浆进行全基因组miRNA差异表达分析,发现及初步验证不同亚型肺癌血浆特异性miRNA。将筛选出的特异的miRNAs用做在肺癌高危人群中筛选早期肺癌的分子标记物,具有长远的临床实践意义。
目的：发现不同亚型肺癌组织中的miRNA肿瘤标记物
方法：运用LCM技术从136例正常肺组织、腺癌、鳞癌及小细胞癌中获取纯的上皮细胞。用Agilent最新芯片技术对上述标本进行全基因组miRNA表达分析,并用实时荧光定量RT-PCR (Taqman)加以验证。用GeneSpring GX10软件进行Quantile数据标准化。用未配对t检验(Bonferroni correction)和方差分析(ANOVA)对miRNA芯片数据进行显著性差异性分析。用聚类分析(Hierarchical clustering)进行miRNA基因表达差异的分类。用芯片预测分析方法(prediction analysis of microarrays, PAM predictor and WEAK)对芯片数据进行预测性分析。
结果：在不同亚型肺癌组织中,发现161个miRNA的表达与肺癌显著相关。7个miRNA可以区分正常肺组织及肺癌组织,准确率高达98%；10个miRNA可以区分非小细胞肺癌及小细胞癌,准确率高达99.9%；2个miRNA可以区分例肺腺癌及肺鳞癌,准确率高达93.5%；13个miRNA可以区分肺腺癌、鳞癌及小细胞癌,准确率高达95.5%。定量RT-PCR和miRNA芯片之间相关性(R)平均为0.980。
结论：特异的miRNA表达与肺癌不同亚型有显著的相关性,候选的的特异性miRNA可以区分肺腺癌、鳞癌及小细胞癌,而其中一些miRNA可能预示了肺癌的发病机制。
目的：验证不同亚型肺癌组织中的miRNA肿瘤标记物,明确其在肺癌分子诊断中的临床应用。
方法：运用定量RT-PCR在192例石蜡标本中对7个候选的不同亚型肺癌组织中的miRNA肿瘤标记物(hsa-miR-25, hsa-miR-205, hsa-miR-34a, hsa-miR-375, hsa-miR-29a, hsa-miR-29b, hsa-miR-27a)进行验证。用小RNA U47进行数据标准化。用未配对t检验对miRNA在不同亚型肺癌组织中的表达进行显著差异性分析。用受试者工作特征曲线(receive operating characteristic curve,ROC)及logistic regression分析来评估候选miRNA肿瘤标记物在鉴别不同亚型肺癌的诊断价值。
结果：候选的miRNA肿瘤标记物能鉴别正常肺组织、腺癌、鳞癌及小细胞癌。实验证明：7个miRNA肿瘤标记物能在石蜡组织中鉴别正常肺组织和肺癌及不同亚型肺癌,其中hsa-miR-25及hsa-miR-375组合能鉴别肺腺癌与肺鳞癌准确率达89%(AUC=0.919)；hsa-miR-25,hsa-miR-27a, hsa-miR-29a, hsa-miR-29b及hsa-miR-34a组合能鉴别肺腺癌与小细胞肺癌准确率达98%(AUC=0.990),hsa-miR-29b及hsa-miR-375组合能鉴别肺鳞癌与小细胞肺癌准确率达93%(AUC=0.976)。
结论：实验确定7个miRNA肿瘤标记物能准确鉴别正常肺组织和肺癌及不同亚型肺癌,明确其在肺癌分子诊断中的临床应用。
目的：发现不同亚型肺癌患者血浆中的miRNA肿瘤标记物。
方法：运用用mirVana miRNA isolation kit从19例肺腺癌、10例鳞癌、7例小细胞肺癌患者及23例正常对照血浆中抽提总RNA。用Agilent公司的最新miRNA芯片平台进行分析,通过实时荧光定量RT-PCR (Taqman)方法进行初步验证。用未配对t检验对miRNA芯片数据进行显著性差异性分析。用受试者工作特征曲线(receive operating characteristic curve,ROC)来评估候选miRNA肿瘤标记物在不同亚型肺癌的诊断价值。
结果：在不同亚型肺癌患者血浆中,发现64个miRNA的表达与肺癌显著相关。11个miRNA可以区分正常对照及肺腺癌患者,其中hsa-miR-383及hsa-miR-1233组合能鉴别肺腺癌准确率达83%(AUC=0.886)；18个miRNA可以区分正常对照及肺鳞癌患者,其中hsa-miR-623及hsa-miR-654-5p组合能鉴别肺鳞癌准确率达82%(AUC=0.843)；35个miRNA可以区分正常对照及肺小细胞鳞癌患者,其中hsa-miR-520b及hsa-miR-139-3p组合能鉴别肺小细胞癌准确率达96%(AUC=0.975)。
结论：血浆中的miRNA表达与不同亚型肺癌患有显著的相关性。候选的特异性miRNA可以在血浆中区分肺腺癌、鳞癌及小细胞癌。
Lung cancer remains the most common cause of cancer-related deaths among man and woman worldwide. Despite some improvements in surgical techniques and combined therapies over the last several decades, the five-year survival rate for all stages combined is about 15% in the United States and Europe. Lung cancers are classified as either small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC). The predominant (>80%) histological form of lung cancer is NSCLC including adenocarcinoma and squamous-cell carcinoma.
Treatment for lung cancer differs according to the subtype of cancer. The treatment of choice for early stage NSCLC is surgery with a 5 year overall survival of 40%. However, a majority of patients are at an advanced disease stage at the time of diagnosis, which limits first-line therapy to multi-agent chemotherapy and an expected survival is less than 8 months. Recent advances in targeted therapies require greater accuracy in the subclassification of NSCLC.
Besides the different treatments on the different subtypes and etiologies of lung cancer, the inter-observer variability and the lack of specific, standardized assays also limit the current abilities to adequately stratify patients for suitable treatments. Treatment decisions for an individual patient with lung cancer will soon be based on detailed tumor and host characteristics. Specific molecular biomarkers to differentiate subtypes of lung cancers are definitely needed.
One approach to address this issue might be based on small regulatory RNA molecules, microRNAs (miRNAs), constitute an evolutionary conserved class of endogenously expressed small non-coding RNAs of 20-25 nucleotides (nt) in size that can mediate the expression of target mRNAs and thus-since their discovery about ten years ago-have been implicated with critical functions in cellular development, differentiation, proliferation, and apoptosis. Furthermore, miRNAs have advantages over mRNAs as cancer biomarkers, since they are very stable in vitro and long-lived in vivo.
Molecular profiling of clinical tissue specimens is frequently complicated by their cellular heterogeneity. Laser capture microdissection (LCM) has successfully been used to tackle this problem by isolating pure cell populations from tissue sections. So far, the large majority of published miRNA expression studies utilized whole tumor tissues without separating the truly transformed cancerous cells from those other cell types commonly present within a solid tumor. Analysis of such complex tissues could conceal the specific signature of the particular cell type of interest.
In this study, we combined LCM with genome-wide miRNA analysis to discovery the potential miRNA biomarkers in the different subtype of lung cancer using frozen surgical specimens. We evaluated the miRNA expression profiles to study their potential role in the tumor formation and molecular classification in lung carcinoma. We then validated the candidate miRNA biomarkers in the lung adenocarcinoma, squamous-cell carcinoma and small cell lung cancer using FFPE surgical specimens.
To date, the specificity of tumor markers for a specific tumor is poor. A large number of individuals with an abnormal level of serum cancer marker have a non-malignant condition (false-positive). Also, the sensitivity of many of the serum tests for the presence of malignancy is often poor, resulting in false-negative tests. Therefore, frequent false-positive and false-negative tests result in a limited use of tumor markers as a screening test for malignancy. Therefore, the identification of a new class of blood-based cancer biomarkers, and the development of sensitive and specific clinical assays, need to expand the current clinical capabilities for early cancer detection and reduce cancer mortality.
Very recent studies have demonstrated that the tumor-derived circulating miRNAs at levels sufficient to be measurable as biomarkers for cancer detection. The measurement of tumor derived miRNAs in blood could be an important approach for detection of human cancer. Researches have begun to identify the potential miRNA biomarkers in serum or plasma. However, there is no comprehensive study on miRNA expression profiles in serum/plasma of lung cancer patients.
In this study, we performed genome-wide miRNA analysis on 59 plasma samples collected from 19 patients with adenocarcinoma lung cancer,10 patients with squamous-cell carcinoma lung cancer,7 patients with small cell lung cancer and 23 healthy individuals. We discovered 64 differentially expressed miRNAs in plasma of the patients with different types of lung cancer.
Objective:Discover miRNA biomarkers in LCM-selected epithelial cells from the different types of lung cancer.
Methods:An optimized LCM protocol was applied to isolate pure epithelial cells from 136 frozen surgical specimens of lung cancer patients. Genome-wide miRNA analysis was performed to determine the expression profiles in the LCM-selected epithelial cells. GeneSpring GX10 software was used for quantile normalization. Unpaired t-test with Bonferroni correction and one-way analysis of variance (ANOVA) were applied for the differential expression analysis. Hierarchical clustering was performed with Pearson correlation using the differentially expressed miRNAs. Prediction analysis of microarray (PAM predictor and WEAK) was employed to analyze the data acquired on the microarrays. Subsequently, quantitative RT-PCR was used to verfy the miRNA expression profiles.
Results:161 differentially expressed miRNAs at the different types of lung cancer tissues were discovered. Four classifiers of miRNA signatures were identified for predicting different types of lung cancer. A minimal set of 7 miRNAs could distinct lung cancer from normal tissue with 98% accuracy; a minimal set of 10 miRNAs could distinct SCLC from NSCLC with 95% accuracy; a minimal set of 2 miRNAs could distinct adenocarcinoma from squamous-cell carcinoma with 93.5% accuracy; a minimal set of 13 miRNAs could distinct amongst adenocarcinoma, squamous-cell carcinoma and small cell lung carcinoma with 95.5% accuracy and a minimal set of 20 miRNAs could distinct non-neoplasm polyps and neoplasm polyps with the highest accuracy of 96.9%. The average quantitative correlation (R) of fold changes between Agilent miRNA microarrays and quantitative RT-PCR was 0.980.
Conclusions:The candidate miRNA biomarkers were discovered at the different types of lung cancer tissues. Such biomarkers could accurately discriminate adenocarcinoma, squamous-cell carcinoma and small cell lung carcinoma. The predicted outcomes using the miRNA classifiers were well consistent with the pathological diagnosis.
Objective:Validate the candidate miRNA biomarkers in discriminating the different types of lung cancer tissues
Methods:Quantitative RT-PCR was performed on 7 candidate miRNA biomarkers that could discriminate amongst adenocarcinoma, squamous-cell carcinoma and small cell lung carcinoma using 192 FFPE lung tissue specimens. Unpaired t-test was performed for significance analysis. Receiver operating characteristic (ROC) curve analysis and logistic regression analysis were performed to determine the specificity and sensitivity of individual miRNA as diagnostic biomarkers.
Results:The different types of lung carcinoma tissues could be accurately discriminated by 7 validated miRNA biomarkers. Of them, the combination of hsa-miR-25 with hsa-miR-375 yielded the accuracy of 89%(AUC=0.919) in discriminating adenocarcinoma from squamous-cell carcinoma; the combination of hsa-miR-25, hsa-miR-27a, hsa-miR-29a, hsa-miR-29b and hsa-miR-34a yielded the accuracy of 98%(AUC=0.990)in discriminating adenocarcinoma from small cell lung cancer, and the combination of hsa-miR-29b with hsa-miR-375 yielded the accuracy of 93%(AUC=0.976) in discriminating squamous-cell carcinoma from small cell lung cancer.
Conclusions:The different types of lung carcinoma tissues could be accurately discriminated by 7 validated miRNA biomarkers.
Objective:Discover miRNA biomarkers in plasma of patients with the different types of lung cancer.
Methods:Genome-wide miRNA analysis using Agilent miRNA microarray was performed on 59 plasma samples collected from 19 patients with adenocarcinoma lung cancer,10 patients with squamous-cell carcinoma lung cancer,7 patients with small cell lung cancer and 23 healthy individuals. Unpaired t-test was performed for significance analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the specificity and sensitivity of individual miRNA as diagnostic biomarkers.
Results:64 differentially expressed miRNAs in plasma of the patients with the different types of lung cancer were discovered. Of the 64 miRNAs,11 miRNAs could distinct the patients with adenocarcinoma lung cancer from healthy individuals, the combination of hsa-miR-383 with hsa-miR-1233 yielded the accuracy of 85% (AUC=0.898); 18 miRNAs could distinct the patients with squamous-cell carcinoma lung cancer from healthy individuals, the combination of hsa-miR-623 with hsa-miR-654-5p yielded the accuracy of 82%(AUC=0.843), and 35 miRNAs could distinct the patients with small cell lung cancer from healthy individuals, the combination of hsa-miR-520b with hsa-miR-139-3p yielded the accuracy of 96% (AUC=0.975).
Conclusions:The candidate miRNA biomarkers were discovered in plasma of the patients with the different types of lung cancer. Such biomarkers could accurately discriminate the patients with adenocarcinoma, squamous-cell carcinoma and small cell lung cancer from healthy individuals.
引文
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近几年来对MicroRNAs研究进展很快,发现MicroRNAs作为一种重要的调节分子参与了各种疾病包括肿瘤的发生、发展过程。目前已经发现和阐明了多种MicroRNAs在肿瘤细胞生长、凋亡、浸润转移和新生血管形成等方面的作用,以及MicroRNAs本身的表达调节和成熟过程。研究还发现MicroRNAs在各种肿瘤中的特异性表达谱,有助于临床上肿瘤的诊断、分子分型、预后、治疗反应预测、复发监测和治疗等。但是在MicroRNAs能够被真正有效的应用到临床上之前,还有许多问题有待进一步解决。例如肺癌患者血液、痰液中MicroRNAs作为一种非创伤性检查,其益处还有待更多样本的分析评估。外源性的MicroRNAs和MicroRNAs抑制剂作为一种治疗手段,还需更多的临床前期和临床研究工作以明确这类“药物”合适的载体和动力代谢情况。MicroRNAs如何帮助对肿瘤患者进一步分层、分型以协助肿瘤个体化治疗,有待更深的研究。总之,MicroRNAs是一种广泛存在的调节分子,为基础和临床医学研究开辟了一个崭新、有前景的领域。
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