血清microRNA作为生物标记物在结直肠腺癌早期诊断中的应用
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摘要
研究背景
结直肠癌(colorectal cancer, CRC)是常见的消化道恶性肿瘤之一,在我国,结直肠癌死亡率居恶性肿瘤第五位,其发病率和死亡率呈逐年上升趋势。结直肠癌大多经历了正常粘膜—腺瘤—腺癌的发展过程,即Nornal-Adenoma-Adenocarcinoma(N-A-AC)学说。腺癌是结直肠癌最主要的组织学类型,而腺瘤被公认为结直肠腺癌最重要的癌前病变。结直肠腺癌早期不易发现,大多患者就诊时已到中晚期,尽管诊疗技术不断发展,患者术后5年生存率并无明显改善,缺乏有效的诊断方法实现对结直肠腺癌及其癌前病变的早期诊治是主要的原因之一,若能早期发现并切除结直肠腺癌及其腺瘤病变,将有助于降低其发病率及死亡率。目前,临床上结直肠腺癌早期诊断主要依赖于肠镜、影像学检查和血清学标志物癌胚抗原(Carcinoembryonic antigen, CEA)等,因具有侵入性或敏感性、特异性不足,而无法满足临床需求。寻找敏感性、特异性高的生物标志物用于结直肠腺癌早期诊断仍是临床亟待解决的难题。
MicroRNA (miRNA)是一类长度约19-24个核苷酸的非编码小分子RNA,其异常表达与肿瘤的发生、发展密切相关。近年研究发现,血液中存在丰富而稳定的miRNA,其表达谱具有明显的组织特异性,在不同肿瘤中具有特异的表达谱,是具有潜在价值的肿瘤早期诊断生物标志物。单一miRNA作为标志物的敏感性和特异性仍难以满足临床需求,将差异miRNA组合建立肿瘤特异性表达谱将改进单一分子标志物难以克服的低灵敏度、低特异性问题,成为早期诊断的有效手段。若能获得结直肠癌血清miRNA表达谱,可为其早期诊断提供一条新途径。
在miRNA表达研究中,很多因素(如RNA的数量和浓度)可导致样品测定结果出现偏差。目前,RT-qPCR是miRNA定量检测最常用的方法,选用恰当的内参基因是RT-qPCR定量检测数据标准化最常用的方法,在miRNA研究中发挥重要作用。对于结直肠腺癌、腺瘤外周血miRNA RT-qPCR定量检测,目前尚无适宜内参基因筛选的相关研究报道。
基于上述问题,本课题采用高通量Miseq测序技术,对结直肠腺癌、腺瘤和健康对照组的血清miRNA进行了系统分析,确定标准筛选三组间无差异表达的miRNA和三组间渐进性升高或降低的差异表达miRNA,对上述无差异表达的miRNA进一步RT-qPCR验证,并经geNorrm、Normfinder软件评估,筛选用于结直肠腺癌血清miRNA RT-qPCR测定的最适合内参,为下一步结直肠腺癌血清miRNA差异表达检测及数据标准化分析提供了保障；以筛选出的内参基因作为内对照,将上述差异表达的miRNA进行逐个RT-qPCR验证,筛选差异有统计学意义的miRNA,运用ROC曲线评估各差异miRNA的诊断价值；采用多元logistic回归分析方法将差异miRNA引入回归方程,建立logit(P)判别公式,构建miRNA panel;同时,对miRNA panel采用盲法分析,进行大样本临床验证,评估其在结直肠腺癌诊断及鉴别诊断中的应用价值。
第一部分结直肠腺癌血清microRNA RT-qPCR检测最适内参基因的选择与验证
研究目的
实时荧光定量PCR (RT-qPCR)是目前血清microRNA (miRNA)最常用的检测方法,内参基因的选择对获得可靠RT-qPCR分析数据至关重要。本研究旨在对结直肠腺癌患者血清miRNA定量PCR检测中的内参基因进行系统评估与验证,筛选最适内参基因。
方法
收集30例结直肠腺癌、25例结直肠腺瘤和30例健康对照血清,采用高通量Miseq测序技术对三组混合样本进行测序,选择在三组样本中表达无差异的miRNA作为候选内参基因；采用RT-qPCR技术在45例结直肠腺癌、40例结直肠腺瘤和40例健康对照血清中对筛选出的候选内参基因和常用内参基因U6snRNA进行验证；利用geNorm和NormFinder两种运算法则评估内参基因的表达稳定性,进一步选择最适内参基因；在60例结直肠腺癌、60例结直肠腺瘤和60例健康对照血清中对筛选出的最优内参基因进行验证,并采用筛选出的内参基因评估miR-92a-3p在结直肠腺癌患者血清中的表达。
结果
1.候选内参基因的测序筛选：结直肠腺癌组与腺瘤组、结直肠腺癌组与健康对照组、腺瘤组与健康对照组两两比较发现,拷贝数大于50且两组间表达无差异的miRNA分别为17个、32个和25个。进一步分析,得到三组间表达均无差异的miRNA共13个,作为候选内参基因。
2.候选内参基因的RT-qPCR验证：上述候选内参基因中,有5个miRNA (miR-151a-3p, miR-4446-3p, miR-221-3p, miR-93-5p和miR-3184-3p)在部分标本中无法检测到；miR-197-3p和miR-26a-5p的平均Cq值大于35；miR-103b, miR-484, miR-16-5p, miR-3615, miR-18a-3p, miR-191-5p和U6snRNA均可在所有标本中检测到且平均Cq值小于35。
3.所选内参基因表达稳定性评估：采用geNorm和NormFinder两个运算软件评估上述7个内参基因的稳定性,GeNorm分析结果显示miR-191-5p,miR-16-5p,U6snRNA和miR-18a-3p可作为内参基因; geNorm和NormFinder均显示miR-191-5p是表达最稳定的内参基因,miR-191-5p和U6snRNA组合是最稳定的内参基因组合。
4.最适内参基因的验证：在另一独立大样本中进行验证,结果显示miR-191-5p, U6snRNA和miR-191-5p+U6snRNA组合在结直肠腺癌、腺瘤和健康对照组中表达均无统计学差异,在结直肠腺癌的不同分期患者中表达也无统计学差异。
5. MiR-92a-3p分子的表达：采用miR-191-5p+U6snRNA组合作为内参,检测血清miR-92a-3p分子在不同组的表达。结果显示,miR-92a-3p在结直肠腺癌患者中表达高于腺瘤和健康对照组(P<0.001),在腺瘤患者中的表达高于健康对照(P<0.001)。MiR-92a-3p的表达随结直肠癌患者TNM分期的进展而升高,血清miR-92a-3p表达在结直肠腺癌患者Ⅰ期与Ⅲ/Ⅳ期,Ⅱ期与Ⅲ/Ⅳ期,Ⅲ期与Ⅳ期差异均有统计学意义,而在Ⅰ期与Ⅱ期差异无统计学意义(P=0.66)。
结论
MiR-191-5p和U6snRNA的组合可作为结直肠腺癌、结直肠腺瘤和健康对照血清中miRNA RT-qPCR检测的最适宜内参基因。
第二部分结直肠腺癌血清microRNA panel的建立及临床应用
研究目的
结直肠癌是一种常见的消化道恶性肿瘤,目前用于其早期诊断的方法或具侵入性、或敏感性特异性不足尚无法满足临床需求。血清中存在丰富而稳定的miRNA,其作为标志物为肿瘤早期诊断提供了一条新途径。本研究旨在筛选结直肠腺癌差异表达miRNA,进而构建miRNA panel用于结直肠腺癌的早期诊断。
方法
收集30例结直肠腺癌、25例结直肠腺瘤和30例健康对照血清,采用高通量Miseq测序技术对三组混合样本进行测序,筛选结直肠腺癌差异表达miRNA,作为候选标志物。采用RT-qPCR技术对上述差异miRNA验证,进一步筛选差异有统计学意义的miRNA,将差异miRNA引入多元logistic回归分析,建立miRNA panel,采用ROC曲线评估该miRNA panel的诊断价值。
结果
1.候选标志物的筛选：测序结果显示：结直肠腺癌、腺瘤和健康对照组血清中拷贝数大于10的miRNA分别为348,303和283个。根据确定的筛选标准,miR-195-5p, miR-92a-3p, miR-1290, miR-582-5p, miR-223-3p, miR-136-5p, miR-3074-5p, miR-29a-3p, miR-221-3p, miR-148a-3p, miR-19a-3p和miR-17-3p在健康对照组、腺瘤和结直肠腺癌组呈渐进性高表达,而miR-422a, miR-1260a和miR-4502呈渐进性低表达。
2.差异miRNA的RT-qPCR验证：将上述15个差异表达miRNA在80个独立样本中进行RT-qPCR验证,结果显示miR-19a-3p, miR-223-3p和miR-92a-3p在结直肠腺癌组表达高于腺瘤和对照,腺瘤组表达高于健康对照；miR-422a则呈渐进性低表达。进一步在240个样本中检测,ROC曲线评估miR-19a-3p, miR-92a-3p, miR-223-3p和miR-422a诊断结直肠腺癌的AUC分别为0.849,0.871,0.890和0.843。
3. miRNAs panel的建立：将：miR-19a-3p, miR-92a-3p, miR-223-3p和miR-422a引入多元logistic回归分析,得到结直肠腺癌的判别公式为：logit(P)=0.3313-0.0081*miR-19a-3p-0.0257*miR-92a-3p-0.0406*miR-223-3p+0.1328*miR-422a,该4-miRNA panel诊断结直肠腺癌的AUC为0.960。
4. miRNAs panel诊断价值验证：进一步在独立样本中验证发现,4-miRNA panel诊断结直肠腺癌的AUC为0.951(95%CI:0.907-0.978；敏感性和特异性分别为84.3%和91.6%),高于传统标志物CEA(AUC:0.667,95%CI:0.593-0.735, P<0.001);在CEA阳性组(>5ng/mL),其诊断AUC为0.918(95%CI:0.861-0.957；敏感性和特异性分别为76.79%和85.56%)；CEA阴性组(<5ng/mL),其诊断AUC为0.810(95%CI:0.725-0.818；敏感性和特异性分别为57.14%和86.67%)；该4-miRNA panel诊断结直肠腺癌TNMⅠ/Ⅲ/Ⅲ/Ⅳ患者的AUC分别为0.942,0.935,0.954和0.983；同时我们还分析了该4-miRNA panel对腺瘤的诊断和鉴别诊断价值,发现其鉴别诊断腺瘤和腺癌的AUC为0.886,区分腺瘤和健康对照的AUC为0.765。
结论
血清miR-19a-3p, miR-92a-3p, miR-223-3p和miR-422a可作为结直肠腺癌潜在的肿瘤标志物,构建的4-miRNA panel能够准确区分结直肠腺癌、腺瘤和健康对照者,为结直肠腺癌早期诊断提供了一条新途径,可对现有结直肠腺癌早期诊断方法进行有效补充。
Background
Colorectal cancer (CRC) is one of the most digestive tract malignancies. The mortality of CRC ranked the fifth in our country with increased incidence and mortality in the past several decades. Colorectal cancer is a class of typical malignant epithelial tumors which resulting in a transformation from normal mucosa to precancerous lesion and finally to malignant tumor that is a well known normal mucosa-adenoma-adenocarcinoma (NM-A-AC) sequence. Colorectal adenocarcinoma is the main histological type of colorectal cancer and colorectal adenoma is widely considered as important precancerous lesions of colorectal adenocarcinoma. The clinical symptoms of colorectal adenocarcinoma were dormant and difficult to be found at the early stage. Most of colorectal adenocarcinoma patients were diagnosed at the middle and late stages and lost the best operation opportunity. There was no significant improvement of5-year survival rates of colorectal adenocarcinoma patients although clinical medical technology was constantly developing. The lack of effective diagnostic method to diagnosis early stage of colorectal adenocarcinoma and precancerous lesion was one of the main reasons. Removal of early-stage cancer and precancerous lesions would play a pivotal role to reduce the currently high colorectal adenocarcinoma mortality. Several colorectal adenocarcinoma screening tests, including colonoscopy, imageological examination and CEA detection, have been used for years. However, none of these methods has been established as a well-accepted screening tool, particularly for the diagnosis of early-stage colorectal adenocarcinoma. It has become an urgent and tough problem to look for high sensitivity and specificity of biomarkers for colorectal adenocarcinoma.
MicroRNAs (miRNAs) are a class of small non-coding RNAs with19-24nucleotides, and they have attracted a great deal of attention in cancer research. Functional studies have indicated that deregulation of miRNAs is involved in the initiation and progression of human cancer. Recently, serum miRNAs have been shown to be rich and stable. The sensitivity and specificity of single miRNA as biomarker is hard to meet current clinical needs which can be solved by the use of tumor specific miRNA profile. The miRNA profile has been considered as effective tool for tumor early diagnosis. It will provide a new efficient approach for early diagnosis if the specific miRNA profile of colorectal adenoacarcinoma was acquired.
Quantitative real time polymerase chain reaction (RT-qPCR) is the most frequently used approach for detection of miRNAs due to its accuracy, sensitivity, specificity and reproducibility. Many factors for example the quantity and concentration of RNA might lead to deviation of results in the research of gene expression. The use of reference genes as endogenous control is the most common method for normalizing RT-qPCR data of miRNAs expression. The selection of suitable reference genes play a crucial role in miRNAs research because normalization to unreliable reference genes may lead to incorrect determination of miRNAs of interests. As far as aware, no systematic identification and validation of reference genes for normalizing RT-qPCR analysis of serum miRNAs in colorectal adenocarcinoma and colorectal adenoma patients has been published.
Based on the above questions, we firstly characterized the genome-wide miRNA expression profile in serum of patients with colorectal adenocarcinoma, colorectal adenoma and healthy controls using a high-throughput Miseq sequencing screening. A great effort was made to screen candidate reference genes which were undifferentially expressed among three groups and candidate biomarkers which were gradually upregulated or downregulated according to our criteria. Then the reliability of candidate reference genes for normalization was evaluated by RT-qPCR assays to select the most suitable reference genes. The selected reference genes were validated in an independent cohort study. To test for the effect of reference genes, we selected serum miR-92a-3p as target miRNA. Using the above reference genes, the differentially expressed biomarkers were validated by two phases of RT-qPCR. The relationship of candidate biomarkers and clinical parameters were analyzed and receiver operating characteristic (ROC) curve was used to evaluate their diagnostic value. Then, the miRNA panel was developed with a logistic regression model to construct logit(P) equation. The miRNAs panel was validated in huge sample using blind methods and ROC was constructed to evaluate the diagnostic accuracy and differential diagnosis value.
PART I
Identification and validation of reference genes for qPCR detection of serum microRNAs in colorectal adenocarcinoma patients
Objective
The most frequently used approach for serum miRNAs detection is quantitative real time polymerase chain reaction (qPCR). In order to obtain reliable qPCR data of miRNAs expression, the use of reference genes as endogenous control is undoubtly necessary. The aim of our study is to evaluate and validate reference genes for normalizing qPCR analysis of serum miRNAs in colorectal adenocarcinoma.
Methods
We firstly profiled pooled serum of30patients with colorectal adenocarcinoma,25patients with colorectal adenoma and30healthy controls using Miseq sequencing. The miRNAs were selected as candidate reference genes that showing no differential expression among three groups. RT-qPCR was performed to validate above candidate reference genes and U6SnRNA in45patients with colorectal adenocarcinoma,40patients with colorectal adenoma and40healthy controls. The variable stability of candidate reference gene was further evaluated by two algorithms:geNorm and NormFinder to select the most suitable reference gene that was further validated in an independent cohort. The expression of miR-92a-3p was analyzed in colorectal adenocarcinoma patients using the selected most suitable reference gene.
Results
1. Selection of candidate reference genes by Miseq sequencing:Of the serum miRNAs that were scanned by sequencing, there were17,32and25miRNAs having more than50copies and showing no differential expression among pooling group of colorectal adenocarcinoma and colorectal adenoma, colorectal adenocarcinoma and healthy controls, colorectal adenoma patients and healthy controls, respectively (P>0.05). According to the criteria described in "Material and Methods", a list of13miRNAs was considered as candidate reference genes.
2. Confirmation of candidate reference genes by qPCR:Five miRNAs (miR-151a-3p, miR-4446-3p, miR-221-3p, miR-93-5p and miR-3184-3p) were not detected in all samples that were excluded from further analysis. Using the Cq values, there was no evidence for differential expression of any of the nine candidate reference genes among three groups (P>0.05). The candidate reference genes displayed a wide expression range, with Cq values between23.21and44.68. Expression of miR-197-3p and miR-26a-5p was relatively low, with median Cq more than35that were also excluded from further stability analysis.
3. Expression stability analysis of selected reference genes by GeNorm and NormFinder:Variable stability of selected reference genes (miR-103b, miR-484, miR-16-5p, miR-3615, miR-18a-3p, miR-191-5p and U6snRNA) was evaluated using two algorithms:geNorm and NormFinder. GeNorm recommended the use of four (miR-191-5p, miR-16-5p, U6snRNA and miR-18a-3p) of the seven most stable genes for optimal normalization. NormFinder and geNorm both identified miR-191-5p as the most stably expressed reference genes and selected miR-191-5p and U6snRNA as the most stable pair of reference gene.
4. Validation of suitable reference genes in a cohort samples:The expression level of miR-191-5p, U6snRNA and combination of miR-191-5p and U6snRNA (mean) in three groups and four stages of colorectal adenocarcinoma group were validated in an independent cohort. Using the Cq values of each validate reference gene, there was no evidence for differential expression among the three groups and four stages of colorectal adenocarcinoma.
5. Effect of suitable reference genes on relative quantity of serum miR-92a-3p:Our results indicated that serum miR-92a-3p was significantly higher in colorectal adenocarcinoma patients than colorectal adenoma patients and healthy controls (P<0.001). The difference in miR-92a-3p level remained significant between colorectal adenoma patients and healthy controls (P<0.001). The expression of miR-92a-3p increased with the progress of TNM staging. There was significant difference of serum miR-92a-3p between Stage I and StageⅢ/Ⅳ, between Stage Ⅱ and StageⅢ/Ⅳ, between StageⅢ and Stage Ⅳ. The difference of serum miR-92a-3p between Stage Ⅰ and Stage Ⅱ was not found (P=0.66).
Conclusions
We propose that combination of miR-191-5p and U6snRNA could be used as reference genes for serum microRNAs qPCR data in colorectal adenocarcinoma, colorectal adenoma and healthy controls.
PART II
The establishment and clinical value of serum microRNA panel in colorectal adenocarcinoma
Objective
Currently, none of the available colorectal adenocarcinoma (CAC) testing has been established as a well-accepted diagnosis tool, particularly for the early stage of CAC. The recent discovery of serum microRNA (miRNA) profile has provided a new auxiliary approach for tumor diagnosis. Our study is the global analysis of serum miRNAs during the normal-colorectal adenoma (CA)-CAC sequence.
Methods
We firstly profiled pooled serum of30patients with colorectal adenocarcinoma,25patients with colorectal adenoma and30healthy controls using Miseq sequencing. Differentially expressed serum miRNAs were validated by two phase of reverse-transcription polymerase chain reaction (RT-qPCR). The miRNA panel was developed with a logistic regression model and validated using an independent cohort. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic accuracy of the panel.
Results
1. Discovery of candidate biomarkers by miseq sequencing:Of the740sequenced serum miRNAs,348,303and283miRNAs were detectable (more than10copies) in CAC patients, CA patients and healthy controls, respectively. The miRNAs were selected as candidate biomarkers based on the following criteria:(a) having at least50copies in any group;(b) exhibiting at least10-fold altered expression; and (c) the intersection of CAC vs CA and CA vs healthy controls. Levels of12miRNAs, including miR-195-5p, miR-92a-3p, miR-1290, miR-582-5p, miR-223-3p and miR-136-5p, miR-3074-5p, miR-29a-3p, miR-221-3p, miR-148a-3p, miR-19a-3p and miR-17-3p were significantly higher in CAC than those in CA and healthy controls (fold change=10.78-399.02; P<0.05). In contrast, levels of3miRNAs, including miR-422a, miR-1260a and miR-4502, in the CAC group were significantly lower (fold change=0.002-0.09; P<0.05). In summary, a total of15differentially expressed miRNAs were identified as candidate biomarkers, which should be further tested via RT-qPCR.
2. Confirmation of miRNAs by RT-qPCR analysis:We first tested the15candidate miRNAs using an independent cohort of80serum samples with RT-qPCR. Only4miRNAs (miR-19a-3p, miR-223-3p, miR-92a-3p and miR-422a) with a differential expression between the CAC vs CA and CA vs healthy control group were selected using these above-mentioned quality control criteria. These four miRNAs were further evaluated by RT-qPCR using additional independent240serum samples. High expression levels of miR-19a-3p, miR-92a-3p and miR-223-3p as well as the low expression level of miR-422a were detected in CAC patients compared with CA and healthy control group. The diagnostic accuracy of these4miRNAs was measured by ROC, and their corresponding AUCs were0.849,0.871,0.890and0.843, respectively.
3. Establishing the predictive miRNAs panel:In the present study, we constructed the miRNA panel for CAC diagnosis using320serum samples as the training data. The predicted probability of diagnosis with CAC from the stepwise logistic regression model was calculated using the equation as follows:logit (P)=0.3313-0.0081*miR-149a-3p-0.0257*miR-92a-3p-0.0406*miR-223-3p+0.1328*mi R-422a. The diagnostic performance of the established miRNA panel was evaluated by the ROC analysis. The AUC of the established4-miRNA panel was0.960.
4. Validation of the miRNAs panel:We further validated the diagnostic performance of the established4-miRNA panel in another independent validation phase. The AUC of the4-miRNA panel was0.951(95%CI:0.907to0.978; sensitivity=84.3%, specificity=91.6%), which was greater than CEA detection (AUC:0.667,95%CI:0.593to0.735, P<0.001). Moreover, we further evaluated the diagnostic performance of the established miRNA panel at different TNM stages. The corresponding AUCs for patients with TNM stages Ⅰ, Ⅱ, Ⅲ and Ⅳ were0.942,0.935,0.954and0.983, respectively. In addition, we evaluated the diagnostic accuracy of the established miRNA panel according to the CEA level. In the low CEA level (<5ng/mL) group, the AUC of the established miRNA panel was0.810(95%CI,0.725to0.818; sensitivity=57.14%, specificity=86.67%). In the elevated CEA level (>5ng/mL) group, the AUC of the established miRNA panel was0.918(95%CI,0.861to0.957; sensitivity=76.79%, specificity=85.56%). Finally, we also evaluated the diagnostic performance of the4-miRNA panel in discriminating the CA from CAC and healthy controls group. The analysis demonstrated that the miRNA panel possessed a high accuracy in discriminating CA from CAC (AUC=0.886;95%CI:0.809to0.940) and CA from healthy controls (AUC=0.765;95%CI:0.669to0.845).
Conclusions
Serum miR-19a-3p, miR-223-3p, miR-92a-3p and miR-422a were considered as promising biomarkers for colorectal adenocarcinoma. We established a serum4-miRNA panel with considerable clinical value in the early-stage diagnosis of CAC.
结直肠癌(colorectal cancer, CRC)是常见的消化道恶性肿瘤之一,在我国,结直肠癌死亡率居恶性肿瘤第五位,其发病率和死亡率呈逐年上升趋势。结直肠癌大多经历了正常粘膜—腺瘤—腺癌的发展过程,即Nornal-Adenoma-Adenocarcinoma(N-A-AC)学说。腺癌是结直肠癌最主要的组织学类型,而腺瘤被公认为结直肠腺癌最重要的癌前病变。结直肠腺癌早期不易发现,大多患者就诊时已到中晚期,尽管诊疗技术不断发展,患者术后5年生存率并无明显改善,缺乏有效的诊断方法实现对结直肠腺癌及其癌前病变的早期诊治是主要的原因之一,若能早期发现并切除结直肠腺癌及其腺瘤病变,将有助于降低其发病率及死亡率。目前,临床上结直肠腺癌早期诊断主要依赖于肠镜、影像学检查和血清学标志物癌胚抗原(Carcinoembryonic antigen, CEA)等,因具有侵入性或敏感性、特异性不足,而无法满足临床需求。寻找敏感性、特异性高的生物标志物用于结直肠腺癌早期诊断仍是临床亟待解决的难题。
MicroRNA (miRNA)是一类长度约19-24个核苷酸的非编码小分子RNA,其异常表达与肿瘤的发生、发展密切相关。近年研究发现,血液中存在丰富而稳定的miRNA,其表达谱具有明显的组织特异性,在不同肿瘤中具有特异的表达谱,是具有潜在价值的肿瘤早期诊断生物标志物。单一miRNA作为标志物的敏感性和特异性仍难以满足临床需求,将差异miRNA组合建立肿瘤特异性表达谱将改进单一分子标志物难以克服的低灵敏度、低特异性问题,成为早期诊断的有效手段。若能获得结直肠癌血清miRNA表达谱,可为其早期诊断提供一条新途径。
在miRNA表达研究中,很多因素(如RNA的数量和浓度)可导致样品测定结果出现偏差。目前,RT-qPCR是miRNA定量检测最常用的方法,选用恰当的内参基因是RT-qPCR定量检测数据标准化最常用的方法,在miRNA研究中发挥重要作用。对于结直肠腺癌、腺瘤外周血miRNA RT-qPCR定量检测,目前尚无适宜内参基因筛选的相关研究报道。
基于上述问题,本课题采用高通量Miseq测序技术,对结直肠腺癌、腺瘤和健康对照组的血清miRNA进行了系统分析,确定标准筛选三组间无差异表达的miRNA和三组间渐进性升高或降低的差异表达miRNA,对上述无差异表达的miRNA进一步RT-qPCR验证,并经geNorrm、Normfinder软件评估,筛选用于结直肠腺癌血清miRNA RT-qPCR测定的最适合内参,为下一步结直肠腺癌血清miRNA差异表达检测及数据标准化分析提供了保障；以筛选出的内参基因作为内对照,将上述差异表达的miRNA进行逐个RT-qPCR验证,筛选差异有统计学意义的miRNA,运用ROC曲线评估各差异miRNA的诊断价值；采用多元logistic回归分析方法将差异miRNA引入回归方程,建立logit(P)判别公式,构建miRNA panel;同时,对miRNA panel采用盲法分析,进行大样本临床验证,评估其在结直肠腺癌诊断及鉴别诊断中的应用价值。
第一部分结直肠腺癌血清microRNA RT-qPCR检测最适内参基因的选择与验证
研究目的
实时荧光定量PCR (RT-qPCR)是目前血清microRNA (miRNA)最常用的检测方法,内参基因的选择对获得可靠RT-qPCR分析数据至关重要。本研究旨在对结直肠腺癌患者血清miRNA定量PCR检测中的内参基因进行系统评估与验证,筛选最适内参基因。
方法
收集30例结直肠腺癌、25例结直肠腺瘤和30例健康对照血清,采用高通量Miseq测序技术对三组混合样本进行测序,选择在三组样本中表达无差异的miRNA作为候选内参基因；采用RT-qPCR技术在45例结直肠腺癌、40例结直肠腺瘤和40例健康对照血清中对筛选出的候选内参基因和常用内参基因U6snRNA进行验证；利用geNorm和NormFinder两种运算法则评估内参基因的表达稳定性,进一步选择最适内参基因；在60例结直肠腺癌、60例结直肠腺瘤和60例健康对照血清中对筛选出的最优内参基因进行验证,并采用筛选出的内参基因评估miR-92a-3p在结直肠腺癌患者血清中的表达。
结果
1.候选内参基因的测序筛选：结直肠腺癌组与腺瘤组、结直肠腺癌组与健康对照组、腺瘤组与健康对照组两两比较发现,拷贝数大于50且两组间表达无差异的miRNA分别为17个、32个和25个。进一步分析,得到三组间表达均无差异的miRNA共13个,作为候选内参基因。
2.候选内参基因的RT-qPCR验证：上述候选内参基因中,有5个miRNA (miR-151a-3p, miR-4446-3p, miR-221-3p, miR-93-5p和miR-3184-3p)在部分标本中无法检测到；miR-197-3p和miR-26a-5p的平均Cq值大于35；miR-103b, miR-484, miR-16-5p, miR-3615, miR-18a-3p, miR-191-5p和U6snRNA均可在所有标本中检测到且平均Cq值小于35。
3.所选内参基因表达稳定性评估：采用geNorm和NormFinder两个运算软件评估上述7个内参基因的稳定性,GeNorm分析结果显示miR-191-5p,miR-16-5p,U6snRNA和miR-18a-3p可作为内参基因; geNorm和NormFinder均显示miR-191-5p是表达最稳定的内参基因,miR-191-5p和U6snRNA组合是最稳定的内参基因组合。
4.最适内参基因的验证：在另一独立大样本中进行验证,结果显示miR-191-5p, U6snRNA和miR-191-5p+U6snRNA组合在结直肠腺癌、腺瘤和健康对照组中表达均无统计学差异,在结直肠腺癌的不同分期患者中表达也无统计学差异。
5. MiR-92a-3p分子的表达：采用miR-191-5p+U6snRNA组合作为内参,检测血清miR-92a-3p分子在不同组的表达。结果显示,miR-92a-3p在结直肠腺癌患者中表达高于腺瘤和健康对照组(P<0.001),在腺瘤患者中的表达高于健康对照(P<0.001)。MiR-92a-3p的表达随结直肠癌患者TNM分期的进展而升高,血清miR-92a-3p表达在结直肠腺癌患者Ⅰ期与Ⅲ/Ⅳ期,Ⅱ期与Ⅲ/Ⅳ期,Ⅲ期与Ⅳ期差异均有统计学意义,而在Ⅰ期与Ⅱ期差异无统计学意义(P=0.66)。
结论
MiR-191-5p和U6snRNA的组合可作为结直肠腺癌、结直肠腺瘤和健康对照血清中miRNA RT-qPCR检测的最适宜内参基因。
第二部分结直肠腺癌血清microRNA panel的建立及临床应用
研究目的
结直肠癌是一种常见的消化道恶性肿瘤,目前用于其早期诊断的方法或具侵入性、或敏感性特异性不足尚无法满足临床需求。血清中存在丰富而稳定的miRNA,其作为标志物为肿瘤早期诊断提供了一条新途径。本研究旨在筛选结直肠腺癌差异表达miRNA,进而构建miRNA panel用于结直肠腺癌的早期诊断。
方法
收集30例结直肠腺癌、25例结直肠腺瘤和30例健康对照血清,采用高通量Miseq测序技术对三组混合样本进行测序,筛选结直肠腺癌差异表达miRNA,作为候选标志物。采用RT-qPCR技术对上述差异miRNA验证,进一步筛选差异有统计学意义的miRNA,将差异miRNA引入多元logistic回归分析,建立miRNA panel,采用ROC曲线评估该miRNA panel的诊断价值。
结果
1.候选标志物的筛选：测序结果显示：结直肠腺癌、腺瘤和健康对照组血清中拷贝数大于10的miRNA分别为348,303和283个。根据确定的筛选标准,miR-195-5p, miR-92a-3p, miR-1290, miR-582-5p, miR-223-3p, miR-136-5p, miR-3074-5p, miR-29a-3p, miR-221-3p, miR-148a-3p, miR-19a-3p和miR-17-3p在健康对照组、腺瘤和结直肠腺癌组呈渐进性高表达,而miR-422a, miR-1260a和miR-4502呈渐进性低表达。
2.差异miRNA的RT-qPCR验证：将上述15个差异表达miRNA在80个独立样本中进行RT-qPCR验证,结果显示miR-19a-3p, miR-223-3p和miR-92a-3p在结直肠腺癌组表达高于腺瘤和对照,腺瘤组表达高于健康对照；miR-422a则呈渐进性低表达。进一步在240个样本中检测,ROC曲线评估miR-19a-3p, miR-92a-3p, miR-223-3p和miR-422a诊断结直肠腺癌的AUC分别为0.849,0.871,0.890和0.843。
3. miRNAs panel的建立：将：miR-19a-3p, miR-92a-3p, miR-223-3p和miR-422a引入多元logistic回归分析,得到结直肠腺癌的判别公式为：logit(P)=0.3313-0.0081*miR-19a-3p-0.0257*miR-92a-3p-0.0406*miR-223-3p+0.1328*miR-422a,该4-miRNA panel诊断结直肠腺癌的AUC为0.960。
4. miRNAs panel诊断价值验证：进一步在独立样本中验证发现,4-miRNA panel诊断结直肠腺癌的AUC为0.951(95%CI:0.907-0.978；敏感性和特异性分别为84.3%和91.6%),高于传统标志物CEA(AUC:0.667,95%CI:0.593-0.735, P<0.001);在CEA阳性组(>5ng/mL),其诊断AUC为0.918(95%CI:0.861-0.957；敏感性和特异性分别为76.79%和85.56%)；CEA阴性组(<5ng/mL),其诊断AUC为0.810(95%CI:0.725-0.818；敏感性和特异性分别为57.14%和86.67%)；该4-miRNA panel诊断结直肠腺癌TNMⅠ/Ⅲ/Ⅲ/Ⅳ患者的AUC分别为0.942,0.935,0.954和0.983；同时我们还分析了该4-miRNA panel对腺瘤的诊断和鉴别诊断价值,发现其鉴别诊断腺瘤和腺癌的AUC为0.886,区分腺瘤和健康对照的AUC为0.765。
结论
血清miR-19a-3p, miR-92a-3p, miR-223-3p和miR-422a可作为结直肠腺癌潜在的肿瘤标志物,构建的4-miRNA panel能够准确区分结直肠腺癌、腺瘤和健康对照者,为结直肠腺癌早期诊断提供了一条新途径,可对现有结直肠腺癌早期诊断方法进行有效补充。
Background
Colorectal cancer (CRC) is one of the most digestive tract malignancies. The mortality of CRC ranked the fifth in our country with increased incidence and mortality in the past several decades. Colorectal cancer is a class of typical malignant epithelial tumors which resulting in a transformation from normal mucosa to precancerous lesion and finally to malignant tumor that is a well known normal mucosa-adenoma-adenocarcinoma (NM-A-AC) sequence. Colorectal adenocarcinoma is the main histological type of colorectal cancer and colorectal adenoma is widely considered as important precancerous lesions of colorectal adenocarcinoma. The clinical symptoms of colorectal adenocarcinoma were dormant and difficult to be found at the early stage. Most of colorectal adenocarcinoma patients were diagnosed at the middle and late stages and lost the best operation opportunity. There was no significant improvement of5-year survival rates of colorectal adenocarcinoma patients although clinical medical technology was constantly developing. The lack of effective diagnostic method to diagnosis early stage of colorectal adenocarcinoma and precancerous lesion was one of the main reasons. Removal of early-stage cancer and precancerous lesions would play a pivotal role to reduce the currently high colorectal adenocarcinoma mortality. Several colorectal adenocarcinoma screening tests, including colonoscopy, imageological examination and CEA detection, have been used for years. However, none of these methods has been established as a well-accepted screening tool, particularly for the diagnosis of early-stage colorectal adenocarcinoma. It has become an urgent and tough problem to look for high sensitivity and specificity of biomarkers for colorectal adenocarcinoma.
MicroRNAs (miRNAs) are a class of small non-coding RNAs with19-24nucleotides, and they have attracted a great deal of attention in cancer research. Functional studies have indicated that deregulation of miRNAs is involved in the initiation and progression of human cancer. Recently, serum miRNAs have been shown to be rich and stable. The sensitivity and specificity of single miRNA as biomarker is hard to meet current clinical needs which can be solved by the use of tumor specific miRNA profile. The miRNA profile has been considered as effective tool for tumor early diagnosis. It will provide a new efficient approach for early diagnosis if the specific miRNA profile of colorectal adenoacarcinoma was acquired.
Quantitative real time polymerase chain reaction (RT-qPCR) is the most frequently used approach for detection of miRNAs due to its accuracy, sensitivity, specificity and reproducibility. Many factors for example the quantity and concentration of RNA might lead to deviation of results in the research of gene expression. The use of reference genes as endogenous control is the most common method for normalizing RT-qPCR data of miRNAs expression. The selection of suitable reference genes play a crucial role in miRNAs research because normalization to unreliable reference genes may lead to incorrect determination of miRNAs of interests. As far as aware, no systematic identification and validation of reference genes for normalizing RT-qPCR analysis of serum miRNAs in colorectal adenocarcinoma and colorectal adenoma patients has been published.
Based on the above questions, we firstly characterized the genome-wide miRNA expression profile in serum of patients with colorectal adenocarcinoma, colorectal adenoma and healthy controls using a high-throughput Miseq sequencing screening. A great effort was made to screen candidate reference genes which were undifferentially expressed among three groups and candidate biomarkers which were gradually upregulated or downregulated according to our criteria. Then the reliability of candidate reference genes for normalization was evaluated by RT-qPCR assays to select the most suitable reference genes. The selected reference genes were validated in an independent cohort study. To test for the effect of reference genes, we selected serum miR-92a-3p as target miRNA. Using the above reference genes, the differentially expressed biomarkers were validated by two phases of RT-qPCR. The relationship of candidate biomarkers and clinical parameters were analyzed and receiver operating characteristic (ROC) curve was used to evaluate their diagnostic value. Then, the miRNA panel was developed with a logistic regression model to construct logit(P) equation. The miRNAs panel was validated in huge sample using blind methods and ROC was constructed to evaluate the diagnostic accuracy and differential diagnosis value.
PART I
Identification and validation of reference genes for qPCR detection of serum microRNAs in colorectal adenocarcinoma patients
Objective
The most frequently used approach for serum miRNAs detection is quantitative real time polymerase chain reaction (qPCR). In order to obtain reliable qPCR data of miRNAs expression, the use of reference genes as endogenous control is undoubtly necessary. The aim of our study is to evaluate and validate reference genes for normalizing qPCR analysis of serum miRNAs in colorectal adenocarcinoma.
Methods
We firstly profiled pooled serum of30patients with colorectal adenocarcinoma,25patients with colorectal adenoma and30healthy controls using Miseq sequencing. The miRNAs were selected as candidate reference genes that showing no differential expression among three groups. RT-qPCR was performed to validate above candidate reference genes and U6SnRNA in45patients with colorectal adenocarcinoma,40patients with colorectal adenoma and40healthy controls. The variable stability of candidate reference gene was further evaluated by two algorithms:geNorm and NormFinder to select the most suitable reference gene that was further validated in an independent cohort. The expression of miR-92a-3p was analyzed in colorectal adenocarcinoma patients using the selected most suitable reference gene.
Results
1. Selection of candidate reference genes by Miseq sequencing:Of the serum miRNAs that were scanned by sequencing, there were17,32and25miRNAs having more than50copies and showing no differential expression among pooling group of colorectal adenocarcinoma and colorectal adenoma, colorectal adenocarcinoma and healthy controls, colorectal adenoma patients and healthy controls, respectively (P>0.05). According to the criteria described in "Material and Methods", a list of13miRNAs was considered as candidate reference genes.
2. Confirmation of candidate reference genes by qPCR:Five miRNAs (miR-151a-3p, miR-4446-3p, miR-221-3p, miR-93-5p and miR-3184-3p) were not detected in all samples that were excluded from further analysis. Using the Cq values, there was no evidence for differential expression of any of the nine candidate reference genes among three groups (P>0.05). The candidate reference genes displayed a wide expression range, with Cq values between23.21and44.68. Expression of miR-197-3p and miR-26a-5p was relatively low, with median Cq more than35that were also excluded from further stability analysis.
3. Expression stability analysis of selected reference genes by GeNorm and NormFinder:Variable stability of selected reference genes (miR-103b, miR-484, miR-16-5p, miR-3615, miR-18a-3p, miR-191-5p and U6snRNA) was evaluated using two algorithms:geNorm and NormFinder. GeNorm recommended the use of four (miR-191-5p, miR-16-5p, U6snRNA and miR-18a-3p) of the seven most stable genes for optimal normalization. NormFinder and geNorm both identified miR-191-5p as the most stably expressed reference genes and selected miR-191-5p and U6snRNA as the most stable pair of reference gene.
4. Validation of suitable reference genes in a cohort samples:The expression level of miR-191-5p, U6snRNA and combination of miR-191-5p and U6snRNA (mean) in three groups and four stages of colorectal adenocarcinoma group were validated in an independent cohort. Using the Cq values of each validate reference gene, there was no evidence for differential expression among the three groups and four stages of colorectal adenocarcinoma.
5. Effect of suitable reference genes on relative quantity of serum miR-92a-3p:Our results indicated that serum miR-92a-3p was significantly higher in colorectal adenocarcinoma patients than colorectal adenoma patients and healthy controls (P<0.001). The difference in miR-92a-3p level remained significant between colorectal adenoma patients and healthy controls (P<0.001). The expression of miR-92a-3p increased with the progress of TNM staging. There was significant difference of serum miR-92a-3p between Stage I and StageⅢ/Ⅳ, between Stage Ⅱ and StageⅢ/Ⅳ, between StageⅢ and Stage Ⅳ. The difference of serum miR-92a-3p between Stage Ⅰ and Stage Ⅱ was not found (P=0.66).
Conclusions
We propose that combination of miR-191-5p and U6snRNA could be used as reference genes for serum microRNAs qPCR data in colorectal adenocarcinoma, colorectal adenoma and healthy controls.
PART II
The establishment and clinical value of serum microRNA panel in colorectal adenocarcinoma
Objective
Currently, none of the available colorectal adenocarcinoma (CAC) testing has been established as a well-accepted diagnosis tool, particularly for the early stage of CAC. The recent discovery of serum microRNA (miRNA) profile has provided a new auxiliary approach for tumor diagnosis. Our study is the global analysis of serum miRNAs during the normal-colorectal adenoma (CA)-CAC sequence.
Methods
We firstly profiled pooled serum of30patients with colorectal adenocarcinoma,25patients with colorectal adenoma and30healthy controls using Miseq sequencing. Differentially expressed serum miRNAs were validated by two phase of reverse-transcription polymerase chain reaction (RT-qPCR). The miRNA panel was developed with a logistic regression model and validated using an independent cohort. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic accuracy of the panel.
Results
1. Discovery of candidate biomarkers by miseq sequencing:Of the740sequenced serum miRNAs,348,303and283miRNAs were detectable (more than10copies) in CAC patients, CA patients and healthy controls, respectively. The miRNAs were selected as candidate biomarkers based on the following criteria:(a) having at least50copies in any group;(b) exhibiting at least10-fold altered expression; and (c) the intersection of CAC vs CA and CA vs healthy controls. Levels of12miRNAs, including miR-195-5p, miR-92a-3p, miR-1290, miR-582-5p, miR-223-3p and miR-136-5p, miR-3074-5p, miR-29a-3p, miR-221-3p, miR-148a-3p, miR-19a-3p and miR-17-3p were significantly higher in CAC than those in CA and healthy controls (fold change=10.78-399.02; P<0.05). In contrast, levels of3miRNAs, including miR-422a, miR-1260a and miR-4502, in the CAC group were significantly lower (fold change=0.002-0.09; P<0.05). In summary, a total of15differentially expressed miRNAs were identified as candidate biomarkers, which should be further tested via RT-qPCR.
2. Confirmation of miRNAs by RT-qPCR analysis:We first tested the15candidate miRNAs using an independent cohort of80serum samples with RT-qPCR. Only4miRNAs (miR-19a-3p, miR-223-3p, miR-92a-3p and miR-422a) with a differential expression between the CAC vs CA and CA vs healthy control group were selected using these above-mentioned quality control criteria. These four miRNAs were further evaluated by RT-qPCR using additional independent240serum samples. High expression levels of miR-19a-3p, miR-92a-3p and miR-223-3p as well as the low expression level of miR-422a were detected in CAC patients compared with CA and healthy control group. The diagnostic accuracy of these4miRNAs was measured by ROC, and their corresponding AUCs were0.849,0.871,0.890and0.843, respectively.
3. Establishing the predictive miRNAs panel:In the present study, we constructed the miRNA panel for CAC diagnosis using320serum samples as the training data. The predicted probability of diagnosis with CAC from the stepwise logistic regression model was calculated using the equation as follows:logit (P)=0.3313-0.0081*miR-149a-3p-0.0257*miR-92a-3p-0.0406*miR-223-3p+0.1328*mi R-422a. The diagnostic performance of the established miRNA panel was evaluated by the ROC analysis. The AUC of the established4-miRNA panel was0.960.
4. Validation of the miRNAs panel:We further validated the diagnostic performance of the established4-miRNA panel in another independent validation phase. The AUC of the4-miRNA panel was0.951(95%CI:0.907to0.978; sensitivity=84.3%, specificity=91.6%), which was greater than CEA detection (AUC:0.667,95%CI:0.593to0.735, P<0.001). Moreover, we further evaluated the diagnostic performance of the established miRNA panel at different TNM stages. The corresponding AUCs for patients with TNM stages Ⅰ, Ⅱ, Ⅲ and Ⅳ were0.942,0.935,0.954and0.983, respectively. In addition, we evaluated the diagnostic accuracy of the established miRNA panel according to the CEA level. In the low CEA level (<5ng/mL) group, the AUC of the established miRNA panel was0.810(95%CI,0.725to0.818; sensitivity=57.14%, specificity=86.67%). In the elevated CEA level (>5ng/mL) group, the AUC of the established miRNA panel was0.918(95%CI,0.861to0.957; sensitivity=76.79%, specificity=85.56%). Finally, we also evaluated the diagnostic performance of the4-miRNA panel in discriminating the CA from CAC and healthy controls group. The analysis demonstrated that the miRNA panel possessed a high accuracy in discriminating CA from CAC (AUC=0.886;95%CI:0.809to0.940) and CA from healthy controls (AUC=0.765;95%CI:0.669to0.845).
Conclusions
Serum miR-19a-3p, miR-223-3p, miR-92a-3p and miR-422a were considered as promising biomarkers for colorectal adenocarcinoma. We established a serum4-miRNA panel with considerable clinical value in the early-stage diagnosis of CAC.
引文
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6. O'Conn ell JB, Maggard MA, Ko CY. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging. J Natl Cancer Inst 2004; 96:1420e5.
7. Lange CP, Laird PW. Clinical application of DNA methylation biomarkers in colorectal cancer. Epigenomics 2013; 5(2):105-108.
8. Zhang X, Xiao Z, Liu X, Du L, Wang L, Wang S, Zheng N, Zheng G, Li W, Zhang X, Dong Z, Zhuang X, Wang C. The potential role of ORM2 in the development of colorectal cancer. PLoS One 2012; 7(2):e31868.
9. Kin C, Kidess E, Poultsides GA, Visser BC, Jeffrey SS. Colorectal cancer diagnostics:biomarkers, cell-free DNA, circulating tumor cells and definingheterogeneous populations by single-cell analysis. Expert Rev Mol Diagn 2013; 13(6):581-599.
10. Calin GA, Croce CM. MicroRNA-cancer connection:the beginning of a new tale. Cancer Res 2006; 66(15):7390-1394.
11. Deng S, Calin GA, Croce CM, et al. Mechanisms of microRNA deregulation in human cancer. Cell Cycle 2008; 7(17):2643-2646.
12. Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993; 75: 843-854.
13. Carthew RW, Sontheimer EJ. Origins and Mechanisms of miRNAs and siRNAs. Cell 2009; 136:642-655.
14. Tazawa H, Tsuchiya N, Izumiya M, Nakagama H. Tumor-suppressive miR-34a induces senescence-like growth arrest through modulation of the E2F pathway in human colon cancer cells. Proceedings of the National Academy of Sciences of the United States of America 2007; 104:15472-15477.
15. Liu L, Chen L, Xu Y, Li R, Du X. microRNA-195 promotes apoptosis and suppresses tumorigenicity of human colorectal cancer cells. Biochemical and biophysical research communications 2010; 400:236-240.
16. Schetter AJ, Leung SY, Sohn JJ, et al. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. Jama 2008; 299: 425-436.
17. Lin M, Chen W, Huang J, et al. MicroRNA expression profiles in human colorectal cancers with liver metastases. Oncology reports 2011; 25:739-747.
18. Cissell KA, Deo SK. Trends in microRNA detection. Anal Bio-anal Chem 2009; 394(4):1109-1116.
19. Adachi T, Nakanishi M, Otsuka Y, et al. Plasma microRNA-499 as a biomarker of acute myocardial infarction. ClinChem 2010; 56(7):1183-1185.
20.王亚南,顾国浩.生物芯片在肺腺癌研究和诊疗中的应用进展.国际检验医学杂志2011；32(2)：230-231.
21. Kozomara A, Griffiths-Jones S. miRBase:integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2011; 39(Database issue):152-157.
22.汤海明,陈红,张静,任景怡,许宁.新一代测序技术应用于microRNA检测.遗传2012；34(6)：784-792.
23. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008; 456(7218):53-59.
24. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005,437(7057):376-380.
25. Smith DR, Quinlan AR, Peckham HE, Makowsky K, Tao W, Woolf B, Shen L, et al. Rapid whole-genome mutational profiling using next-generation sequencing technologies. Genome Res 2008; 18(10):1638-1642.
26. Kuchenbauer F, Mah SM, Heuser M, McPherson A, Ruschmann J, Rouhi A, et al. Comprehensive analysis of mammalian miRNA species and their role in myeloid cells. Blood 2011; 118(12):3350-3358.
27. Dupuis DE, Maas S. MiRNA editing. Methods Mol Biol 2010; 667:267-279.
28. Fernandez-Valverde SL, Taft RJ, Mattick JS. Dynamic isomiR regulation in Drosophila development. RNAna 2010; 16(10):1881-1888.
29. Thomson DW, Bracken CP, Goodall GJ. Experimental strategies for microRNA target identification. Nucleic Acids Res 2011; 39(16):6845-6853.
30.陈旭,史菊,蒋敏,顾国浩.MiRNA定量检测中内参基因的选择.国际检验医学杂志2012；33(11)：1338-1340.
31. Turchinovich A, Weiz L, Langheinz A, Burwinkel B. Characterization of extracellular circulating microRNA. Nucleic Acids Res 2011; 39(16):7223-7233.
32. Etheridge A, Lee I, Hood L, et al. Extracellular mircoRNA:a new source of biomarkers. Mutat Res,2011; 717(1/2):85-90.
33. Zhu HT, Dong QZ, Wang G, et al. Identification of suitable reference genes for qRT-PCR analysis of circulating microRNAs in hepatitis B virus-infected patients. Mol Biotechnol,2012; 50(1):49-56.
34. Song J, Bai Z, Han W, Zhang J, Meng H, et al. Identification of Suitable Reference Genes for qPCR Analysis of Serum microRNA in Gastric Cancer Patients. Dig Dis Sci 2012; 57:897-904.
35. Zanutto S, Pizzamiglio S, Ghilotti M, Bertan C, Ravagnani F, Perrone F, Leo E, Pilotti S, Verderio P, Gariboldi M, Pierotti MA. Circulating miR-378 in plasma:a reliable, haemolysis-independent biomarker for colorectal cancer. Br J Cancer 2014; 110(4):1001-7.
36. Hu Z, Chen X, Zhao Y, Tian T, Jin G, Shu Y, Chen Y, Xu L, Zen K, Zhang C, Shen H. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol 2010; 28(10):1721-6.
37. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, Peterson A, Noteboom J, O'Briant KC, Allen A, Lin DW, Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL, Nelson PS, Martin DB, Tewari M. Circulating microRNAs as stable blood-based markers for cancer detection[J]. Proc Natl Acad Sci USA 2008; 105(30):10513-10518.
38. Huang Z, Huang D, Ni S, Peng Z, Sheng W, Du X. Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. Int J Cancer 2010; 127:118-126.
39. Pu X, Huang G, Guo H, Guo C, Li H, Ye S, Ling S, Jiang L, Tian Y, Lin TY. Circulating miR-221 directly amplified from plasma is a potential diagnostic and prognostic marker of colorectal cancer and is correlated with p53 expression. J Gastroenterol Hepatol 2010; 25:1674-80.
40. Ng EK, Chong WW, Jin H, Lam EK, Shin VY, Yu J, Poon TC, Ng SS, Sung JJ. Differential expression of micrornas in plasma of patients with colorectal cancer:A potential marker for colorectal cancer screening. Gut 2009; 58(10):1375-81.
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42. Yanaihara N, Caplen N, Bowman E, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006; 9(3):189-198.
43. Zhou J, Yu L, Gao X, Hu J, Wang J, Dai Z, Wang JF, Zhang Z, Lu S, Huang X, Wang Z, Qiu S, et al. Plasma MicroRNA Panel to Diagnose Hepatitis B Virus-Related Hepatocellular Carcinoma. J of Clin Oncol 2011; 29:4781-4782.
44. Liu R, Chen X, Du Y, Yao W, Shen L, Wang C, Hu Z, Zhuang R, Ning G, Zhang C, Yuan Y, Li Z, et al. Serum MicroRNA Expression Profile as a Biomarker in the Diagnosis and Prognosis of Pancreatic Cancer. Clin Chem 2012; 58:610-618.
45. Asaga S, Kuo C, Nguyen T, Terpenning M, Giuliano AE, et al. Direct serum assay for microRNA-21 concentrations in early and advanced breast cancer. Clin Chem 2011; 57:84-91.
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47. Murakami Y, Toyoda H, Tanahashi T, Tanaka J, Kumada T, et al. Comprehensive miRNA expression analysis in peripheral blood can diagnose liver disease. PLoS One 2012; 7:e48366.
48. Mo MH, Chen L, Fu Y, Wang W, Fu SW. Cell-free Circulating miRNA Biomarkers in Cancer. J Cancer 2012; 3:432-448.
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51. Lardizabal MN, Nocito AL, Daniele SM, Ornella LA, Palatnik JF, et al. Reference Genes for Real-Time PCR Quantification of MicroRNAs and Messenger RNAs in Rat Models of Hepatotoxicity. PLoS One 2012; 7:e36323.
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2. Matanoski G, Tao X, Almon L, Adade AA, Davies-Cole JO. Demographics and tumor characteristics of colorectal cancers in the United States,1998-2001 Cancer. 2006; 107:1112-1120.
3. Zhao P, Dai M, Chen W, Li N. Cancer trends in China. Japanese journal of clinical oncology.2010; 40:281-285.
4. Yang L, Parkin DM, Li L, Chen Y. Time trends in cancer mortality in China: 1987-1999. International journal of cancer.2003; 106:771-783.
5.郑杰.结直肠息肉和结直肠癌.中华病理学杂志,2005；34(1)：4-5.
6. O'Conn ell JB, Maggard MA, Ko CY. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging. J Natl Cancer Inst 2004; 96:1420e5.
7. Lange CP, Laird PW. Clinical application of DNA methylation biomarkers in colorectal cancer. Epigenomics 2013; 5(2):105-108.
8. Zhang X, Xiao Z, Liu X, Du L, Wang L, Wang S, Zheng N, Zheng G, Li W, Zhang X, Dong Z, Zhuang X, Wang C. The potential role of ORM2 in the development of colorectal cancer. PLoS One 2012; 7(2):e31868.
9. Kin C, Kidess E, Poultsides GA, Visser BC, Jeffrey SS. Colorectal cancer diagnostics:biomarkers, cell-free DNA, circulating tumor cells and definingheterogeneous populations by single-cell analysis. Expert Rev Mol Diagn 2013; 13(6):581-599.
10. Calin GA, Croce CM. MicroRNA-cancer connection:the beginning of a new tale. Cancer Res 2006; 66(15):7390-1394.
11. Deng S, Calin GA, Croce CM, et al. Mechanisms of microRNA deregulation in human cancer. Cell Cycle 2008; 7(17):2643-2646.
12. Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993; 75: 843-854.
13. Carthew RW, Sontheimer EJ. Origins and Mechanisms of miRNAs and siRNAs. Cell 2009; 136:642-655.
14. Tazawa H, Tsuchiya N, Izumiya M, Nakagama H. Tumor-suppressive miR-34a induces senescence-like growth arrest through modulation of the E2F pathway in human colon cancer cells. Proceedings of the National Academy of Sciences of the United States of America 2007; 104:15472-15477.
15. Liu L, Chen L, Xu Y, Li R, Du X. microRNA-195 promotes apoptosis and suppresses tumorigenicity of human colorectal cancer cells. Biochemical and biophysical research communications 2010; 400:236-240.
16. Schetter AJ, Leung SY, Sohn JJ, et al. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. Jama 2008; 299: 425-436.
17. Lin M, Chen W, Huang J, et al. MicroRNA expression profiles in human colorectal cancers with liver metastases. Oncology reports 2011; 25:739-747.
18. Cissell KA, Deo SK. Trends in microRNA detection. Anal Bio-anal Chem 2009; 394(4):1109-1116.
19. Adachi T, Nakanishi M, Otsuka Y, et al. Plasma microRNA-499 as a biomarker of acute myocardial infarction. ClinChem 2010; 56(7):1183-1185.
20.王亚南,顾国浩.生物芯片在肺腺癌研究和诊疗中的应用进展.国际检验医学杂志2011；32(2)：230-231.
21. Kozomara A, Griffiths-Jones S. miRBase:integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2011; 39(Database issue):152-157.
22.汤海明,陈红,张静,任景怡,许宁.新一代测序技术应用于microRNA检测.遗传2012；34(6)：784-792.
23. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008; 456(7218):53-59.
24. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005,437(7057):376-380.
25. Smith DR, Quinlan AR, Peckham HE, Makowsky K, Tao W, Woolf B, Shen L, et al. Rapid whole-genome mutational profiling using next-generation sequencing technologies. Genome Res 2008; 18(10):1638-1642.
26. Kuchenbauer F, Mah SM, Heuser M, McPherson A, Ruschmann J, Rouhi A, et al. Comprehensive analysis of mammalian miRNA species and their role in myeloid cells. Blood 2011; 118(12):3350-3358.
27. Dupuis DE, Maas S. MiRNA editing. Methods Mol Biol 2010; 667:267-279.
28. Fernandez-Valverde SL, Taft RJ, Mattick JS. Dynamic isomiR regulation in Drosophila development. RNAna 2010; 16(10):1881-1888.
29. Thomson DW, Bracken CP, Goodall GJ. Experimental strategies for microRNA target identification. Nucleic Acids Res 2011; 39(16):6845-6853.
30.陈旭,史菊,蒋敏,顾国浩.MiRNA定量检测中内参基因的选择.国际检验医学杂志2012；33(11)：1338-1340.
31. Turchinovich A, Weiz L, Langheinz A, Burwinkel B. Characterization of extracellular circulating microRNA. Nucleic Acids Res 2011; 39(16):7223-7233.
32. Etheridge A, Lee I, Hood L, et al. Extracellular mircoRNA:a new source of biomarkers. Mutat Res,2011; 717(1/2):85-90.
33. Zhu HT, Dong QZ, Wang G, et al. Identification of suitable reference genes for qRT-PCR analysis of circulating microRNAs in hepatitis B virus-infected patients. Mol Biotechnol,2012; 50(1):49-56.
34. Song J, Bai Z, Han W, Zhang J, Meng H, et al. Identification of Suitable Reference Genes for qPCR Analysis of Serum microRNA in Gastric Cancer Patients. Dig Dis Sci 2012; 57:897-904.
35. Zanutto S, Pizzamiglio S, Ghilotti M, Bertan C, Ravagnani F, Perrone F, Leo E, Pilotti S, Verderio P, Gariboldi M, Pierotti MA. Circulating miR-378 in plasma:a reliable, haemolysis-independent biomarker for colorectal cancer. Br J Cancer 2014; 110(4):1001-7.
36. Hu Z, Chen X, Zhao Y, Tian T, Jin G, Shu Y, Chen Y, Xu L, Zen K, Zhang C, Shen H. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol 2010; 28(10):1721-6.
37. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, Peterson A, Noteboom J, O'Briant KC, Allen A, Lin DW, Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL, Nelson PS, Martin DB, Tewari M. Circulating microRNAs as stable blood-based markers for cancer detection[J]. Proc Natl Acad Sci USA 2008; 105(30):10513-10518.
38. Huang Z, Huang D, Ni S, Peng Z, Sheng W, Du X. Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. Int J Cancer 2010; 127:118-126.
39. Pu X, Huang G, Guo H, Guo C, Li H, Ye S, Ling S, Jiang L, Tian Y, Lin TY. Circulating miR-221 directly amplified from plasma is a potential diagnostic and prognostic marker of colorectal cancer and is correlated with p53 expression. J Gastroenterol Hepatol 2010; 25:1674-80.
40. Ng EK, Chong WW, Jin H, Lam EK, Shin VY, Yu J, Poon TC, Ng SS, Sung JJ. Differential expression of micrornas in plasma of patients with colorectal cancer:A potential marker for colorectal cancer screening. Gut 2009; 58(10):1375-81.
41. Lu, J, G. Getz, E. A. Miska, et al. MicroRNA expression profiles classify human cancers. Nature 2005; 435:834-838.
42. Yanaihara N, Caplen N, Bowman E, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 2006; 9(3):189-198.
43. Zhou J, Yu L, Gao X, Hu J, Wang J, Dai Z, Wang JF, Zhang Z, Lu S, Huang X, Wang Z, Qiu S, et al. Plasma MicroRNA Panel to Diagnose Hepatitis B Virus-Related Hepatocellular Carcinoma. J of Clin Oncol 2011; 29:4781-4782.
44. Liu R, Chen X, Du Y, Yao W, Shen L, Wang C, Hu Z, Zhuang R, Ning G, Zhang C, Yuan Y, Li Z, et al. Serum MicroRNA Expression Profile as a Biomarker in the Diagnosis and Prognosis of Pancreatic Cancer. Clin Chem 2012; 58:610-618.
45. Asaga S, Kuo C, Nguyen T, Terpenning M, Giuliano AE, et al. Direct serum assay for microRNA-21 concentrations in early and advanced breast cancer. Clin Chem 2011; 57:84-91.
46. Jones CI, Zabolotskaya MV, King AJ, Stewart HJ, Home GA, et al. (2012) Identification of circulating microRNAs as diagnostic biomarkers for use in multiple myeloma. Br J Cancer 107:1987-1996.
47. Murakami Y, Toyoda H, Tanahashi T, Tanaka J, Kumada T, et al. Comprehensive miRNA expression analysis in peripheral blood can diagnose liver disease. PLoS One 2012; 7:e48366.
48. Mo MH, Chen L, Fu Y, Wang W, Fu SW. Cell-free Circulating miRNA Biomarkers in Cancer. J Cancer 2012; 3:432-448.
49. Chen X, Ba Y, Ma L, Cai X, Yin Y, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 2008; 18:997-1006.
50. Bargaje R, Hariharan M, Scaria V, Pillai B. Consensus miRNA expression profiles derived from interplatform normalization of microarray data. RNA 2010; 16:16-25.
51. Lardizabal MN, Nocito AL, Daniele SM, Ornella LA, Palatnik JF, et al. Reference Genes for Real-Time PCR Quantification of MicroRNAs and Messenger RNAs in Rat Models of Hepatotoxicity. PLoS One 2012; 7:e36323.
52. Szabo A, Perou CM, Karaca M, et al. Statistical modeling for selecting housekeeper genes. Genome boil,2008; 9(8):R59.
53. Finnegan EJ, Matzke MA. The small RNA world. J Cell Sci 2003; 116(Pt23): 4689-4693.
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