摘要
冠心病已成为危害人类健康的严重公共卫生问题和社会问题。中医药在改善冠心病患者症状和提高冠心病患者生活质量中发挥着重要的作用,其起效的核心在于辨证论治。其实质是对冠心病的患者进行分层,确定不同的亚型,再进行个体化的治疗,以达到最大限度的发挥药物的治疗作用和减少药物的不良反应。辨证论治的优势在于根据宏观的症状群,即可确定个体化的治疗方案；而其不足之处在于缺乏客观定量的生物学指标。在中医理论的指导下,流行病学显示血瘀证是冠心病的主要证候,寻找冠心病血瘀证相关的生物标志物,有助于对冠心病患者进行进一步分层,以指导临床治疗。
microRNA (miRNA)是一类非编码小RNA分子,可以导致其靶基因的降解或阻碍其靶基因的翻译。miRNA的表达异常引起相应生物网络的紊乱是疾病发生的重要原因之一。新近研究显示miRNA的表达模式在诸多的心血管疾病中发生了变化,如动脉粥样硬化、高血压、心律失常、心肌梗死、心肌肥厚及心力衰竭。miRNA在冠心病中作为生物标志物和治疗靶点已经开展了广泛的研究。miRNA与冠心病密切相关,但目前仍未有探索miRNA作为冠心病血瘀证生物标志物的研究。将miRNA引入冠心病血瘀证生物标志物的研究,为寻找冠心病血瘀证生物标志物提供了新的思路。本课题采用系统生物学的方法寻找不稳定性心绞痛血瘀证的生物标志物,探索其发生机制,期望能为不稳定性心绞痛血瘀证的诊断提供新的生物标志物,同时为其治疗提供新的靶点。
1目的
检测不稳定性心绞痛血瘀证患者,不稳定性心绞痛痰浊证患者,急性缺血性中风血瘀证患者和健康对照者的microRNA表达谱和基因表达谱,寻找起关键调控作用的差异表达的miRNA和基因,确定各组能够作为生物标志物的rniRNA及其靶基因,对各组的生物标志物的表达模式进行验证。探索miRNA及其靶基因作为不稳定性心绞痛血瘀证的生物标志物的可行性,为不稳定性心绞痛患者的临床分层提供依据。
2研究方法
2.1外周血单核细胞(PBMC)的分离和总RNA的提取
选取符合西医疾病诊断标准和中医证侯诊断标准的患者,分为4组,包括不稳定性心绞痛血瘀证患者、不稳定性心绞痛痰浊证患者、急性缺血性中风血瘀证患者和健康对照者。每组纳入20例,抽取外周血,分离PBMC,提取总RNA。采用紫外吸收法检测总RNA的纯度和浓度,变性琼脂糖凝胶电泳检测总RNA的完整性。
2.2外周血单核细胞的microRNA和基因的表达谱检测
每组取5例病例的PBMC的总RNA用于microRNA芯片和基因芯片检测。采用Phalanx公司生产的Human miRNA OneArray? v4芯片和Human Whole Genome OneArray? v5芯片进行检测。
2.3microRNA和基因的表达谱的生物信息学整合分析
根据各组与正常组的比值的对数值和P值进行差异表达的microRNA和基因的筛选。运用聚类软件Cluster3.0进行average-linkage层次聚类运算。根据聚类结果,运用Java TreeView-1.1.6r2绘制热图。采用在线分析软件DAVID6.7进行差异基因富集的KEGG通路分析。基于microRNA对靶基因的负向性调控,根据不同组中的差异表达的]microRNA的情况,采取不同的整合通路和网络分析方法。运用miRTrail构建microRNA和靶基因相互作用网络。运用网络分析和可视化软件BiNA生成nicroRNA和靶基因相互作用网络图。
2.4qRT-PCR验证
根据各组差异表达的microRNA和基因的通路和网络分析结果,确定各组中起关键调控作用的microRNA及其靶基因。每组中除用于microRNA芯片和基因芯片检测的5例标本外,剩余的15例标本用于qRT-PCR验证各组中起关键调控作用的microRNA及其靶基因的表达模式。
2.5统计学方法
计量资料用均数±标准差表示。样本量小于50采Shapiro-Wilk检验判断是否符合正态分布,样本量大于50采用Kolmogorov-Smirnov检验判断是否符合正态分布。两组间计量资料比较时,不符合正态分布的组采用Mann-Whitney秩和检验；符合正态分布的,采用Levene's检验判断方差是否具有齐性,方差齐的组采用独立样本的t检验(unpaired Student's t test);方差不齐的组采用近似t检验(approximate Student's t test)。多组间计量资料比较时,不符合正态分布的组采用Kruskal-Wallis秩和检验；符合正态分布的,采用Levene's检验判断方差足否具有齐性,方差齐的组采用单因素方差分析(one-way ANOVA);方差不齐的组采用Kruskal-Wallis秩和检验。计数资料进行卡方检验,但足若行×列表格中有1/5以上的格子理论频数<5,或有一个格子理论频数<1则采用似然比卡方的结果。采用SPSS16.0统计软件分析数据,P<0.05为差异有统计学意义。采用GraphPad Prism5软件根据统计结果绘制柱状图和盒状图。
3结果
3.1一般临床资料
对不稳定性心绞痛血瘀证组、不稳定性心绞痛痰浊证组、急性缺血性中风血瘀证组和健康对照组的一般临床资料进行了统计分析,发现4组用于nicroRNA芯片和基因芯片检测的年龄、性别、BMI、吸烟史、2型糖尿病、CHO、LDL、 HDL、TG和CRP方面比较均无显著性差异(P>0.05)。4组用于qRT-PCR验证的年龄、性别、BMI、吸烟史、2型糖尿病、CHO、LDL、HDL和CRP比较均无显著性差异(P>0.05)。
3.2外周血单核细胞的microRNA和基因的表达谱检测
在microRNA表达谱中,不稳定性心绞痛血瘀证组和健康对照组相比,25个microRNA的表达存在差异,其中23个microRNA上调,2个microRNA下调；不稳定性心绞痛痰浊证组和健康对照组相比,11个microRNA的表达存在差异,其中2个microRNA上调,9个microRNA下调；急性缺血性中风血瘀证组和健康对照组相比,20个microRNA的表达上调,无microRNA下调。各组差异表达的microRNA见表7。在基因表达谱中,不稳定性心绞痛血瘀证组和健康对照组相比,1081个基因的表达存在差异,其中673个基因上调,408个基因下调；不稳定性心绞痛痰浊证组和健康对照组相比,697个基因的表达存在差异,其中451个基因上调,246个基因下调；急性缺血性中风血瘀证组和健康对照组相比,546个基因的表达存在差异,其中383个基因上调,163个基因下调。
3.3microRNA和基因表达谱的生物信息学分析
各组差异表达的基因的聚类分析产生的热图结果显示,每组的5个样本聚为一类,显示各组组内的样本一致性较好。各组与健康对照组相比,1nicroRNA和基因的表达差异显著,可以通过microRNA和基因的表达差异将各组与健康对照组区分开。DAVID软件分析各组差异表达的基因富集的KEGG通路的结果如下：在不稳定性心绞痛血瘀证组上调的基因富集的7条通路中,NOD样受体信号通路、凋亡通路和细胞因子和受体相互作用通路与不稳定性心绞痛密切相关。不稳定性心绞痛血瘀证组下调的基因富集的6条通路中,抗原呈递和处理通路和p53信号通路与不稳定性心绞痛密切相关。不稳定性心绞痛痰浊证组下调的基因富集的7条通路中,抗原呈递和处理通路和NK细胞介导的细胞毒性作用通路与不稳定性心绞痛密切相关。不稳定性心绞痛痰浊证组上调的基因富集的6条通路中,MAPK信号通路、NOD样受体信号通路和趋化因子信号通路与不稳定性心绞痛密切相关。急性缺血性中风血瘀证组上调的基因富集的3条通路中,NOD样受体信号通路和MAPK信号通路与急性缺血性中风密切相关。急性缺血性中风血瘀证组下调的基因富集的3条通路中,NK细胞介导的细胞毒性作用通路和细胞因子和受体相互作用通路与急性缺血性中风密切相关。
在miRTrail构建的microRNA和靶基因相互作用网络中,6个上调的microRNA和115个下调的靶基因构成了不稳定性心绞痛血瘀证的网络,1个下调的microRNA和10个上调的靶基因构成了不稳定性心绞痛痰浊证的网络,5个上调的microRNA和24个实际下调的靶基因构成了急性缺血性中风血瘀证的网络。
3.4qRT-PCR验证
和健康对照组相比,miR-146b-5p、miR-199a-3p和miR-199a-5p在不稳定性心绞痛血瘀证组和急性缺血性中风血瘀证组中表达上调,差异有统计学意义(P<0.05),在不稳定性心绞痛痰浊证组中无明显变化；miR-146b-5p、miR-199a-3p和miR-199a-5p在不稳定性心绞痛血瘀证组和急性缺血性中风血瘀证组之间相比,表达无差异;miR-363a-5p和miR-668在不稳定性心绞痛痰浊证组中表达下调,差异有统计学意义(P<0.05),在不稳定性心绞痛血瘀证组和急性缺血性中风血瘀证组中无明显变化。
和健康对照组相比,CALR在不稳定性心绞痛血瘀证组中表达下调,差异有统计学意义(P<0.05),在不稳定性心绞痛痰浊证组和急性缺血性中风血瘀证组中无明显变化；TP53在不稳定性心绞痛血瘀证组和急性缺血性中风血瘀证组中表达下调,差异有统计学意义(P<0.05),在不稳定性心绞痛痰浊证组中无明显变化；RIPK2在三组之中表达都升高,差异有统计学意义(P<0.05)；RIPK2在三组之间相比,表达无差异；STK4在不稳定性心绞痛血瘀证组和不稳定性心绞痛痰浊证组中表达升高,差异有统计学意义(P<0.05),在急性缺血性中风血瘀证组中无明显变化；STK4在不稳定性心绞痛血瘀证组和不稳定性心绞痛痰浊证组之间相比,表达无差异；IL2RB在不稳定性心绞痛血瘀证组和急性缺血性中风血瘀证组中表达下调,差异有统计学意义(P<0.05),在不稳定性心绞痛痰浊证组中无明显变化；IL2RB在不稳定性心绞痛血瘀证组和急性缺血.性中风血瘀证组之间相比,表达无差异；FASLG在急性缺血性中风血瘀证和不稳定性心绞痛痰浊证组中表达下调,差异有统计学意义(P<0.05),在不稳定性心绞痛血瘀证组中无明显变化；FASLG在急性缺血性中风血瘀证和不稳定性心绞痛痰浊证组之间相比,表达无差异。
4结论
4.1microRNA芯片检测发现,与健康对照者相比,不稳定性心绞痛血瘀证、不稳定性心绞痛痰浊证和急性缺血性中风血瘀证的microRNA表达谱存在差异。
4.2基于通路和网络相结合的生物信息学分析发现各组中具有关键调控作用的microRNA和靶基因如下：在不稳定性心绞痛血瘀证中,上调的miR-146b-5p和miR-199a-5p可能通过下调CALR和TP53以减轻炎症和凋亡；在不稳定性心绞痛痰浊证中,下调的miR-363-5p和miR-668可能通过上调RIPK2和STK4以促进炎症和凋亡;在急性缺血性中风血瘀证中,上调的miR-146b-5p和miR-199a-3p可能通过下调IL2RB和FASLG以减轻炎症和凋亡。
4.3qRT-PCR验证证实了各种证候中具有关键调控作用的microRNA和靶基因的表达模式,其结果提示：miR-146b-5p、miR-199a-5p、CALR和TP53可以作为不稳定性心绞痛血瘀证患者的生物标志物,miR-363-5p、miR-668、RIPK2和STK4可以作为不稳定性心绞痛痰浊证患者的生物标志物,miR-146b-5p、miR-199a-3p、 IL2RB和FASLG可以作为急性缺血性中风血瘀证患者的生物标志物。
4.4在不稳定性心绞痛中,血瘀证患者的1niR-146b-5p和miR-199a-5p表达上调,而CALR和TP53表达下调；痰浊证患者的miR-363-5p和miR-668表达上调,而RIPK2和STK4表达下调。这表明“同病异证”在microRNA及其靶基因表达层面具有其生物学基础。
4.5在不稳定性心绞痛血瘀证和急性缺血性中风血瘀证中,miR-146b-5p、 miR-199a-3p和miR-199a-5p都表达上调,而TP53和IL2RB都表达下调。这表明“异病同证”在microRNA及其靶基因表达层面具有其生物学基础。
4.6不稳定性心绞痛血瘀证和急性缺血性中风血瘀证在差异表达的rnicroRNA和靶基因方面具有相似性,不同之处在于调控的关键靶基因仍有差异。从microRNA及其靶基因表达层面分析“异病同证”由于其病变部位的不同,仍然存在差异性。
Coronary artery disease (CAD) has become a serious public health and social issue which jeopardizes human health. Increasing clinical evidence has demonstrated the use of Traditional Chinese medicine (TCM) in CAD patients could improve patients'symptoms and quality of life. The key effective mechanism of TCM is that all diagnoses and treatments in CM are based on differentiation of the syndrome. The essence of syndrome differentiation is to stratify patients for identifying subtypes of the same disease, so personalized treatment can be given and thus optimize drug therapy can be achieved with maximum efficacy and minimal adverse effects. The advantage of syndrome differentiation is that personalized treatment can be identified by analysis of profiles of symptoms, while its disadvantage is that it is lack of objective and quantitative biomedical indicators. Guided by the theory of TCM, the epidemiological investigation has demonstrated that blood stasis syndrome (BSS) is the major type of syndrome in CAD patients. Looking for the related biomarkers of BSS of CAD patients will be helpful for the stratification of CAD patients.
MicroRNAs (miRNAs) are non-protein-coding small RNAs by targeting mRNAs for cleavage or translational repression. It is one of the key mechanisms of diseases that the disorder of the relevant biological networks caused by the deregulation of miRNAs.Recent studies have demonstrated that miRNAs expression patterns change in various cardiovascular diseases, such as atherosclerosis, hypertension, arrhythmia, myocardial infarction, cardiac hypertrophy and heart failure. There have been lots of studies about miRNAs as biomarkers and therapeutic targets of CAD. Although miRNAs are closely related with CAD, there is still lack of study that explores miRNAs as biomarkers of BSS of CAD. If miRNAs are introduced into the studies of biomarkers of BSS of CAD, it will provide new thinking. The purpose of this study was to investigate biomarkers of BSS of UA patients and their relative biomedical mechanisms by a systems biology approach. The study may provide new biomarkers and therapeutic targets for BSS of UA patients.
1Objective
The purpose of this study was to test expression profiles of miRNAs and genes in BSS of UA patients, phlegm syndrome (PS) of UA patients, BSS of acute ischemic stroke (AIS) patients and healthy controls, look for key regulating miRNAs and genes which were differentially expressed, identify miRNAs and their target genes as biomarkers, and validate the expression pattern of biomarkers in each group. The feasibility of using miRNAs and their target genes as biomarkers was explored, and this could be helpful for the clinical stratification of UA patients.
2Method
2.1Plasma collection and RNA isolation
Patients who were fit for diagnostic standard of disease and syndrome were included in the study. The study populations were divided into4groups, including BSS of UA patients, PS of UA patients, BSS of AIS patients and healthy controls.20subjects were included in each group. Whole blood samples were drawn from each participant and total RNA was isolated. RNA quantity and purity was assessed using ultraviolet ray absorption method, and RNA Integrity Number (RIN) values are ascertained using agarose gel electrophoresis.
2.2The test of expression profiles of miRNAs and genes of PBMCs
In each group, total RNAs of PBMCs (peripheral blood mononuclear cells) of5participants were used for the test of expression profiles of miRNAs and genes. miRNA expression profile was test by using the Human miRNA OneArray? v4and gene expression profile was test by the Human Whole Genome OneArray? v5.
2.3Integrated bioinformatics analysis of the genes and microRNAs expression profiles
Identification of differentially expressed genes and miRNAs was based on log2ratios and P value. An average linkage hierarchical clustering was performed with clustering software Cluster3.0and Java Tree View-1.1.6r2was applied to generate the heatmap. We used DAVID Bioinformatics Resources6.7to identify enriched KEGG pathways. According to differentially expressed miRNAs in each group and the negative regulation of miRNAs on target genes, different integrative pathway and network analysis was made. The interactive networks between miRNAs and their target genes were built by the miRTrail. The interactive network of selected miRNAs and actually deregulated target genes was visualized by the network analyzers and viewers BiNA.
2.4qRT-PCR validation
According to the results of pathway analysis and network analysis, the key regulating miRNAs and genes were identified. In each group, except5participants who were used for miRNA and gene expression profile analysis, total RNAs of PBMCs of the rest15participants were used for qRT-PCR validation of expression patterns of the key regulating miRNAs and genes in each group.
2.5Statistics
All results for quantitative data were expressed as means±SEM. If the sample size was less than50, Shapiro-Wilk test would be used to evaluate whether they followed the normal distribution. If the sample size was more than50, Kolmogorov-Smirnov test would be used to evaluate whether they followed the normal distribution. In2group comparisons of quantitative data, for the data that did not fit the normal distribution, Mann-Whitney test was performed, while for the data of normal distribution. Levene's test of homogeneity of variance was further performed. When the data fitted the homogeneity of variance, unpaired Student's t test was applied, and for the data that did not fit the homogeneity of variance, the approximate t test was performed. In3group comparisons of quantitative data, for the data that did not fit the normal distribution, Kruskal-Wallis test was performed, while for the data of normal distribution, Levene's test of homogeneity of variance was further performed. When the data fitted the homogeneity of variance, one-way ANOVA was applied, and for the data that did not fit the homogeneity of variance, Kruskal-Wallis test was performed. For categorical variables, Chi-square test, Chi-square test with continuity correction or Fischer's exact test was used. All tests were performed2-sided and a significance level of P<0.05was considered to indicate statistical significance. For all statistical analyses, the statistical software SPSS16.0(Statistical Package for the Social Sciences, Chicago, IL, USA) for Windows was used. GraphPad Prism5(GraphPad software, San Diego, CA, USA) was used to draw bar and box chars.
3Results
3.1Basic clinical characteristics of subjects
The clinical characteristics of the4group were analyzed. There were no significant differences in age, percentage of males, BMI (body mass index), percentage of active smoker, history of type2diabetes mellitus, total cholesterol, LDL cholesterol, HDL cholesterol, tiglycerides and CRP (P>0.05) among4groups for miRNA and gene expression profile test. There were no significant differences in age, percentage of males, BMI (body mass index), percentage of active smoker, history of type2diabetes mellitus, total cholesterol, LDL cholesterol, HDL cholesterol and CRP (P>0.05) among4groups for qRT-PCR validation.
3.2The test of expression profiles of miRNAs and genes of PBMCs
A list of25miRNAs was identified as differentially expressed between UA patients with BSS and the healthy control:23overexpressed and2underexpressed.A list of11miRNAs was identified as differentially expressed between UA patients with PS and the healthy control:2overexpressed and9underexpressed. A list of20miRNAs was identified as all overexpressed between AIS patients with BSS and the healthy control. A list of1081mRNAs was identified as differentially expressed between UA patients with BSS and the healthy control:673overexpressed and408underexpressed.A list of697mRNAs was identified as differentially expressed between UA patients with PS and the healthy control:451overexpressed and246underexpressed. A list of546mRNAs was identified as differentially expressed between AIS patients with BSS and the healthy control:383overexpressed and163underexpressed.
3.3Integrated bioinformatics analysis of the genes and microRNAs expression profiles
According to the results of heatmaps generated by hierarchical clustering analysis,5samples within each group were grouped into1cluster and this indicated that the overall reproducibility was good in each group. Compared with healthy controls group, there were significantly different expressions of miRNAs and genes in BSS of UA patients, PS of UA patients and BSS of AIS patients. Based on different expressions of miRNAs and genes, other groups could be distinguished from healthy controls group. The results of analyzing KEGG pathways enriched by deregulated genes using DAVID were as follows. In UA patients with BSS group, among7pathways enriched of upregulated genes, NOD-like receptor signaling pathway, apoptosis pathway and cytokine-cytokine receptor interaction pathway were closely related with UA, while among6pathways enriched of downregulated genes, the antigen processing and presentation pathway and p53signaling pathway were closely related with UA. In UA patients with PS group, among6pathways enriched of upregulated genes, MAPK signaling pathway, NOD-like receptor signaling pathway and chemokine signaling pathway were closely related with UA, while among7pathways enriched of downregulated genes, the antigen processing and presentation pathway and natural killer cell mediated cytotoxicity pathway were closely related with UA. In BSS of AIS patients, among3pathways enriched of upregulated genes, NOD-like receptor signaling pathway and MAPK signaling pathway were closely related with AIS, while among3pathways enriched of downregulated genes, the natural killer cell mediated cytotoxicity pathway and cytokine-cytokine receptor interaction pathway were closely related with AIS.
In the interactive networks between miRNAs and their target genes built by the miRTrail,6upregulated miRNAs and115downregulated target genes composed the network of BSS of UA patients,1downregulated miRNAs and10upregulated genes composed the network of PS of UA patients, and5upregulated miRNAs and24downregulated genes composed the network of BSS of AIS patients.
3.4qRT-PCR validation
Compared with healthy controls group, miR-146b-5p, miR-199a-3p and miR-199a-5p were significantly upregulated in BSS of UA group and BSS of AIS group (P<0.05). while miR-146b-5p, miR-199a-3p and miR-199a-5p were no significant difference in PS of UA group. There was no significant difference in the expression of miR-146b-5p,miR-199a-3p and miR-199a-5p between BSS of UA group and BSS of AIS group. Compared with healthy controls group, miR-363a-5p and miR-668were significantly downregulated in PS of UA group (P<0.05). while miR-363a-5p and miR-668were no significant difference in BSS of UA group and BSS of AIS group.
Compared with healthy controls group, CALR was significantly downregulated in BSS of UA group (P<0.05), while CALR was no significant difference in PS of UA group and BSS of AIS group. Compared with healthy controls group. TP53was significantly downregulated in BSS of UA group and BSS of AIS group (P<0.05), while TP53was no significant difference in PS of UA group. Compared with healthy controls group, RIPK2was significantly upregulated in BSS of UA group, PS of UA group and BSS of AIS group (P<0.05). There was no significant difference in the expression of RIPK2among BSS of UA group, PS of UA group and BSS of AIS group. Compared with healthy controls group, STK4was significantly upregulated in BSS of UA group and PS of UA group (P<0.05), while STK4was no significant difference in BSS of AIS group. There was no significant difference in the expression of STK4between BSS of UA group and PS of UA group. Compared with healthy controls group, IL2RB was significantly downregulated in BSS of UA group and BSS of AIS group (P<0.05), while IL2RB was no significant difference in PS of UA group. There was no significant difference in the expression of IL2RB between BSS of UA group and BSS of AIS group. Compared with healthy controls group, FASLG was significantly downregulated in PS of UA group and BSS of AIS group (P<0.05), while FASLG was no significant difference in BSS of UA group. There was no significant difference in the expression of FASLG between PS of UA group and BSS of AIS group.
4Conclusions
4.1Compared with healthy controls group, there were significant differences in miRNA expression among BSS of UA group, PS of UA group and BSS of AIS group.
4.2Bioinformatics analysis which combined pathway and network analysis identified key regulating miRNAs and target genes in each group. In BSS of UA group, upregulated miR-146b-5p and miR-199a-5p may downregulate CALR and TP53to attenuate apoptosis and inflammation. In PS of UA group, downregulated miR-363-5p and miR-668may upregulate RIPK2and STK4to promote apoptosis and inflammation. In BSS of AIS group, upregulated miR-146b-5p and miR-199a-3p may downregulate IL2RB and FASLG to attenuate apoptosis and inflammation.
4.3The qRT-PCR validation confirmed the expression patterns of the key regulating miRNAs and genes in each group. It indicated that miR-146b-5p, miR-199a-5p, CALR and TP53could be significant biomarkers of BSS of UA patients, miR-363-5p, miR-668, STK4and RIPK2could be significant biomarkers of PS of UA patients, and miR-146b-5p, miR-199a-3p, IL2RB and FASLG could be significant biomarkers of BSS of AIS patients.
4.4In UA patients, miR-146b-5p and miR-199a-5p were upregulated and CALR and TP53were downregulated in BSS patients, while miR-363-5p and miR-668were downregulated and STK4and RIPK2were upregulated in PS patients. It indicated that the biological mechanisms of different syndromes in the same disease may be related with the deregulation of miRNAs and target genes.
4.5In BSS of UA patients and BSS of AIS patients, miR-146b-5p, miR-199a-3p and miR-199a-5p were all upregulated, while TP53and IL2RB were all downregulated. It indicated that the biological mechanisms of the same syndrome in different diseases may be related with the deregulation of miRNAs and target genes.
4.6Although BSS of UA patients and BSS of AIS patients had some similarities in the deregulation of miRNAs and target genes, there were still some differences in their key regulating target genes. It indicated that the same syndrome in different diseases may have some differences in miRNAs and target genes due to the differences of location of diseases.
引文
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