| 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.1ObjectiveThe 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.2Method2.1Plasma collection and RNA isolationPatients 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 PBMCsIn 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 profilesIdentification 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 validationAccording 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. 2.5StatisticsAll 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.3Results3.1Basic clinical characteristics of subjectsThe 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 PBMCsA 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 profilesAccording 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 validationCompared 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.4Conclusions4.1Compared 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. |
PDF Full Text Request