Comprehensive miRNA expression analysis in peripheral blood can diagnose liver disease

Abstract

Background: miRNAs circulating in the blood in a cell-free form have been acknowledged for their potential as readily accessible disease markers. Presently, histological examination is the golden standard for diagnosing and grading liver disease, therefore non-invasive options are desirable. Here, we investigated if miRNA expression profile in exosome rich fractionated serum could be useful for determining the disease parameters in patients with chronic hepatitis C (CHC).

Methodology: Exosome rich fractionated RNA was extracted from the serum of 64 CHC and 24 controls with normal liver (NL). Extracted RNA was subjected to miRNA profiling by microarray and real-time qPCR analysis. The miRNA expression profiles from 4 chronic hepatitis B (CHB) and 12 non alcoholic steatohepatitis (NASH) patients were also established. The resulting miRNA expression was compared to the stage or grade of CHC determined by blood examination and histological inspection.

Principal findings: miRNAs implicated in chronic liver disease and inflammation showed expression profiles that differed from those in NL and varied among the types and grades of liver diseases. Using the expression patterns of nine miRNAs, we classified CHC and NL with 96.59% accuracy. Additionally, we could link miRNA expression pattern with liver fibrosis stage and grade of liver inflammation in CHC. In particular, the miRNA expression pattern for early fibrotic stage differed greatly from that observed in high inflammation grades.

Conclusions: We demonstrated that miRNA expression pattern in exosome rich fractionated serum shows a high potential as a biomarker for diagnosing the grade and stage of liver diseases.

Figure 1. The method used to obtain reproducible data for microarray analysis conducted on serum-extracted samples.

A. NL patients’ serum were sampled twice. In the first, RNA was extracted first from untreated serum, and then extracted again from serum treated with exoquick. In the second serum sample, RNA was also extracted from both untreated serum and serum treated with exoquick. Microarray analysis was conducted for RNA in a total of four samples. B. Reproducibility test of microarray data. Scatter plots comparing non- normalized signal intensities of miRNAs in two independent experiments from human total serum and exosome rich fraction. Red and black denotes high and low miRNA expressions respectively. Total serum extracted first, versus exosome rich fraction first (left), total serum extracted first versus second (middle), and exosome rich fraction extracted first versus second (right). C. Pearson’s pairwise correlations of signal intensities of miRNAs from human total serum and exosome rich fraction. D. Western blot was performed for untreated serum, serum extracted by exoquick and exosome fraction from PNT-2, using anti-CD63.

Figure 2. Expression patterns of miRNA used for discriminating between CHC and NL.

A. Box plots of expression patterns of the nine miRNAs used for discriminating between CHC and NL. B. Classification of CHC and NL using LOOCV from miRNA expression profile. C. PCA in CHC and NL. The two dimensional embedding of CHC and NL by PCA. The first and second principal component scores computed (not selected for discrimination) of normalized miRNA expression were employed for this plot. Computation was done with ALL.

Figure 3. Pairwise heatmap of the miRNAs used for classifying two arbitrary groups.

Pairwise heatmap showed the miRNAs and their p-value of two arbitrary groups.

Figure 4. Pairwise heatmap of the miRNAs used for classifying among four groups.

Figure 5. Significantly differentially expressed miRNAs according to liver inflammation grade.

Pairwise heatmap showing the miRNAs and p-value of two arbitrary grades.

Figure 6. Significantly differentially expressed miRNA according to liver fibrotic stage.

Pairwise heatmap showing the miRNAs and p-value of two arbitrary stages.

Figure 7. Determining liver inflammation grade and fibrotic stage using miRNA expression pattern in LOOCV analysis.

A. In order to diagnose the grade of liver inflammation, A0 was identified first. Next A1, A2, and A3 were identified in a similar manner as A0. For each, the accuracy rate, P value, and the odds ratio are shown. B. For liver fibrosis stage, F0 was first diagnosed following which the other stages F1, F2, and F3 were diagnosed in a similar manner. For each group the accuracy rate, P value, and the odds ratio are shown.

Figure 8. Real-time qPCR validation of microarray analysis.

The microarray expression analysis result of four miRNAs was reproduced in real-time PCR analysis. The pairs with p<0.001 are marked by “***”.

Figure 9. The list of miRNAs used to obtain the maximum correlation coefficient between miRNA expression level, and clinical characteristics.

Pairwise heatmap showing miRNAs and their correlation coefficient and p-values.