Transcriptional Profiling and miRNA-Target Network Analysis Identify Potential Biomarkers for Efficacy Evaluation of Fuzheng-Huayu Formula-Treated Hepatitis B Caused Liver Cirrhosis

Abstract

Fuzheng-Huayu (FZHY) formula has been found to have a satisfactory effect on hepatitis B-caused cirrhosis (HBC) treatment. However, the efficacy evaluation of FZHY is often challenging. In this study, a randomized, double-blind and placebo-controlled trial was used to evaluate the therapeutic efficacy of FZHY in HBC treatment. In the trial, 35 medical indexes were detected, and 14 indexes had a statistically-significant difference before compared to after the trial. Importantly, the Child-Pugh score also demonstrated FZHY having therapeutic efficacy. Furthermore, the microRNA (miRNA) profiles of 12 serum samples were detected in FZHY groups, and 112 differential-expressed (DE) miRNAs were determined. Using predicted miRNA targets, 13 kernel miRNAs were identified from the established miRNA-target network. Subsequently, quantitative Real-time Polymerase Chain Reaction (qRT-PCR) was used to validate the expression level of 13 identified miRNAs in the trials. The results showed that nine miRNAs have a statistically-significant difference before compared to after FZHY treatment. By means of a logistic regression model, a miRNA panel with hsa-miR-18a-5p, -326, -1182 and -193b-5p was established, and it can clearly improve the accuracy of the efficacy evaluation of FZHY. This study suggested that the particular miRNAs can act as potential biomarkers and obviously increase the diagnostic accuracy for drug evaluation in HBC treatment progression.

Keywords: Fuzheng-Huayu (FZHY) formula; hepatitis B-caused cirrhosis (HBC); miRNA-target network; microRNA.

Figure 1

The clinical data analysis results between before and after treatment of Fuzheng-Huayu (FZHY) and placebo groups. (A) The Child–Pugh total score and classification comparison between FZHY and placebo groups; (B) the p-values’ distribution of medical indexes, which were tested using the random variance model of R package; (C) the expression levels of 14 significant medical indexes between FZHY and the placebo groups. To draw the figures conveniently, the detected value of each medical index was assigned a code (Arabic numerals); for example, 1 represents that the detected value is the normal level; 2 represents that the value is the abnormal level, but not worse; and 3 represents that the detected value is the abnormal level and worse.

Figure 2

The bioinformatics analysis of miRNAs and target genes between before and after FZHY treatment for hepatitis B-caused cirrhosis (HBC) patients. (A) Heat-map of the differential expression of miRNAs between before and after FZHY treatment. Red color represents that the miRNA is upregulated, and green color represents downregulated. The relationship among the samples was divided by binary tree classification and is shown in the upper portion. The hierarchical cluster of miRNAs is displayed at the bottom; (B) The distribution of differential expressed (DE) miRNA expression levels; the right side represents the up-expressed miRNAs between before and after FZHY treatment; the left side represents the down-expressed miRNAs between before and after FZHY treatment; (C–E) MiRNA target genes related Gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and disease terms.

Figure 3

The miRNA-target network was constructed based on DE miRNAs and target genes, and the related clusters were identified using the ClusterONE algorithm. (A) The global profiles of the FZHY treatment-related miRNA-target network were built. The target genes of miRNAs were predicted using the TarBase (v7.0), miRecords and miRTarBase databases and the miRanda, miRDB, miRWalk and RNAhybrid prediction programs; (B) The kernel miRNA-related clusters isolated from the network using the ClusterONE algorithm, which was defined as node > 5, density > 0.05, quality > 0.60 and p < 0.0001. There are 12 clusters, containing 13 kernel miRNAs.

Figure 4

The potential kernel miRNAs were validated using the qPCR method. (A) Expression levels of 13 kernel miRNAs in the FZHY group. Illustrated p-values are based on pair-wise comparisons by the Mann–Whitney U-test. The result show that hsa-miR-1182, -18a-5p, -18b-5p, -193b-5p, -23a-5p, -326, -378a-5p, -564 and -760 (p < 0.05) have a statistically significant difference, while has-miR-1225-3p, -18b-3p, -1915-3p and -324-3p are insignificant between before and after trial; (B) Expression levels of 13 kernel miRNAs in the placebo group; hsa-miR-18b-5p and -193b-5p (p < 0.05) have significant differences between before and after trial. Illustrated p-values are based on pair-wise comparisons by the Mann–Whitney U-test; (C) Receiver operating characteristic (ROC) curves for hsa-miR-1182, -18a-5p, -18b-5p, -193b-5p, -23a-5p, -326, -378a-5p, -564 and -760 in the FZHY group; (D) ROC curves for hsa-miR-18b-5p and -193b-5p in the placebo group; (E) Hierarchical cluster and heat map of nine kernel miRNAs. The pathway analysis was performed using the miRPath program, and the significant pathways were determined when the p-value <0.001.

Figure 5

The KEGG terms of miRNA panel and the comparison of ROC between the miRNA panel and Child–Pugh. (A) ROC curves from the miRNA panel data and Child–Pugh data. ROC curve (p = miRNAs) generated using the miRNA expression data in the miRNA panel; the AUC was 0.824 (p = 0.000). The ROC curve of Child–Pugh was generated using the Child–Pugh score; the AUC was 0.732 (p = 0.000); (B) The miRNA combination of hsa-miR-18a-5p, -326, -1182 and -193b-5p associated with the KEGG pathway.

Figure 6

Overview of the framework. FZHY: Fuzheng-Huayu (Chinese herbal formula); MI: medical index; RT-PCR: reverse transcriptase polymerase chain reaction; ROC: receiver operating characteristic.