Progression from excessive to deficient syndromes in chronic hepatitis B: A dynamical network analysis of miRNA array data

15Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Traditional Chinese medicine (TCM) treatment is regarded as a safe and effective method for chronic hepatitis B (CHB), which requires a traditional diagnosis method to distinguish the TCM syndrome. In this study, we study the differences and similarities among excessive, excessive-deficient, and deficient syndromes, by an integrative and comparative analysis of weighted miRNA expression or miRNA-target network in CHB patients. We first calculated the differential expressed miRNAs based on random module t-test and classified three CHB TCM syndromes using SVM method. Then, miRNA target genes were obtained by validated database and predicted programs subsequently, the weighted miRNA-target networks were constructed for different TCM syndromes. Furthermore, prioritize target genes of networks of CHB TCM syndromes progression analyzed using DAVID online analysis. The results have shown that the difference between TCM syndromes is distinctly based on hierarchical cluster and network structure. GO and pathway analysis implicated that three CHB syndromes more likely have different molecular mechanisms, while the excessive-deficient and deficient syndromes are more dangerous than excessive syndrome in the process of tumorigenesis. This study suggested that miRNAs are important mediators for TCM syndromes classification as well as CHB development progression and therefore could be potential diagnosis and therapeutic molecular markers. © 2013 Qi-Long Chen et al.

Cite

CITATION STYLE

APA

Chen, Q. L., Lu, Y. Y., Zhang, G. B., Song, Y. N., Zhou, Q. M., Zhang, H., … Su, S. B. (2013). Progression from excessive to deficient syndromes in chronic hepatitis B: A dynamical network analysis of miRNA array data. Evidence-Based Complementary and Alternative Medicine, 2013. https://doi.org/10.1155/2013/945245

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free