A systems biology-based investigation into the therapeutic effects of Gansui Banxia Tang on reversing the imbalanced network of hepatocellular carcinoma

Abstract

Several complex molecular events are involved in tumorigenesis of hepatocellular carcinoma (HCC). The interactions of these molecules may constitute the HCC imbalanced network. Gansui Banxia Tang (GSBXT), as a classic Chinese herbal formula, is a popular complementary and alternative medicine modality for treating HCC. In order to investigate the therapeutic effects and the pharmacological mechanisms of GSBXT on reversing HCC imbalanced network, we in the current study developed a comprehensive systems approach of integrating disease-specific and drug-specific networks, and successfully revealed the relationships of the ingredients in GSBXT with their putative targets, and with HCC significant molecules and HCC related pathway systems for the first time. Meanwhile, further experimental validation also demonstrated the preventive effects of GSBXT on tumor growth in mice and its regulatory effects on potential targets.

Figure 1. A schematic diagram of this systems biology-based investigation into the pharmacological mechanisms of GSBXT acting on Hepatocellular carcinoma (HCC) by integrating multitarget identification and network analysis.

Figure 2. Interaction network between chemical components of GSBXT and their putative targets built and visualized with Navigator.

(A) Multi-level network of Ingredient-chemical component-putative target; (B) Interaction network of Ingredient-putative target extracted from (A), in order to understand relationships among drug ingredients in GSBXT. Edges: interactions between chemical components of GSBXT and their putative targets; Pink square nodes: five ingredients in GSBXT, including Radix Kansui, Rhizoma Pinelliae, Radix Glycytthizae, Raidix Paeoniae Alba and Mel; Blue triangular nodes: chemical components of GSBXT; Yellow round nodes: putative targets for chemical components of GSBXT. Yellow round nodes with red rings: putative targets shared by various ingredients in GSBXT.

Figure 3

(A) Protein-protein interaction (PPI) network between putative targets of GSBXT and their interactive partners built and visualized with Navigator. Edges: PPIs between putative targets of GSBXT and their interactive partners; Yellow round nodes: putative targets of GSBXT; Purple.round nodes: interactive partners of putative targets of GSBXT. (B) Multi-level network of drug ingredients → major putative targets → candidate HCC targets built and visualized with Navigator. Pink square nodes: five ingredients in GSBXT: Radix Kansui, Rhizoma Pinelliae, Radix Glycytthizae, Raidix Paeoniae Alba and Mel; Yellow round nodes: major putative targets of GSBXT; Yellow round nodes with purple ring: major putative targets which had more than 10 direct interactions with candidate HCC targets; Green triangular nodes: candidate HCC targets.

Figure 4. GSBXT inhibits tumor growth in vivo.

(A) Images of dissected tumors in control, low dose, middle dose and high dose groups. (B) Tumor sizes of xenografts in control, low dose, middle dose and high dose groups. (C) Tumor weight of xenografts in control, low dose, middle dose and high dose groups. Data are represented as the mean ± S.D. ‘*’ and ‘**’, P < 0.05 and P < 0.01, respectively, comparison with the control group.

Figure 5. GSBXT administration suppresses Hsp90α (A), ATP1A1 (B) and STAT3 (C) protein expression in tumor tissues of murine xenograft models examined by immunohistochemistry (A ~ C) and western blot (D).

Data are represented as the mean ± S.D. ‘*’, ‘**’ and ‘***’, P < 0.05, P < 0.01 and P < 0.001, respectively, comparison with the control group.