Unveiling the anticancer potential of flavonoids in hepatocellular carcinoma through microbiome and spatially resolved metabolomics analysis

Abstract

Background: Hepatocellular carcinoma (HCC) causes a large worldwide health burden, needing novel ways to prevention and treatment. Traditional Chinese medicine, which is rich in bioactive substances, has emerged as a viable approach to tackling HCC difficulties. Artemisia rupestris L. (AR), a perennial plant, has received interest for its immunoregulatory qualities and potential protection against viral influenza and hepatocellular cancer.

Methods: In this work, we looked at the pharmacological effects of Artemisia rupestris L. extract (ARE) on HCC mice. We used 16 S rRNA sequencing and computational biology approaches to investigate ARE-induced changes in bacterial composition inside HCC mouse tissues. Furthermore, we used liquid chromatography tandem mass spectrometry (UPLC-MS/MS) to identify metabolic changes caused by ARE.

Results: Our data indicate that ARE affects hepatocellular cancer via several pathways. AR offers a multi-faceted strategy to combating HCC by influencing critical metabolic pathways such α-linolenic acid and glycerophospholipid metabolism.

Conclusions: This research sheds new light on Artemisia rupestris L.'s anticancer potential, setting the platform for a more in-depth knowledge of its influence on hepatocellular carcinoma using a multi-omics approach.

Keywords: Flavonoids; Hepatocellular carcinoma; Microbiome.

Fig. 1

Effect of ARE on tumor volume and tumor weight. The data are expressed as the mean ± standard deviation (SD) (n = 10). A Tumor growth rate. B Tumor weight. *P <.5; **P <.01; ***P <.05

Fig. 2

Analyzing the microbial community composition present within HCC tumors. A Length distribution of clean reads obtained from 16s rDNA gene sequencing of fecal samples, represented as a boxplot. B Alpha diversity metrics including Chao, Shannon, and Simpson indices, indicating species richness and diversity among samples. C Relative abundance of dominating species in control and treatment groups. D Comparison of species-level abundance, highlighting underrepresentation of Alistipes and Odoribacter in treated mice. E Weighted UniFrac PCoA revealing clustering patterns between control and treatment groups. F LEfSe analysis identifying discriminative microbial taxa at various taxonomic levels (phylum, class, order, family, and genus), with emphasis on five species distinguishing between experimental groups (LDA > 2, p <.05). PCoA: principal coordinates analysis; OTU: Operational Taxonomic Unit

Fig. 3

Multivariate Analysis of Serum Metabolomic Profiles. A PCA plot displaying the separation of metabolites in serum samples between groups. B PLS-DA plot demonstrating distinct clustering of the control group and the ARE group. C Pie chart of identified metabolites. PCA: Principal Component Analysis; PLS-DA: Partial Least Squares Discriminant Analysis

Fig. 4

Differential metabolite analysis between the control group and the ARE group. Metabolites were considered significantly different if they exhibited a fold change ≥ 2 or ≤ 0.5, coupled with P <.05.. A The volcano plot visually represents the differential metabolites, with each point denoting a specific metabolite. This plot serves as a graphical depiction of the relationship between fold change and statistical significance for each metabolite, with those exhibiting up-regulation depicted in vibrant red and down-regulation in striking blue. B Differential metabolites were distinctly color-coded based on their assigned categories. C This bar plot illustrates the top 21 metabolites exhibiting significant alterations

Fig. 5

Metabolic interaction and pathway enrichment analysis. A Correlation heat map showing the relationships between substantially different metabolites. Each cell’s color represents the strength and direction of the association between metabolite pairs. B Chord diagrams depicting the co-regulatory interactions among different metabolites. The breadth and color of the chords indicate the intensity and type of the correlations, creating an understandable visual representation of the linkages. C A bar plot displaying major metabolic pathways discovered using KEGG enrichment analysis

Fig. 6

A mRNA expression of Sirt2 B mRNA expression of PGE2 C Expression of SIRT2 protein in HCC cells. The expression levels were measured by qPCR. The expression levels were normalized to GAPDH level. Data are shown as mean ± SD and were analyzed by ordinary one-way ANOVA. *P <.5; **P <.01; ***P <.05