The therapeutic role and potential mechanism of EGCG in obesity-related precocious puberty as determined by integrated metabolomics and network pharmacology

Abstract

Objective: (-)-Epigallocatechin-3-gallate (EGCG) has preventive effects on obesity-related precocious puberty, but its underlying mechanism remains unclear. The aim of this study was to integrate metabolomics and network pharmacology to reveal the mechanism of EGCG in the prevention of obesity-related precocious puberty.

Materials and methods: A high-performance liquid chromatography-electrospray ionization ion-trap tandem mass spectrometry (LC-ESI-MS/MS) was used to analyze the impact of EGCG on serum metabolomics and associated metabolic pathways in a randomized controlled trial. Twelve weeks of EGCG capsules were given to obese girls in this trail. Additionally, the targets and pathways of EGCG in preventing obesity-related precocious puberty network pharmacology were predicted using network pharmacology. Finally, the mechanism of EGCG prevention of obesity-related precocious puberty was elucidated through integrated metabolomics and network pharmacology.

Results: Serum metabolomics screened 234 endogenous differential metabolites, and network pharmacology identified a total of 153 common targets. These metabolites and targets mainly enrichment pathways involving endocrine-related pathways (estrogen signaling pathway, insulin resistance, and insulin secretion), and signal transduction (PI3K-Akt, MAPK, and Jak-STAT signaling pathways). The integrated metabolomics and network pharmacology indicated that AKT1, EGFR, ESR1, STAT3, IGF1, and MAPK1 may be key targets for EGCG in preventing obesity-related precocious puberty.

Conclusion: EGCG may contribute to preventing obesity-related precocious puberty through targets such as AKT1, EGFR, ESR1, STAT3, IGF1, and MAPK1 and multiple signaling pathways, including the estrogen, PI3K-Akt, MAPK, and Jak-STAT pathways. This study provided a theoretical foundation for future research.

Keywords: EGCG; metabolomics; network pharmacology; obesity; precocious puberty.

Figure 1

Multivariate statistical analysis of metabolite profiles in serum in obese girls OPLS-DA score plots of the EGCG group and Placebo group in positive mode (A) and negative mode (B). (C) Heatmap of top 30 endogenous differential metabolites between two groups, *P < 0.05, **P < 0.01, ***P < 0.001. (D) KEGG signaling pathway enrichment analysis (P < 0.05). (E) Categories of endogenous differential metabolites.

Figure 2

An analysis of potential therapeutic targets based on a Venn diagram and a PPI network (A) Venn diagram of the common targets of EGCG, obesity and precocious puberty. (B) Potential therapeutic targets in the PPI network. (C) The bar plot of the protein–protein interaction network.

Figure 3

Gene Ontology (GO) enrichment analysis (A) GO enrichment analysis. The top 10 evidently enriched terms in each category. BP, biological process; CC, cellular component; MF, molecular function. (B) The top 10 enriched BP terms.

Figure 4

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway results (A) Statistical chart of the B-level classification of each pathway. (B) The top 20 significant pathways. Bubble size from large to small indicates the count of potential targets enriched in the pathway in descending order. Bubble color from red to green indicates the -log (p value) in descending order. (C) The top 20 significantly enriched differential pathway circle maps.

Figure 5

Potential therapeutic targets based on evidently enriched pathways The green nodes represent potential therapeutic targets of EGCG obesity and precocious puberty in PI3K-AKT pathway (A) and estrogen pathway (B). (C) EGCG-target-pathway multiple interactive network.

Figure 6

Molecular docking analysis (A) EGCG with AKT1. (B) EGCG with EGFR. (C) EGCG with ESR1. (D) EGCG with IGF1. (E) EGCG with MAPK1. (F) EGCG with STAT3.