# Network Pharmacology and Transcriptome Analysis Reveal Potential Cardiometabolic Targets of Polygonum cuspidatum

## TL;DR

This study explores how Polygonum cuspidatum may help with heart and metabolic issues by combining chemical and genetic data.

## Contribution

The study integrates network pharmacology with transcriptomic data to reveal new cardiometabolic targets of Polygonum cuspidatum.

## Key findings

- Resveratrol was identified as a key compound with 214 predicted targets in cardiometabolic pathways.
- Key targets like MAPK14 and VEGFA are linked to inflammation and vascular injury.
- Transcriptomic data confirmed connections to insulin resistance and atherosclerosis.

## Abstract

Objectives: Polygonum cuspidatum Sieb. et Zucc (PC) has traditionally been used for inflammatory and circulatory disorders; however, the systems-level mechanisms of its effect on cardiometabolic disease processes, including insulin resistance and vascular injury, remain incompletely understood. This study aimed to identify biological pathways potentially modulated by PC through the integration of network pharmacology with patient-derived transcriptomic data. Methods: Four representative compounds—resveratrol, polydatin, emodin, and physcion—were selected based on previously reported chemical fingerprints that characterize PC. Predicted targets were obtained from public compound–target databases and used to construct a compound–target network. Functional enrichment was performed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Genetic Association Database (GAD) disease associations. To evaluate clinical relevance, predicted targets were compared with differentially expressed genes (DEGs) from insulin-resistant adipose tissue (GSE20950) and atherosclerotic lesions (GSE43292). Results: A total of 329 predicted target genes were identified, with resveratrol emerging as the dominant topological hub (214 targets). Network and enrichment analyses highlighted MAPK14, MAPT, VEGFA, IL1B, NLRP3, and HMOX1 as key targets involved in inflammatory, oxidative, and vascular injury pathways that overlapped with transcriptomic signatures. KEGG analysis demonstrated significant enrichment in AGE–RAGE signaling, TNF-mediated inflammation, and lipid–atherosclerosis pathways, while GAD mapping indicated associations with type 2 diabetes and atherosclerosis. Integration of transcriptomic datasets further supported a convergence on coordinated inflammatory and oxidative processes driving vascular remodeling. Conclusions: These findings suggest that the major constituents of PC may modulate interconnected cardiometabolic processes linking insulin resistance and vascular injury implicated in atherosclerotic cardiovascular disease. By integrating network pharmacology with patient-derived transcriptomic evidence, this study provides a systems-level framework for interpreting the potential biological roles of PC in insulin resistance and vascular injury.