Predicting the molecular mechanism of ginger targeting PRMT1/BTG2 axis to inhibit gastric cancer based on WGCNA and machine algorithms.

Objective: The aim of this study was to screen the biomarkers of ginger against gastric cancer (GC) by network pharmacology, WGCNA and machine algorithms. To find the upstream transcription factors and downstream signaling proteins constituting the signaling axis, so as to predict the possible mechanism of action of ginger against GC.

Methods: Ginger was screened for active ingredients and targets through public databases. GC genes were screened using disease database, GEO database and WGCNA. The intersection of the four was taken to obtain the potential core genes. Machine algorithms was used to screen the core genes. Clinical relevance analysis, gene mutation relationship, epigenetic regulation analysis, immune infiltration analysis and molecular docking validation were performed on the core genes. Find its upstream transcription factors and downstream signaling proteins through database.

Results: 35 intersecting genes were obtained by databases and WGCNA analysis. Machine algorithms and PPI were combined to finally screen the core gene PRMT1. The upstream transcription factor of PRMT1 was identified as EGR1 and the downstream protein as BTG2 by database and molecular docking.

Conclusion: In this study, we found that PRMT1 could be used as a biomarker for ginger against GC using network pharmacology, WGCNA and machine algorithms. We hypothesized that ginger may exert antitumor effects through PRMT1/BTG2, providing new insights into the pharmacological mechanism of ginger against GC.