Integrative analysis of epigenomics, transcriptomics, and proteomics to identify key targets and pathways of Weining granule for gastric cancer.

Abstract Ethnopharmacological relevance Weining granule (WNG) is a “Qi-Enriching and Kidney-Tonifying, Spleen-Reinforcing and Stasis-Removing” formula for gastric cancer (GC). Past research we noted WNG inhibited cell growth and raised apoptosis in GC. However, the underlying mechanism of WNG for GC have yet to be systematically clarified. Aim of the study We sought to characterize the molecular landscape of GC cells in vitro after WNG treated, to identify the molecular targets and pathways that were associated with WNG for inducing the apoptosis of GC cells, and further to clarify underlying molecular mechanism of WNG for GC. Materials and methods We performed the techniques of RNA sequencing, tandem mass tags (TMT) based quantitative proteomics, and reduced representation bisulfite sequencing (RRBS) in WNG-treated/or untreated SGC-7901 GC cells to gain a comprehensive molecular portrait of WNG treatment. Then we integrated methylomics, transcriptomics, and proteomics data to carry out the bioinformatics analysis, and constructed the protein-protein interaction (PPI) network to identify molecular targets, and to discover the underlying signaling pathways associated with WNG for GC by network analysis. Besides, we verified the candidate target genes by Kaplan–Meier plotter database. Results We identified 1249 significant differentially expressed genes (DEGs) from RNA expression datasets, 191 significant differentially abunabundant proteins (DAPs) from proteomics datasets, and 8293 significant differentially methylated regions (DMRs) from DNA methylation datasets. GO and KEGG analysis showed DEGs, DAPs, and DMRs enriched in the cancer-related biological processes of calcium signaling pathway, pathways in cancer, metabolic pathways, MAPK signaling pathway, PI3K-Akt signaling pathway, and transcriptional misregulation in cancer. We integrated three profile datasets and performed network analysis to distinguish the hub genes, and finally the genes of SOD2, HMOX1, MMP1, SRXN1, NOTCH1, MAPK14, TXNIP, VEGFA, POLR2F, and HSPA9 were identified. The Kaplan–Meier plotter confirmed that SOD2, MMP1, SRXN1, NOTCH1, MAPK14, TXNIP, VEGFA, and HSPA9 were significantly correlated with OS in GC patients (P   0.01). Conclusions SOD2, MMP1, SRXN1, NOTCH1, MAPK14, TXNIP, VEGFA, and HSPA9 were the predictive pharmaceutical targets of WNG for GC. The anticancer function of WNG was significantly associated with the pathways of focal adhesion pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, and Wnt signaling pathway.