Ann Med. 2025 Dec;57(1):2494671. doi: 10.1080/07853890.2025.2494671. Epub 2025 May 3.

ABSTRACT

BACKGROUND: The combination of traditional Chinese medicine (TCM) with chemotherapy has been widely applied in the treatment of gastric cancer (GC). However, previous clinical studies have been constrained by small sample sizes and a lack of investigation into the molecular mechanisms of TCM. This study aims to assess the efficacy of TCM in treating GC by leveraging the strengths of meta-analysis and multi-omics approaches while also summarizing the underlying pharmacological mechanisms.

METHODS: A systematic literature review and meta-analysis were conducted using online databases to collect data before May 2024. This was to investigate the association between TCM combined with chemotherapy and the prognosis in GC. The molecular targets between the high-frequency TCMs and GC were identified through network pharmacology. The underlying mechanisms were investigated using multi-omics.

RESULTS: 9 studies with 2,158 patients were included. The meta-analysis results demonstrated that the combination of TCM and chemotherapy significantly improved the overall survival (OS) of GC patients (OR = 2.91; 95% CI: 2.70-3.12, p < 0.00001) and enhanced their quality of life (OR = 4.00; 95% CI: 1.99-8.03, p < 0.0001). Network pharmacology analysis identified 13 potential molecular targets of TCM in GC; additionally, multi-omics analysis highlighted the significant roles of MK, MIF, GALECTIN, and CypA signaling pathways in GC.

CONCLUSION: The combination of TCM with chemotherapy significantly improves the prognosis of GC; future research can focus on these key molecular targets and signaling pathways. This supports the application of precision medicine in cancer treatment and suggests the rational use of TCM in managing GC.

PMID:40317214 | PMC:PMC12051567 | DOI:10.1080/07853890.2025.2494671

Bioinformatics. 2024 Nov 1;40(11):btae623. doi: 10.1093/bioinformatics/btae623.

ABSTRACT

MOTIVATION: Enzymatic reaction play a pivotal role in regulating cellular processes with a high degree of specificity to biological functions. When enzymatic reactions are disrupted by gene, protein, or metabolite dysfunctions in diseases, it becomes crucial to visualize the resulting perturbed enzymatic reaction-induced multi-omics network. Multi-omics network visualization aids in gaining a comprehensive understanding of the functionality and regulatory mechanisms within biological systems.

RESULTS: In this study, we designed PhenoMultiOmics, an enzymatic reaction-based multi-omics web server designed to explore the scope of the multi-omics network across various cancer types. We first curated the PhenoMultiOmics database, which enables the retrieval of cancer-gene-protein-metabolite relationships based on the enzymatic reactions. We then developed the MultiOmics network visualization module to depict the interplay between genes, proteins, and metabolites in response to specific cancer-related enzymatic reactions. The biomarker discovery module facilitates functional analysis through differential omic feature expression and pathway enrichment analysis. PhenoMultiOmics has been applied to analyze the transcriptomics data of gastric cancer and the metabolomics data of lung cancer, providing mechanistic insights into interrupted enzymatic reactions and the associated multi-omics network.

AVAILABILITY AND IMPLEMENTATION: PhenoMultiOmics is freely accessed at https://phenomultiomics.shinyapps.io/cancer/ with a user-friendly and interactive web interface.

PMID:39418180 | PMC:PMC11549024 | DOI:10.1093/bioinformatics/btae623