J Am Soc Mass Spectrom. 2025 Aug 6;36(8):1598-1608. doi: 10.1021/jasms.4c00505. Epub 2025 Jul 2.

ABSTRACT

Gastric signet ring cell carcinoma (GSRC) is an aggressive subtype of gastric cancer (GC) with a poor prognosis. The lack of a systematic molecular and metabolic heterogeneity overview has led to slow progress in clinical practice. This study used mass spectrometry imaging (MSI) to investigate the metabolic landscape of GSRC in GC tissue with various differentiation grades. Our comprehensive spatial profiling of metabolites and lipids unveiled distinct metabolic signatures across different tissue subregions. A substantial number of lipidomic biomarkers associated with GSRC were identified, including phosphatidylethanolamine N-methyl (PE-NMe), phosphatidylethanolamine (PE), sphingomyelin (SM), diacylglycerol (DG), phosphatidic acid (PA), and phosphatidylcholine (PC), which may provide insights into its pathogenesis and potential therapeutic targets. Furthermore, multi-omics network analysis revealed intricate metabolic pathways involved in GSRC progression. Our findings highlight the importance of understanding the metabolic heterogeneity of GSRC and pave the way for future studies exploring its clinical implications and therapeutic strategies.

PMID:40600435 | DOI:10.1021/jasms.4c00505

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