ArticleMolecular & cellular proteomics : MCP2022
Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer.
Article in Molecular & cellular proteomics : MCP, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Integrative multi-omics analysis reveals lipid/metabolite dysregulation and temporal decoupling in disease progression.Scientific reports · 2026Article
- Multiomic profiling of chronically activated CD4+ T cells identifies drivers of exhaustion and metabolic reprogramming.PLoS biology · 2024Article
- Multi-Omics Profiling of Hypertrophic Cardiomyopathy Reveals Altered Mechanisms in Mitochondrial Dynamics and Excitation-Contraction Coupling.International journal of molecular sciences · 2023Article
- The Knowns and Unknowns in Protein-Metabolite Interactions.International journal of molecular sciences · 2023Review
- Integration of Metabolomic and Proteomic Data to Uncover Actionable Metabolic Pathways.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Multi-omic insights into the cellular response ofFrontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.
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Registered trials
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