TY - JOUR
T1 - Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer
AU - Blum, Benjamin C.
AU - Lin, Weiwei
AU - Lawton, Matthew L.
AU - Liu, Qian
AU - Kwan, Julian
AU - Turcinovic, Isabella
AU - Hekman, Ryan
AU - Hu, Pingzhao
AU - Emili, Andrew
N1 - Publisher Copyright:
© 2021 THE AUTHORS.
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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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U2 - 10.1016/j.mcpro.2021.100189
DO - 10.1016/j.mcpro.2021.100189
M3 - Article
C2 - 34933084
AN - SCOPUS:85123814267
SN - 1535-9476
VL - 21
JO - Molecular and Cellular Proteomics
JF - Molecular and Cellular Proteomics
IS - 1
M1 - 100189
ER -