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by claude@2026-07, 2026-07-03
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This study uses machine learning to predict metabolomic variation across approximately 1,000 cancer cell lines using matched multi-omics spanning genomics, epigenomics, transcriptomics/splicing, non-coding RNAs, proteomics, and phosphoproteomics, integrating these with curated interaction and metabolite-reaction databases. The authors find that the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs as top predictors; peripheral metabolites are predicted by corresponding enzyme levels, whereas central metabolites require combinatorial predictors involving signaling and redox pathways, which the authors note may not track pathway expression. They reconstruct multi-omic interaction subnetworks for highly predictable metabolites and identify YAP1 signaling as a top global predictor across multiple omic layers. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ∼1000 different cancer cell lines with matched omics data from 8 biomolecular classes: genomics (copy-number and mutations), epigenomics (histone post-translational modifications (PTMs) and DNA-methylation), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across 4 omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.
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Abstract
To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ∼1000 different cancer cell lines with matched omics data from 8 biomolecular classes: genomics (copy-number and mutations), epigenomics (histone post-translational modifications (PTMs) and DNA-methylation), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across 4 omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We have performed a new analysis across all eight omic regulatory layers in Recon8D and integrated this with known protein-protein interactions, metabolite-reaction, and miRNA-gene interactions from curated databases. This integrated analysis revealed cross-omic interactions and complementary regulatory nodes in the network. We have also incorporated two more omic layers including genomic mutation and lncRNAs, which revealed several well known and novel regulatory interactions. We have further included a novel confidence scoring system based on robustness of interactions across several control experiments. We have also described methods and limitations in greater detail and provided a detailed protocol in our github page.
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