ArticleMolecular systems biology2026
Spatially-guided metabolomics profiling of metabolic regions in human tumor tissues.
Article in Molecular systems biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Bulk high-resolution mass spectrometry provides sensitive and global snapshots of metabolites involved in cancer metabolism. However, intratumoral heterogeneity obscures the cellular origins of detected metabolites, making it difficult to identify reproducible and predictive metabolic markers. Here, we present "Spatially guided MEtabolomics (SgME) profiling", a multi-modal metabolomics data analysis approach that delineates and maps metabolic regions (MERs), including overlapping regions, within tumor tissues. We applied SgME profiling to human hepatocellular carcinoma (HCC) tumors and refined potential RNA markers that were also found in previous transcriptomics or bioinformatics studies to those specifically associated with malignant regions. We further estimated that more than 50% of the highly abundant metabolites detected in bulk tumors originated from the non-malignant MERs and are therefore unlikely to be predictive and/or reproducible markers. Importantly, SgME profiling also revealed new potential metabolic markers that were not apparent in bulk analysis because they increased in low-grade tumor regions but declined sharply in necrotic regions. Together, these findings show that SgME profiling overcomes key limitations of conventional metabolomics profiling by enabling more granular, spatially resolved metabolic characterization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.