ArticleScientific reports2026
Integrated multi-omics analysis reveals comprehensive metabolism-driven tumor heterogeneity and immune microenvironment in hepatocellular carcinomas.
Article in Scientific reports, 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
7 authors.
Funding
Abstract
Metabolic reprogramming is a well-recognized hallmark of cancer, characterized by its remarkable flexibility in activating alternative pathways in the absence of specific regulators or substrates. Non-negative Matrix Factorization (NMF) was employed to identify tumor heterogeneity at the terms of metabolism, progression-related pathways and molecular subtypes of HCC based on gene set variation analysis (GSVA) and stratified survival analysis. The inherent heterogeneities of metabolism landscape which includes genomes, methelomic and transcriptomic data, proteomes, phosphoproteomes, mutational and immune microenvironment landscape between metabolic subtypes were performed based on multi-omics analyses. NMF has led three distinct survival-associated subtypes (NMF cluster 1, iHCC1; NMF cluster 2, iHCC2; NMF cluster 3, iHCC3) based on metabolic gene expression, and GSVA revealed remakeable metabolic differences. Additionally, multi-omics analysis further revealed the unique landscape of different metabolic subtypes, encompassing transcriptome, epigenetic and post-transcriptional modifications (PTMs) at both bulk and single cell seq-RNA level. Furthermore, 39 subtype-specific variables for identifying metabolic subtypes were screened using four feature selection algorithms and preliminarily validated on 8 machine learning models. We then built and verified a nomogram to guide the individualized strategy for HCC patients, utilizing a combination of metabolic signatures and clinical characteristics. Finally, we preliminarily identified the potential contribution of Aldolase B (ALDOB) in metabolic reprogramming triggered by epigenetic and PTMs. Overall, the research defined robust subtypes and further revealed potential targets linking metabolism with immune microenvironment and non-mutational epigenetic modifications, thereby advancing our understanding of metabolic heterogeneity for application in HCC diagnosis and clinical risk stratification.
Indexed as
Identifiers
What Socratic 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 Socratic graph.