Evidence map›Paper›PMID 41781663›Full record

ArticleScientific reports2026

Integrated multi-omics analysis reveals comprehensive metabolism-driven tumor heterogeneity and immune microenvironment in hepatocellular carcinomas.

Yu Li, Zihan Luo, Bingyu Zhang, Wenyao Zhu, Jinlian Zhang, Zizhao Li, Lingyu Zhang

Abstract read
In one paragraph

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.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yu Li *Department of Laboratory Medicine, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, 233004, People's Republic of China.
Zihan Luo *Department of Laboratory Medicine, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, 233004, People's Republic of China.
Bingyu Zhang *School of Public Health, China Medical University, Shenyang, Liaoning Province, 110112, People's Republic of China.
Wenyao ZhuDepartment of urology, the Central Hospital of Bengbu, Bengbu, Anhui Province, 233004, People's Republic of China.
Jinlian ZhangDepartment of pathology, the Second Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, 233004, People's Republic of China.
Zizhao LiZongshan School of Medicine, Sun Yat-sen University, Shenzhen, 518107, People's Republic of China.
Lingyu ZhangDepartment of Laboratory Medicine, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, 233004, People's Republic of China. lingyuzhang@zju.edu.cn.

Funding

Key Program of Anhui Provincial Department of Education 2022AH051438National Natural Science Foundation of China 82303218Natural Science Foundation of Anhui Province 2308085QH285
6 · The paper itself

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

Carcinoma, HepatocellularLiver NeoplasmsTumor MicroenvironmentGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMetabolic ReprogrammingMultiomicsTranscriptomeEpigenetic reprogrammingHepatocellular carcinomaMetabolic reprogrammingMulti-Omics integrationPost-translational modification

Identifiers

PMID41781663
PMCPMC13077083

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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.