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.
Data availability
The data sets (TCGA, GEO, and ICGC) generated and/or analyzed during the current study period can be found in the MATERIALS AND METHODS section of the original manuscript file. The microarray data of GSE14520 were downloaded from the GEO database. Proteome and phosphoproteome data of 159 HCC patients were approved and provided by the researchers in supplementary data. The DNA methylation expression profile was derived from TCGA-LIHC. We have described in detail the processes including data set selection, filtering and/or standardization in materials and methods. Any further data or information are available from the corresponding author upon reasonable request.
Abbreviations
- HCC:
-
Hepatocellular carcinoma
- NMF:
-
Non-negative matrix factorization
- GSVA:
-
Gene set variation analysis
- NAFLD:
-
Nonalcoholic fatty liver disease
- T2DM:
-
Type 2 diabetes mellitus
- MsigDB:
-
Molecular signatures database
- TCGA:
-
The cancer genome atlas
- ICGC:
-
The international cancer genome consortium
- TPM:
-
Transcripts per kilobase million
- FDR:
-
False discovery rate
- UMAP:
-
Uniform manifold approximation and projection
- PCA:
-
Principal component analysis
- ML:
-
Machine learning
- DT:
-
Decision tree
- RF:
-
Random Forest
- SVM:
-
Support vector machine
- KNN:
-
k-nearest neighbor
- LDA:
-
Linear discriminant analysis
- AdaBoost:
-
Adaptive boosting
- XgBoost:
-
Extreme gradient boosting
- AFP:
-
Alpha-fetoprotein
- GGT:
-
γ-Glutamyl transferase
- FCM:
-
Fuzzy C-means clustering
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Acknowledgments
We would like to thank all participants for their understanding and support of our research.
Funding
This work has been supported by grants from the National Natural Science Foundation of China (No. 82303218), the Natural Science Foundation of Anhui Province (No.2308085QH285), the Key Program of Anhui Provincial Department of Education (No.2022AH051438) and the Natural Science Key Project of Bengbu Medical College (No.2021BYZD238).
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All authors read and approved the final version of the manuscript. L.Y.Z. and Y.L. conceived and designed the study. Y.L., B.Y.Z., and J.L.Z. developed and implemented ML pipeline under the supervision of L.Y.Z. and J.L.Z. L.Y.Z., J.L.Z., and W.X.Z. developed the methodology for multi-omics data generation and phenotypic data. L.Y.Z. and Y.L. performed formal analysis and investigation on omics data. L.Y.Z., Y.L., B.Y.Z., W.Y.Z. and Z.Z.L, were under the responsibility of the production of Figures and Tables, and manuscript writing. Z.H.L performed additional dataset retrieval and analysis in the revised manuscript. Z.H.L performed additional dataset retrieval and analysis in the revised manuscript.
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Ethics approval for the study was granted by the clinical trial ethics committee (No. 2023023) at the First Affiliated Hospital of Bengbu Medical University, and all patients provided written informed consent. Ethic approval was exempted for public resource by the First Affiliated Hospital of Bengbu Medical University according to the description of national legislation guidelines, such as item 1 and 2 of Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects dated February 18, 2023, China, because the patients involved in the database have obtained ethical approval.
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Li, Y., Luo, Z., Zhang, B. et al. Integrated multi-omics analysis reveals comprehensive metabolism-driven tumor heterogeneity and immune microenvironment in hepatocellular carcinomas. Sci Rep (2026). https://doi.org/10.1038/s41598-026-42856-7
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DOI: https://doi.org/10.1038/s41598-026-42856-7
