ArticleJournal of translational medicine2025
Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and hotspots in research related to tumor immune escape: bibliometric analysis and future perspectives.Frontiers in immunology · 2025Pooled it
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Network pharmacology identifies repurposable drugs targeting host pathways across the oral-gut-lung axis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma.Journal of Zhejiang University. Science. B · 2026Article
- Diet-Microbiota-Immune Interactions in Hepatocellular Carcinoma: An Immunometabolic and Spatial Perspective.Nutrients · 2026Review
- Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms.Cancer cell international · 2026Article
- Advances in mitophagy research in hepatocellular carcinoma: mechanisms and therapeutic implications.Journal of gastrointestinal oncology · 2026Review
- Primary Liver Cancer Trends Worldwide and in China: Analysis of GLOBOCAN 2022 Data and Disease Management Implications.Portal hypertension & cirrhosis · 2026Review
- Exploring Matrix Stiffness-Related Gene in Periodontitis: A Comprehensive Multidataset Analysis.Mediators of inflammation · 2026Article
- Novel insights into triple-negative breast cancer heterogeneity, prognosis, and treatment response based on matrix stiffness: a combined single-Cell and transcriptome analysis.Frontiers in oncology · 2026Article
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10 authors.
Funding
Abstract
backgroundMatrix stiffness is strongly associated with hepatocarcinogenesis and significantly influences the properties of hepatocellular carcinoma (HCC). Investigating matrix stiffness-related signatures provides crucial insights into HCC prognosis and therapeutic response.
methodsMulti-omics data from liver hepatocellular carcinoma (LIHC) were integrated using 10 clustering algorithms, identifying three subgroups with distinct survival outcomes and treatment responses. A matrix stiffness-related signature comprising 57 genes was constructed by evaluating 101 machine learning algorithm combinations. PPARG, the key gene with the greatest contribution to the model, was selected for validation. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) analyses assessed matrix stiffness activity scores across different cell subgroups and examined PPARG spatial localization within tissues. Experimental studies and bioinformatics analyses further explored the role of PPARG in HCC carcinogenesis and the immune microenvironment.
resultsThe matrix stiffness-related signature demonstrated superior prognostic prediction performance in both training and validation cohorts compared to other existing HCC signatures. Distinct immune and mutation landscape characteristics were observed between patients categorized into high and low matrix stiffness groups. PPARG functioned in tumorigenesis through HSC activation and immune suppression. Furthermore, increased matrix stiffness was found to upregulate PPARG expression, promoting cell proliferation, activating lipid metabolism, and enhancing the stemness of HCC cells through the MAPK signaling pathway. Targeting PPARG with trametinib displayed an enhanced therapy response.
conclusionsThe matrix stiffness-related signature not only serves as a robust prognostic tool but also aids in identifying immune characteristics and optimizing therapeutic strategies, thus advancing personalized medicine for patients with HCC.
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