ArticleMolecular oncology2022
Integrating bulk and single-cell RNA sequencing reveals cellular heterogeneity and immune infiltration in hepatocellular carcinoma.
Article in Molecular oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
- Single-cell transcriptomics and machine-learning reveal M1 macrophage-driven progression from minimal change disease to focal segmental glomerulosclerosis.Renal failure · 2026Article
- Noninvasive Multi-Omics Radiomic Model Integrating scRNA-seq and Bulk RNA-seq for Hepatocellular Carcinoma Prognosis.Journal of imaging informatics in medicine · 2026Article
- Integrated multi-omics analysis reveals comprehensive metabolism-driven tumor heterogeneity and immune microenvironment in hepatocellular carcinomas.Scientific reports · 2026Article
- Multi-omics Analysis Revealed the Diagnostic and Therapeutic Value of Immunogenic Cell Death-derived SCN5A in Hepatocellular Carcinoma.Molecular biotechnology · 2026Article
- Subtype-dependent PD-L1 stability and immune context shape immunotherapy response in hepatocellular carcinoma.Frontiers in immunology · 2026Article
- Prognostic model for predicting recurrence-free survival in hepatocellular carcinoma using integrated analysis of single-cell RNA-sequencing and bulk RNA-sequencing.Oncology letters · 2025Article
- Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer.Discover oncology · 2025Article
- Metabolism archetype cancer cells induce protumor TREM2Nature communications · 2025Article
- Integrating single-cell and spatial transcriptomics reveals the cellular heterogeneity of vestibular schwannoma.NPJ precision oncology · 2025Article
- Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation.Journal of translational medicine · 2025Article
- Identification of a novel FOXO3‑associated prognostic model in hepatocellular carcinoma.Oncology letters · 2025Article
- Characteristics of the Dynamic Evolutionary Pathway of ADSCs Induced Differentiation into Astrocytes Based on scRNA-Seq Analysis.Molecular neurobiology · 2025Article
- Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology.International journal of molecular sciences · 2025Review
- Article
- Investigation of Exome-Wide Tumor Heterogeneity on Colorectal Tissue-Based Single Cells.International journal of molecular sciences · 2025Article
- Analysis and Validation of Tyrosine Metabolism-related Prognostic Features for Liver Hepatocellular Carcinoma Therapy.Current medicinal chemistry · 2025Article
- Machine learning developed an immune evasion signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma.Frontiers in cell and developmental biology · 2025Article
- Article
- Pseudouridylation-Related Genes Predict Prognosis and Therapeutic Response in Hepatocellular Carcinoma Patients.Journal of Cancer · 2025Article
- Article
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11 authors.
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Abstract
Efficacy of immunotherapy in hepatocellular carcinoma (HCC) is blocked by its high degree of inter- and intra-tumor heterogeneity and immunosuppressive tumor microenvironment. However, the correlation between tumor heterogeneity and immunosuppressive microenvironment in HCC has not been well addressed. Here, we endeavored to dissect inter- and intra-tumor heterogeneity in HCC and uncover how they contribute to the immunosuppressive microenvironment. We performed consensus molecular subtyping with non-negative matrix factorization (NMF) clustering to stratify the inter-heterogeneity profile of HCC tumors. We grouped HCC tumors from the Cancer Genome Atlas (TCGA) patients into three subtypes (S1, S2 and S3), where S1 was characterized as a 'hot tumor' profile with high expression level of T cell genes and rate of immune scores. S2 was characterized as a 'cold tumor' profile with the highest tumor purity score, and S3 as an 'immunosuppressed tumor' profile with the poorest prognosis and a high expression level of immunosuppressive genes such as cytotoxic T-lymphocyte-associated protein-4, TIGIT, and PDCD1. Moreover, we combined weighted gene co-expression network analysis and single-cell regulatory network inference and clustering (SCENIC) in the single-cell dataset of the S3-like subtype (CS3) and identified a transcription factor, BATF, which could upregulate immunosuppressive genes. Finally, we identified a cell interaction network in which a myeloid-derived suppressor cell-like macrophage subtype could promote the formation of immunosuppressive T-cells.
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