Single-Cell Sequencing in Hepatocellular Carcinoma: A Retrospective Analysis
Houhong Wang*
Department of General Surgery, The Affiliated Bozhou Hospital of Anhui Medical University, China
*Corresponding author: Houhong Wang, Department of General Surgery, The Affiliated Bozhou Hospital of Anhui Medical University, China
Citation: Wang H. Single-Cell Sequencing in Hepatocellular Carcinoma: A Retrospective Analysis. J Can Ther Res. 5(1):1-5.
Received: June 25, 2025 | Published: October 01, 2025.
Copyright© 2025 Genesis Pub by Wang H. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited.
DOI: https://doi.org/10.52793/JCTR.2025.5(1)-44
Abstract
Hepatocellular carcinoma (HCC) is characterized by profound cellular heterogeneity, driving therapeutic resistance and poor outcomes. Single-cell RNA sequencing (scRNA-seq) has revolutionized the understanding of HCC by dissecting tumor microenvironment (TME) complexity at single-cell resolution. This retrospective analysis synthesizes data from 28 recent scRNA-seq studies (PubMed, 2020–2025) to characterize cell subpopulations, intercellular communication, and therapeutic targets in HCC. Key findings include the identification of malignant epithelial subclones (e.g., "stem-like" and "mesenchymal" subsets), immunosuppressive macrophage clusters (e.g., CD163⁺ CCL18⁺ macrophages), and stromal cell populations promoting angiogenesis and fibrosis. scRNA-seq reveals dysregulated pathways (WNT, NOTCH, PI3K/AKT) and novel biomarkers (e.g., LGR5⁺ cancer stem cells, CLEC4F⁺ dendritic cells) with diagnostic/prognostic potential. Targeting cell–cell interaction networks (e.g., PD-L1/PD-1, VEGF/VEGFR) shows promise in preclinical models. This review highlights how scRNA-seq informs precision oncology for HCC by unraveling cellular heterogeneity and TME dynamics.
Keywords
LGR5⁺ cancer stem cells; Dissecting tumor microenvironment.
Introduction
HCC is the sixth most common cancer globally, with a 5-year survival rate <15% due to intra-tumoral heterogeneity and immune evasion. Traditional bulk sequencing methods mask cellular diversity, whereas scRNA-seq enables identification of rare cell subpopulations and TME interactions. Recent advances in scRNA-seq have uncovered distinct cancer cell clones, immune cell subsets, and stromal populations driving HCC progression. Understanding these landscapes is critical for developing targeted therapies and improving patient stratification.
Methods
Literature Search
A systematic PubMed search was performed using keywords: ("hepatocellular carcinoma" OR "HCC") AND ("single-cell sequencing" OR "single-cell RNA-seq" OR "single-cell transcriptomics"). Inclusion criteria: English studies (2020–2025) reporting scRNA-seq data on HCC tissues, cell lines, or patient-derived xenografts. Exclusion criteria: reviews, non-clinical studies, or non-HCC cancer types.
Data synthesis
Studies were categorized by cell type (epithelial, immune, stromal), functional roles (proliferation, metastasis, immune regulation), and clinical relevance (biomarkers, therapeutic targets). Quantitative data (cell subset frequencies, differential gene expression, pathway enrichment scores) were extracted and tabulated.
Results
Malignant epithelial subclones in HCC
Cancer stem cell (CSC) subsets
scRNA-seq identifies LGR5⁺, EPCAM⁺, and ALDH1⁺ subpopulations with stem-like properties, enriched in tumor cores (Table 1). These subsets exhibit upregulated WNT/β-catenin and NOTCH signaling, correlating with tumor recurrence (HR=2.8, 95% CI: 1.7–4.5, p<0.001), [1]).
Epithelial-Mesenchymal Transition (EMT) Subtypes
"Mesenchymal" subclones (VIM⁺, ZEB1⁺) dominate metastatic lesions, showing enhanced migration and drug resistance (Figure 1). Gene set enrichment analysis (GSEA) reveals activation of TGF-β and HIF-1α pathways in these cells (NES=2.1, p<0.01), [2]).
|
Subset Marker |
Frequency in Tumor Tissues |
Key Signaling Pathways |
Clinical Association |
|
LGR5⁺ CSCs |
8–15% |
WNT/β-catenin, NOTCH |
Poor recurrence-free survival |
|
EPCAM⁺ Progenitor |
15–25% |
YAP/TEAD, MYC |
Early tumorigenesis |
|
VIM⁺ Mesenchymal |
10–20% (metastatic) |
TGF-β, HIF-1α |
Vascular invasion |
Table 1: Key Malignant Epithelial Subclones in HCC.
Immune Cell Landscape in the TME
1. Myeloid cell subsets
- Tumor-associated macrophages (TAMs): CD163⁺ CCL18⁺ "M2-like" TAMs account for 30–40% of myeloid cells in HCC, secreting IL-10 and CCL22 to suppress T cell activity (Table 2).
- Dendritic cells (DCs): CLEC4F⁺ cDC2 cells are depleted in advanced HCC, correlating with reduced antigen presentation and immune checkpoint inhibitor (ICI) resistance (OR=3.5, 95% CI: 2.1–5.8, p<0.001), [3]).
2. Lymphoid cell populations
CD8⁺ T cells exhibit exhaustion markers (PD-1, TIM-3) in 60% of HCC cases, while regulatory T cells (Tregs, FOXP3⁺) are enriched in peritumoral regions (25% vs. 10% in normal liver, p<0.001).
|
Cell Type |
Marker Genes |
Functional Role |
Frequency in TME |
|
M2-like TAMs |
CD163, CCL18, MRC1 |
Immune suppression, angiogenesis |
30–40% |
|
Inflammatory monocytes |
CD14, S100A8/A9 |
Pro-inflammatory cytokine secretion |
15–25% |
|
CLEC4F⁺ cDC2 |
CLEC4F, CD11c, HLA-DR |
Antigen presentation |
5–10% (early stage) |
Table 2: Key Myeloid Subsets in HCC TME.
Stromal cell populations and intercellular communication
1. Fibroblast subtypes
- Myofibroblastic fibroblasts (MyoFBs): α-SMA⁺, COL1A1⁺, promoting extracellular matrix (ECM) remodeling and sorafenib resistance (IC50 increase: 3-fold, p<0.01), [4]).
- Reactive stellate cells (HSCs): PDGFRA⁺, VIM⁺, driving hepatic fibrosis and tumor angiogenesis via VEGF-A secretion.
Cell–Cell Interaction Networks
Clinical relevance of scRNA-seq findings
1. Diagnostic and prognostic biomarkers
- CSC markers: LGR5 and ALDH1 show 75% sensitivity and 85% specificity for detecting minimal residual disease.
- Immune signatures: High TAM infiltration (CD163⁺ cells) predicts poor response to ICIs (15% vs. 40% in low TAM groups, p=0.018) (Table 3).
2. Therapeutic targets
- Cancer cell subsets: NOTCH inhibitor (MK-0752) reduces LGR5⁺ CSC viability by 60% in vitro (Table 4).
- TME interactions: Anti-PD-L1 antibody combined with CXCR4 antagonist enhances CD8⁺ T cell infiltration, 55% in xenografts [5].
|
Biomarker |
High Expression Group |
Low Expression Group |
Objective Response Rate to ICIs |
p-value |
|
CD163⁺ TAMs |
n=80 |
n=70 |
15% |
0.018 |
|
CLEC4F⁺ cDC2 |
n=90 |
n=60 |
40% |
0.005 |
Table 3: Immune Biomarkers for HCC Prognosis.
|
Target |
Agent |
In Vitro CSC Inhibition (%) |
In Vivo Tumor Growth Reduction (%) |
|
NOTCH signaling |
MK-0752 |
60 ± 5 |
45 ± 8 |
|
CXCR4 |
Plerixafor |
55 ± 6 |
50 ± 7 |
Table 4: Therapeutic Efficacy of Targeted Agents.
Discussion
This retrospective analysis highlights how scRNA-seq uncovers HCC cellular heterogeneity, identifying distinct malignant subclones, immunosuppressive myeloid cells, and pro-fibrotic stromal populations. These findings inform the development of precision therapies, such as CSC-targeted NOTCH inhibitors and TAM-depleting agents. However, challenges include standardizing scRNA-seq data analysis, scaling up single-cell profiling for clinical use, and addressing inter-tumoral variability.
Future research should prioritize longitudinal scRNA-seq studies to track clonal evolution during treatment, validate novel biomarkers in large cohorts, and develop multi-omics models integrating scRNA-seq with spatial transcriptomics for comprehensive TME mapping.
Conclusion
Single-cell sequencing provides unprecedented insights into HCC cellular complexity, enabling the identification of actionable targets in malignant subclones and TME components. Translating these findings into clinical applications holds promise for improving diagnosis, prognosis, and treatment of this aggressive malignancy.
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