Evidence map›Paper›PMID 41771574›Full record

ArticleCancer genomics & proteomics

Decoding the Mechanisms of Hepatocellular Carcinoma Cancer Stem Cells and Identifying Potential Therapeutic Strategies Based on Single-cell Omics.

Xiaotian Tan, Qiaoxian Luo, Ying Ge, Ning Deng, Ping Jin, Menghuan Song, Carolina Oi Lam Ung, Meiwan Chen, Hao Hu

Abstract read
In one paragraph

Article in Cancer genomics & proteomics. 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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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Xiaotian Tan *State Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Qiaoxian LuoState Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Ying GeState Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Ning DengState Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Ping JinState Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Menghuan SongState Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Carolina Oi Lam UngState Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Meiwan Chen *State Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau.
Hao Hu *State Key Laboratory of Mechanism and Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau; haohu@um.edu.mo.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimCancer stem cells (CSCs) play key roles in hepatocellular carcinoma (HCC) initiation, progression, therapeutic resistance, and recurrence, yet their cellular and spatial heterogeneity remains poorly understood. This study aimed to systematically characterize HCC-associated CSCs and identify prognostic biomarkers and potential therapeutic strategies using single-cell omics. MATERIALS AND

methodsSingle-cell RNA sequencing and spatial transcriptomics data were obtained from HCCDB v2.0. Malignant cells were re-clustered using Harmony-based batch correction, followed by uniform manifold approximation and projection (UMAP) and Louvain clustering. Copy number variation analysis validated malignant identities. CSC-associated molecular features were characterized using differential expression, gene regulatory network analysis (SCENIC), pathway enrichment (GSVA), pseudotime trajectory inference (Monocle, CytoTRACE2), and cell-cell communication analysis (CellChat). CSC-specific genes were integrated with GEO survival datasets (GSE76427, GSE14520) to construct a prognostic model, and potential CSC-targeting compounds were predicted using Connectivity Map.

resultsSix malignant subpopulations were identified, including a progenitor-like CSC subset expressing EPCAM, SOX9, and SOX4. Spatial transcriptomics revealed CSC enrichment at the tumor-stroma interface. CSCs exhibited strong stemness, metabolic plasticity, and invasive potential, with activation of WNT/β-catenin, TGF-β, Notch, EMT, MYC, and mTORC1 pathways. Key transcription factors (TEAD2, SOX4, HNF1B, KLF7) were identified. A 12-gene CSC-derived signature stratified patients into distinct risk groups with significantly different overall survival. Several candidate compounds, including fluspirilene, genistein, and daunorubicin, showed potential CSC-suppressive activity.

conclusionThis study provides a comprehensive single-cell-based atlas of CSCs in HCC, highlighting their spatial niches, regulatory programs, and clinical relevance. The identified prognostic signature and candidate drugs offer promising avenues for CSC-targeted therapies.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularLiver NeoplasmsNeoplastic Stem CellsSingle-Cell AnalysisGene Expression Regulation, NeoplasticHumansPrognosisSingle-Cell Gene Expression AnalysisBiomarkers, Tumorhepatocellular carcinomaliver cancerSingle-cell RNA sequencingstem cell

Identifiers

PMID41771574
PMCPMC12951371

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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.