ReviewJournal of liver cancer2026
Exploring single-cell and multi-omics technologies and their role in unraveling tumor heterogeneity of hepatocellular carcinoma.
Review in Journal of liver cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Review Article: Biomarkers in Liver Transplantation for Hepatocellular Carcinoma: Towards Precision Medicine.Alimentary pharmacology & therapeutics · 2026Review
- Unraveling the regulatory role of intercellular communication in intestinal immune cells mediated by H₂ in sepsis recovery through single-cell RNA sequencing.Journal of translational medicine · 2026Article
- The lncRNA-DNA Methylation Axis in Hepatocellular Carcinoma: Mechanisms, Epigenetic Plasticity, and Biological Implications.Biology · 2026Review
- Development and Validation of a Six-Gene Signature of Myeloid Antigen Presentation Dysfunction Based on Single-Cell and Multi-Cohort Transcriptomics for Predicting Prognosis and Recurrence of Hepatocellular Carcinoma.Cancer informatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Tumor heterogeneity is a major obstacle to effective treatment and is poorly understood using traditional bulk sequencing methods. This review highlights the transformative role of single-cell and multi-omics technologies in determining the cellular and molecular complexities of HCC. We summarize recent advances in single-cell transcriptomics, epigenomics, multi-omics, and spatial transcriptomics platforms, emphasizing their applications in characterizing tumor subclones, cancer-associated fibroblast-immune interactions, circulating tumor cells, and immune-resistant phenotypes. Spatial approaches have revealed the architecture of cancer stem cell niches and tertiary lymphoid structures, providing unprecedented insights into tumor organization and microenvironmental crosstalk. Although still in their early stages, clinical trials have begun to incorporate these technologies, underscoring their translational potential. Single-cell and spatial omics have reshaped HCC research by enabling high-resolution profiling of tumor ecosystems and driving the discovery of biomarkers, therapeutic targets, and strategies for patient stratification. However, high cost, technical expertise, and limited accessibility, particularly in resource-constrained settings, are major barriers to its widespread adoption. Addressing these challenges is critical for translating these powerful approaches into clinical practice and for advancing precision medicine for the treatment of liver cancer.
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Registered trials
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.