Evidence map›Paper›PMID 41402037›Full record

ReviewJournal of liver cancer2026

Exploring single-cell and multi-omics technologies and their role in unraveling tumor heterogeneity of hepatocellular carcinoma.

Charmi Jyotishi, Suresh Prajapati, Mansi Patel, Reeshu Gupta

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
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

4 authors.

Charmi JyotishiParul Institute of Applied Sciences, Parul University, Gujarat, India.
Suresh PrajapatiParul Institute of Applied Sciences, Parul University, Gujarat, India.
Mansi PatelResearch and Development Cell, Parul University, Gujarat, India.
Reeshu GuptaParul Institute of Applied Sciences, Parul University, Gujarat, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Carcinoma, hepatocellularGenetic heterogeneityNeoplasmsSingle-cell gene expression analysisSpatial transcriptomics

Identifiers

PMID41402037
PMCPMC13062605

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.