Evidence map›Paper›PMID 41112836›Full record

ReviewOncology letters2025

Immune checkpoint biology in hepatocellular carcinoma (Review).

Ching-Hua Hsieh, Pei-Chin Chuang

Abstract readReview
In one paragraph

Review in Oncology letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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

2 authors.

Ching-Hua HsiehDepartment of Plastic Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan, R.O.C.
Pei-Chin ChuangDepartment of Medical Research, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan, R.O.C.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide, frequently arising in the setting of chronic liver inflammation and cirrhosis. Immune checkpoint inhibitors have transformed the treatment landscape for HCC, although response rates remain variable with only a subset of patients deriving durable benefit. The present review provides a comprehensive overview of immune checkpoint biology in HCC, examining their mechanisms of action and their roles within the tumor microenvironment. The present review discusses not only well-established checkpoints (programmed cell death-1 and cytotoxic T-lymphocyte antigen-4) but also emerging inhibitory targets (lymphocyte-activation gene 3, T-cell immunoglobulin and mucin-domain 3, T-cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory motif domains, B and T lymphocyte attenuator, V-domain immunoglobulin suppressor of T-cell activation, B7 homolog 3, B7 homolog 4 and CD47) increasingly recognized in HCC immunology. The clinical implications of checkpoint expression patterns are explored, including their prognostic significance and potential as predictive biomarkers. Current therapeutic strategies are reviewed, from monotherapy approaches to combination regimens involving dual checkpoint blockade and anti-angiogenic agents. Despite recent advances, significant challenges persist, including primary and acquired resistance, the immunosuppressive liver microenvironment and safety concerns in patients with underlying liver dysfunction. Future directions focusing on novel checkpoint targets, innovative combination approaches, personalized cellular therapies and biomarker-driven treatment selection offer potential avenues to improve outcomes for patients with HCC in the future.

Indexed as

combination therapyCTLA-4HCCICIimmunotherapyLAG-3PD-1TIGITTIM-3TME

Identifiers

PMID41112836
PMCPMC12529090

What OpenQuestion holds

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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.