Evidence map›Paper›PMID 40636406›Full record

ArticleiLIVER2023

Single-cell transcriptome analysis reveals a cancer-associated fibroblast marker gene signature in hepatocellular carcinoma that predicts prognosis.

Hao Chi, Dapeng Chen, Yuliang Zhang, Zilin Cui, Yi Bai, Yamin Zhang

Abstract read
In one paragraph

Article in iLIVER, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Hao ChiTianjin First Central Hospital Clinic Institute, Tianjin Medical University, Tianjin 300192, China.
Dapeng ChenTianjin First Central Hospital Clinic Institute, Tianjin Medical University, Tianjin 300192, China.
Yuliang ZhangTianjin First Central Hospital Clinic Institute, Tianjin Medical University, Tianjin 300192, China.
Zilin CuiDepartment of Hepatobiliary Surgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin 300192, China.
Yi BaiDepartment of Hepatobiliary Surgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin 300192, China.
Yamin ZhangDepartment of Hepatobiliary Surgery, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin 300192, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death. Multi-pathway combination therapy is used to treat HCC, and immunotherapy is also a routine part of treatment. As a major component of the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) actively participate in cancer progression through complex functions. However, because CAFs dynamically change during cancer development, most of the current treatment strategies targeting CAFs fail. We created a prognostic CAF marker gene signature (CAFMGS) to investigate the utility of CAFs as a prognostic factor and therapeutic target. Methods: Gene Expression Omnibus (GEO) single-cell RNA sequencing (Sc-RNA-seq) data were analyzed to identify CAF marker genes in HCC. The Cancer Genome Atlas (TCGA) database was used as a training cohort to construct the CAFMGS model and the International Cancer Genome Consortium (ICGC) dataset was used to validate the CAFMGS. Results: Marker genes in the CAFMGS model were (0.0001- Conclusion: CAFMGS offers a fresh perspective on stromal cell marker genes in HCC prognosis and expands our knowledge of CAF heterogeneity and functional diversity, perhaps paving the way for CAF-targeted immunotherapy in HCC patients.

Indexed as

Cancer-associated fibroblast marker genesHepatocellular carcinomaPrognostic signature

Identifiers

PMID40636406
PMCPMC12212742

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.