Evidence map›Paper›PMID 41056495›Full record

ArticleHepatology communications2025

3D-bioprinted liver cancer models derived from primary hepatocytes for simulating cancer initiation and drug screening.

Yuanyuan Zhao, Yuxin Li, Wenjie Zhang, Hongyan Jiang, Lina Mao, Runbang He, Yue Ma, Qiangsong Wang, Pengyu Huang

Abstract read
In one paragraph

Article in Hepatology communications, 2025. 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

9 authors.

Yuanyuan ZhaoState Key Laboratory of Advanced Medical Materials and Devices, Engineering Research Center of Pulmonary and Critical Care Medicine Technology and Device (Ministry of Education), Tianjin Key Laboratory of Biomedical Materials, Institute of Biomedical Engineering, Tianjin Institutes of Health Science, Chinese Academy of Medical Science and Peking Union Medical College, Tianjin, China.
Yuxin Li
Wenjie Zhang
Hongyan Jiang
Lina Mao
Runbang He
Yue Ma
Qiangsong Wang
Pengyu Huang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHCC remains one of the most lethal cancers globally, and accurately replicating the early events of tumor evolution remains a critical challenge.

methodsIn this study, we developed early-stage liver cancer cell lines by introducing distinct combinations of oncogenes into primary mouse hepatocytes. Using 3D bioprinting technology, combined with bioinks composed of gelatin and alginate, we constructed a more precise representation of liver cancer tissue to better simulate key tumor characteristics.

resultsOur·findings revealed that different oncogene combinations produced unique drug response profiles, with Ras-driven cells exhibiting heightened sensitivity to ferroptosis. Furthermore, 3D bioprinting tumor tissues derived from transformed hepatocytes effectively captured early HCC characteristics. These models preserved key features of early-stage liver cancer and provided a reliable platform for drug screening. Importantly, the 3D models demonstrated higher resistance to chemotherapy and targeted therapies compared with 2D cultures.

conclusionsIn summary, we established both 2D and 3D models that replicate early HCC progression, offering valuable tools for drug screening and advancing our understanding of early carcinogenic mechanisms.

Indexed as

BioprintingCarcinoma, HepatocellularHepatocytesLiver NeoplasmsPrinting, Three-DimensionalAnimalsAntineoplastic AgentsCell Line, TumorDrug Evaluation, PreclinicalDrug Screening Assays, AntitumorHumansMiceAntineoplastic Agents3D bioprintingcancer initiationdrug screeninghepatocellular carcinomaoncogene combinations

Identifiers

PMID41056495
PMCPMC12506986

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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.