Evidence map›Paper›PMID 40852642›Full record

ArticleBiomaterials research2025

Liver-Specific Extracellular Matrix Enables High-Fidelity Patient-Derived Hepatocellular Carcinoma Xenograft Models.

Su Kyeom Kim, Jungho Bae, Mi Jeong Lee, Dai Hoon Han, Seung-Woo Cho

Abstract read
In one paragraph

Article in Biomaterials research, 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. 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

5 authors.

Su Kyeom KimDepartment of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.
Jungho BaeDepartment of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.
Mi Jeong LeeDepartment of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.
Dai Hoon HanDepartment of Surgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.ORCID https://orcid.org/0000-0003-2787-7876
Seung-Woo ChoDepartment of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.ORCID https://orcid.org/0000-0001-8058-332X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patient-derived tumor xenograft (PDX) models serve as powerful tools in oncology research owing to their ability to capture patient-specific tumor heterogeneity and clinical behavior. However, the conventional matrices derived from murine tumors, commonly used to generate PDX models, suffer from key limitations such as lack of tissue specificity, high production costs, and inconsistent batch quality. In response, our study investigates the use of decellularized liver extracellular matrix (Liver ECM) as a biomimetic alternative that more accurately recapitulates the native hepatic microenvironment. We demonstrate that Liver ECM, enriched with liver-specific biochemical cues, enables robust engraftment, growth, and metastasis of patient-derived hepatocellular carcinoma cells in both subcutaneous and orthotopic PDX models. Notably, orthotopic models established with Liver ECM exhibited enhanced metastatic behavior, particularly to the intestine, compared to those formed using conventional matrices. Transcriptomic analysis further revealed activation of key pathways associated with cancer progression, including angiogenesis, apoptosis, migration, and inflammation. Additionally, we extend the application of Liver ECM to patient-derived organoid xenografts, which showed improved tumorigenicity and retained pathophysiological features of the original tumor tissue. Together, these findings underscore the potential of liver-specific ECM as a superior platform for generating physiologically relevant PDX models and enhancing the translational relevance of preclinical cancer studies.

Identifiers

PMID40852642
PMCPMC12369845

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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.