Evidence map›Paper›PMID 37537209›Full record

ReviewCancer gene therapy2023

Patient-derived tumor models: a suitable tool for preclinical studies on esophageal cancer.

Fan Liang, Hongyan Xu, Hongwei Cheng, Yabo Zhao, Junhe Zhang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Cancer gene therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

5 authors.

Fan LiangInstitutes of Health Central Plains, Xinxiang Medical University, Xinxiang, 453003, China.
Hongyan XuSchool of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, 453003, China.
Hongwei ChengInstitutes of Health Central Plains, Xinxiang Medical University, Xinxiang, 453003, China.
Yabo ZhaoSchool of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, 453003, China.
Junhe ZhangInstitutes of Health Central Plains, Xinxiang Medical University, Xinxiang, 453003, China. zjh@xxmu.edu.cn.ORCID 0000-0003-0343-0340

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Esophageal cancer (EC) is the tenth most common cancer worldwide and has high morbidity and mortality. Its main subtypes include esophageal squamous cell carcinoma and esophageal adenocarcinoma, which are usually diagnosed during their advanced stages. The biological defects and inability of preclinical models to summarize completely the etiology of multiple factors, the complexity of the tumor microenvironment, and the genetic heterogeneity of tumors severely limit the clinical treatment of EC. Patient-derived models of EC not only retain the tissue structure, cell morphology, and differentiation characteristics of the original tumor, they also retain tumor heterogeneity. Therefore, compared with other preclinical models, they can better predict the efficacy of candidate drugs, explore novel biomarkers, combine with clinical trials, and effectively improve patient prognosis. This review discusses the methods and animals used to establish patient-derived models and genetically engineered mouse models, especially patient-derived xenograft models. It also discusses their advantages, applications, and limitations as preclinical experimental research tools to provide an important reference for the precise personalized treatment of EC and improve the prognosis of patients.

Indexed as

AdenocarcinomaEsophageal NeoplasmsEsophageal Squamous Cell CarcinomaAnimalsDisease Models, AnimalHumansMiceTumor Microenvironment

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.