Evidence map›Paper›PMID 41318700›Full record

ReviewCancer metastasis reviews2025

Advancing small cell lung cancer metastasis research: innovations in preclinical mouse models.

Qiqi Zhao, Liang Hu, Hongbin Ji

Abstract readReview
PubMed Publisher
In one paragraph

Review in Cancer metastasis reviews, 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

3 authors.

Qiqi ZhaoKey Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
Liang HuShanghai Institute of Thoracic Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China. liang_hu@sjtu.edu.cn.
Hongbin JiKey Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China. hbji@sibcb.ac.cn.

Funding

National Key Research and Development Program of China 2022YFA1103900National Natural Science Foundation of China 82030083National Natural Science Foundation of China 82473426
6 · The paper itself

Abstract

Small cell lung cancer (SCLC) represents one of the most aggressive malignancies, featured with its extraordinary metastatic capacity. Preclinical mouse models have become indispensable systems for studying the molecular mechanisms underlying SCLC metastasis. This review summarizes recent advances in genetically engineered mouse models (GEMMs) and transplantation models for SCLC metastasis research, highlighting their unique advantages in investigating oncogenic drivers, tumor heterogeneity, immune interactions, and therapeutic responses. We further discuss emerging technologies capable of integrating with these models to advance both mechanistic and translational research. Lineage tracing and multi-omics approaches have provided unprecedented resolution in mapping clonal dynamics and phenotypic plasticity during SCLC metastasis. High-throughput in vivo screening has accelerated the systematic identification of novel metastasis regulators, and humanized mouse models offer clinically relevant systems for investigating human-specific tumor-immune interactions and supporting preclinical evaluation of immunotherapies. Collectively, these preclinical systems are reshaping our understanding of SCLC metastasis and providing powerful platforms to guide therapeutic discovery.

Indexed as

Disease Models, AnimalLung NeoplasmsSmall Cell Lung CarcinomaAnimalsHumansMiceNeoplasm MetastasisAllograftsGenetically engineered mouse modelsMouse modelsSmall cell lung cancer metastasisXenografts

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.