Evidence map›Paper›PMID 38864342›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Identification and Characterization of Metastasis-Initiating Cells in ESCC in a Multi-Timepoint Pulmonary Metastasis Mouse Model.

Ching Ngar Wong, Yu Zhang, Beibei Ru, Songna Wang, Hongyu Zhou, Jiarun Lin, Yingchen Lyu, Yanru Qin, Peng Jiang, Victor Ho-Fun Lee and 1 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

11 authors.

Ching Ngar WongDepartment of Clinical Oncology, Centre for Cancer Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, 999077, China.ORCID 0000-0002-5034-7067
Yu ZhangDepartment of Pediatric Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Beibei RuCancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20814, USA.
Songna WangDepartment of Clinical Oncology, Centre for Cancer Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, 999077, China.
Hongyu ZhouDepartment of Clinical Oncology, Centre for Cancer Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, 999077, China.
Jiarun LinDepartment of Clinical Oncology, Centre for Cancer Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, 999077, China.
Yingchen LyuDepartment of Clinical Oncology, Centre for Cancer Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, 999077, China.
Yanru QinDepartment of Clinical Oncology, the First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
Peng JiangCancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20814, USA.
Victor Ho-Fun LeeDepartment of Clinical Oncology, Centre for Cancer Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, 999077, China.
Xin-Yuan GuanDepartment of Clinical Oncology, Centre for Cancer Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, 999077, China.ORCID 0000-0002-4485-6017

Funding

General Research Fund 17119322Guangdong Provincial Introduction of Innovative Research and Development Team 2019BT02Y198Guangdong Science and Technology Department 2020B1212030004Hong Kong Research Grant Council C4039-19GFHong Kong Research Grant Council C7026-18GFHong Kong Research Grant Council C7065-18GFNational Natural Science Foundation of China 82072738National Natural Science Foundation of China 82273483Research Grants Council, University Grants Committee T12-703/22-RResearch Impact Fund R1020-18FResearch Impact Fund R4017-18Research Impact Fund R7022-20Shenzhen Key Laboratory for cancer metastasis and personalized therapy ZDSYS20210623091811035Shenzhen Science and Technology Program JCYJ20220818103012025Shenzhen Science and Technology Program JCYJ20220818103014030Shenzhen Science and Technology Program KQTD20180411185028798
6 · The paper itself

Abstract

Metastasis is the biggest obstacle to esophageal squamous cell carcinoma (ESCC) treatment. Single-cell RNA sequencing analyses are applied to investigate lung metastatic ESCC cells isolated from pulmonary metastasis mouse model at multiple timepoints to characterize early metastatic microenvironment. A small population of parental KYSE30 cell line (Cluster S) resembling metastasis-initiating cells (MICs) is identified because they survive and colonize at lung metastatic sites. Differential expression profile comparisons between Cluster S and other subpopulations identified a panel of 7 metastasis-initiating signature genes (MIS), including CD44 and TACSTD2, to represent MICs in ESCC. Functional studies demonstrated MICs (CD44

Indexed as

Disease Models, AnimalEsophageal Squamous Cell CarcinomaLung NeoplasmsAnimalsCell Line, TumorEsophageal NeoplasmsHumansHyaluronan ReceptorsImmunohistochemistryMiceNeoplasm MetastasisNeoplastic Stem CellsTumor MicroenvironmentHyaluronan Receptorsearly cancer metastasisearly metastasis microenvironmentesophageal squamous cell carcinomametastasis biomarkersmetastasis‐initiating cellsmultiplex stainingsingle‐cell transcriptome sequencing

Identifiers

PMID38864342
PMCPMC11321633

What OpenQuestion holds

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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.