Evidence map›Paper›PMID 35844788›Full record

ArticleInternational journal of biological sciences2022

Spatiotemporal quantification of metastatic tumour cell growth and distribution in lymph nodes by whole-mount tissue 3D imaging.

Jun Li, Chun-Jie Xu, Guang-Ang Tian, Qing Li, Dong-Xue Li, Fang Yan, Yao-Qi Zhou, Pei-Qi Huang, Jia-Xuan Xie, Xu Wang and 9 more

Open access · goldAbstract read
In one paragraph

Article in International journal of biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. 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

19 authors at 5 institutions in 2 countries.

Jun LiState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Chun-Jie XuDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Guang-Ang TianState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Qing LiState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Dong-Xue LiState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Fang YanSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yao-Qi ZhouState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Pei-Qi HuangState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jia-Xuan XieState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xu WangState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shu-Heng JiangState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Ya-Hui WangState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jian SongDepartment of Radiation Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xue-Li ZhangState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shuang-Qin YiDepartment of Frontier Health Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Japan.
Li-Peng HuState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Qing XuDepartment of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xiao-Wei LiSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Zhi-Gang ZhangState Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Jiao Tong University · CNShanghai Cancer Institute · CNRenji Hospital · CNState Key Laboratory of Oncogene and Related Genes · CNTokyo Metropolitan University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lymph nodes (LNs) are a common site of metastasis in many solid cancers. Tumour cells can migrate to LNs for further metastatic colonization of distant organs, indicating poor prognosis and requiring different clinical interventions. The histopathological diagnostic methods currently used to detect clinical lymph node metastasis (LNM) have limitations, such as incomplete visualization. To obtain a complete picture of metastatic LNs on the spatial and temporal scales, we used ultimate 3D imaging of solvent-cleared organs (uDISCO) and 3D rapid immunostaining. MC38 cells labelled with EGFP were injected into the left footpads of C57BL/6 mice. Draining lymph nodes (DLNs) harvested from these mice were cleared using the uDISCO method. Metastatic colorectal cancer (CRC) cells in various regions of DLNs from mice at different time points were quantified using 3D imaging of whole-mount tissue. Several stages of tumour cell growth and distribution in LNs were identified: 1) invasion of lymphatic vessels (LVs) and blood vessels (BVs); 2) dispersion outside LVs and BVs for proliferation and expansion; and 3) re-entry into BVs and efferent lymphatic vessels (ELVs) for further distant metastasis. Moreover, these data demonstrated that mouse fibroblast cells (MFCs) could not only promote LNM of tumour cells but also metastasize to LNs together with tumour cells, thus providing a "soil" for tumour cell colonization. In conclusion, 3D imaging of whole-mount tissue and spatiotemporal analysis of LNM may collectively constitute an auxiliary method to improve the accuracy of clinical LNM detection.

Indexed as

Imaging, Three-DimensionalLymphatic VesselsAnimalsLymphatic MetastasisLymph NodesMiceMice, Inbred C57BL

Identifiers

PMID35844788
PMCPMC9274482
OpenAlexW4285155453

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.