Evidence map›Paper›PMID 37247132›Full record

ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2023

Single-cell RNA sequencing reveals the lineage of malignant epithelial cells and upregulation of TAGLN2 promotes peritoneal metastasis in gastric cancer.

Chengbo Ji, Junjie Zhao, Hao Chen, Zhaoming Wang, Tianyi Cai, Chenyu Tian, Chen Wang, Dan Liu, Botian Ye, Min Fu and 4 more

Erratum issuedAbstract read
PubMed Publisher
In one paragraph

Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.9field-weighted citation impact, top 6% 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

10 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 1 institution in 1 country.

Chengbo Ji *Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Junjie Zhao *Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Hao Chen *Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Zhaoming WangDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Tianyi CaiDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Chenyu TianDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Chen WangDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Dan LiuDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Botian YeDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Min FuDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Jie SunDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Haojie LiDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China. li.haojie@zs-hospital.sh.cn.ORCID http://orcid.org/0000-0003-0704-8309
Xuefei WangDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Yihong SunDepartment of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China. sun.yihong@zs-hospital.sh.cn.
Sun Yat-sen University · CN

Funding

National Natural Science Foundation of China 81872425National Natural Science Foundation of China 81972228National Natural Science Foundation of China 82002527
6 · The paper itself

Abstract

backgroundPeritoneal metastasis (PM) is an important factor contributing to poor prognosis in patients with gastric cancer (GC). Transcriptomic sequencing has been used to explore the molecular changes in metastatic cancers, but comparing the bulk RNA-sequencing data between primary tumors and metastases in PM studies is unreasonable due to the small proportion of tumor cells in PM tissues.

methodsWe performed single-cell RNA-sequencing analysis on four gastric adenocarcinoma specimens, including one primary tumor sample (PT), one adjacent nontumoral sample (PN), one peritoneal metastatic sample (MT) and one normal peritoneum sample (MN), from the same patient. Pseudotime trajectory analysis was used to display the process by which nonmalignant epithelial cells transform into tumor cells and then metastasize to the peritoneum. Finally, in vitro and in vivo assays were used to validate one of the selected genes that promote peritoneal metastasis.

resultsSingle-cell RNA sequencing showed that a development curve was found from normal mucosa to tumor tissues and then into metastatic sites on peritoneum. TAGLN2 was found to trigger this metastasis process. The migration and invasion capability of GC cells were changed by downregulating and upregulating TAGLN2 expression. Mechanistically, TAGLN2 might modulate tumor metastasis via alterations in cell morphology and several signaling pathways, thus promoting epithelial-mesenchymal transition (EMT).

conclusionsIn summary, we identified and validated TAGLN2 as a novel gene involved in GC peritoneal metastasis. This study provided valuable insight into the mechanisms of GC metastasis and developed a potential therapeutic target to prevent GC cell dissemination.

Indexed as

Peritoneal NeoplasmsStomach NeoplasmsCell Line, TumorCell MovementCell ProliferationEpithelial CellsEpithelial-Mesenchymal TransitionGene Expression Regulation, NeoplasticHumansMicrofilament ProteinsMuscle ProteinsPeritoneumRNAUp-RegulationMicrofilament ProteinsMuscle ProteinsRNATagln2 protein, humanGastric cancerPeritoneal metastasisSingle-cell RNA sequencingTAGLN2

Identifiers

PMID37247132
OpenAlexW4378639834

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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.