Evidence map›Paper›PMID 34785704›Full record

ArticleScientific reports2021

Characterization of normal and cancer stem-like cell populations in murine lingual epithelial organoids using single-cell RNA sequencing.

Erik Johansson, Hiroo Ueno

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 39% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Review
  2. Application of single-cell RNA sequencing in head and neck squamous cell carcinoma.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2023
    Article
  3. Article
  4. Review
  5. 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

2 authors at 1 institution in 1 country.

Erik JohanssonDepartment of Stem Cell Pathology, Kansai Medical University, 2-5-1 Shin-machi, Hirakata, Osaka, 573-1010, Japan.
Hiroo UenoDepartment of Stem Cell Pathology, Kansai Medical University, 2-5-1 Shin-machi, Hirakata, Osaka, 573-1010, Japan. hueno-tky@umin.ac.jp.
Kansai Medical University · JP

Funding

JST CREST JPMJCR16G3
6 · The paper itself

Abstract

The advances in oral cancer research and therapies have not improved the prognosis of patients with tongue cancer. The poor treatment response of tongue cancer may be attributed to the presence of heterogeneous tumor cells exhibiting stem cell characteristics. Therefore, there is a need to develop effective molecular-targeted therapies based on the specific gene expression profiles of these cancer stem-like cell populations. In this study, the characteristics of normal and cancerous organoids, which are convenient tools for screening anti-cancer drugs, were analyzed comparatively. As organoids are generally generated by single progenitors, they enable the exclusion of normal cell contamination from the analyses. Single-cell RNA sequencing analysis revealed that p53 signaling activation and negative regulation of cell cycle were enriched characteristics in normal stem-like cells whereas hypoxia-related pathways, such as HIF-1 signaling and glycolysis, were upregulated in cancer stem-like cells. The findings of this study improved our understanding of the common features of heterogeneous cell populations with stem cell properties in tongue cancers, that are different from those of normal stem cell populations; this will enable the development of novel molecular-targeted therapies for tongue cancer.

Indexed as

RNA-SeqSingle-Cell AnalysisAnimalsMiceMice, TransgenicNeoplasm ProteinsNeoplastic Stem CellsOrganoidsRNA, NeoplasmTongue NeoplasmsNeoplasm ProteinsRNA, Neoplasm

Identifiers

PMID34785704
PMCPMC8595654
OpenAlexW3211874076

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.