Evidence map›Paper›PMID 36734079›Full record

ArticleCancer research2023

Cancer Cells Employ the Most Prolific RNA Editors: A Closer Look at the Single-Cell Level.

Yikai Luo, Han Liang

Abstract readEditorialComment
PubMed Publisher
In one paragraph

Article in Cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Yikai LuoGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas.ORCID 0000-0001-7589-7981
Han LiangGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, Texas.ORCID 0000-0001-7633-286X
The University of Texas MD Anderson Cancer Center · US

Funding

TCPA: an Integrated Bioinformatics Resource for Functional Cancer Proteomic DataU24CA209851 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIANG, HAN, MILLS, GORDON B. · 2016 to 2020
$4.0M
Characterization and modeling of m6A RNA methylation in cancerR01CA251150 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIANG, HAN · 2020 to 2024
$2.7M
Mechanistic maps of adaptive responses to therapeutic stress to optimize combination therapies.U01CA253472 · NCI · OREGON HEALTH & SCIENCE UNIVERSITY · PI KORKUT, ANIL, LIANG, HAN · 2021 to 2025
$2.6M
Systematic Functional Characterization of RNA Editing in Endometrial CancerR01CA175486 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIANG, HAN · 2014 to 2018
$1.7M
NCI NIH HHS R01 CA175486NCI NIH HHS R01 CA251150NCI NIH HHS U01 CA253472NCI NIH HHS U24 CA209851
6 · The paper itself

Abstract

Adenosine-to-inosine (A-to-I) RNA editing is a major source of nucleotide diversification that has significant mechanistic implications in cancer progression and treatment response. However, its activity and prevalence have not yet been systematically determined at a single-cell resolution. Chan and colleagues revealed widespread A-to-I RNA editing events in single cancer cells through an in-depth analysis of a public lung adenocarcinoma single-cell transcriptome dataset. Edits significantly enriched in cancer cells compared to other cell types have the potential to inhibit innate immune response and to predict poor therapeutic response and prognosis in patients treated with targeted therapies. See related article by Chan et al., p. 374.

Indexed as

Adenocarcinoma of LungLung NeoplasmsHumansImmunity, InnatePrognosisRNARNA EditingSingle-Cell AnalysisRNA

Identifiers

PMID36734079
OpenAlexW4319063162

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.