Evidence map›Paper›PMID 31654804›Full record

ReviewBiochimica et biophysica acta. Gene regulatory mechanisms2020

Identification of non-cancer cells from cancer transcriptomic data.

Michele Bortolomeazzi, Mohamed Reda Keddar, Francesca D Ciccarelli, Lorena Benedetti

Abstract readReview
In one paragraph

Review in Biochimica et biophysica acta. Gene regulatory mechanisms, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. A CD8Frontiers in immunology · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. 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

4 authors.

Michele BortolomeazziCancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK.
Mohamed Reda KeddarCancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK.
Francesca D CiccarelliCancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK. Electronic address: francesca.ciccarelli@crick.ac.uk.
Lorena BenedettiCancer Systems Biology Laboratory, The Francis Crick Institute, London NW1 1AT, UK; School of Cancer and Pharmaceutical Sciences, King's College London, London SE11UL, UK. Electronic address: lorena.benedetti@crick.ac.uk.

Funding

Cancer Research UK 25487Cancer Research UK C43634/A25487Cancer Research UK C604/A25135Cancer Research UK C604/A25189Cancer Research UK C7893/A26233Medical Research Council MC_PC_16048
6 · The paper itself

Abstract

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Indexed as

BiomarkersHumansNeoplasmsRNA-SeqSingle-Cell AnalysisTumor MicroenvironmentBiomarkersCell-specific signaturesDeconvolutionGene expression profilesGSEARNA-seqTumour microenvironment

Identifiers

PMID31654804
PMCPMC7346884

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.