Evidence map›Paper›PMID 31159744›Full record

ArticleBMC genomics2019

Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks.

Kevin Walters, Radmir Sarsenov, Wen Siong Too, Roseanna K Hare, Ian C Paterson, Daniel W Lambert, Stephen Brown, James R Bradford

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 43% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Competing endogenous RNAs (ceRNAs) and drug resistance to cancer therapy.Cancer drug resistance (Alhambra, Calif.) · 2024
    Review
  3. Article
  4. Article
  5. Review
  6. 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

8 authors at 3 institutions in 2 countries.

Kevin WaltersSchool of Mathematics and Statistics, University of Sheffield, Sheffield, South Yorkshire, UK.
Radmir SarsenovSheffield RNAi Screening Facility (SRSF), Department of Biomedical Science, University of Sheffield, Sheffield, South Yorkshire, UK.
Wen Siong TooSheffield RNAi Screening Facility (SRSF), Department of Biomedical Science, University of Sheffield, Sheffield, South Yorkshire, UK.
Roseanna K HareDepartment of Biomedical Science, University of Sheffield, Sheffield, South Yorkshire, UK.
Ian C PatersonDepartment of Oral and Craniofacial Sciences, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia.
Daniel W LambertSheffield Institute for Nucleic Acids (SInFoNiA), Integrated Biosciences, School of Clinical Dentistry, University of Sheffield, Sheffield, South Yorkshire, UK.
Stephen BrownSheffield RNAi Screening Facility (SRSF), Department of Biomedical Science, University of Sheffield, Sheffield, South Yorkshire, UK.
James R BradfordSheffield Institute for Nucleic Acids (SInFoNiA), Department of Oncology and Metabolism, University of Sheffield, Sheffield, South Yorkshire, UK. james.bradford@almacgroup.com.ORCID http://orcid.org/0000-0002-7771-914X
University of Sheffield · GBAlmac (United Kingdom) · GBUniversity of Malaya · MY

Funding

University of Malaya RG456-12HTMYorkshire Cancer Research YCR SHEND01/02
6 · The paper itself

Abstract

backgroundLong non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs.

resultsRemarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelial-mesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment and 12 lncRNAs related to cancer-associated fibroblasts. One member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype.

conclusionsOverall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.

Indexed as

Gene Expression Regulation, NeoplasticGene Regulatory NetworksComputational BiologyGene Expression ProfilingGenetic Association StudiesHumansNeoplasmsRNA, Long NoncodingRNA, MessengerTumor MicroenvironmentRNA, Long NoncodingRNA, MessengerCancerEpithelial-mesenchymal transitionExtracellular matrixFunctional profilingGenes networkslncRNATumour microenvironment

Identifiers

PMID31159744
PMCPMC6547491
OpenAlexW2951719698

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.