Evidence map›Paper›PMID 24299561›Full record

ArticleBMC medical genomics2013

Integrated molecular portrait of non-small cell lung cancers.

Vladimir Lazar, Chen Suo, Cedric Orear, Joost van den Oord, Zsofia Balogh, Justine Guegan, Bastien Job, Guillaume Meurice, Hugues Ripoche, Stefano Calza and 18 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed, 59 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Molecular Pathology of Lung Cancer.Cold Spring Harbor perspectives in medicine · 2022
    Review
  5. Article
  6. Article
  7. Article
  8. Failure to Guard: Mitochondrial Protein Quality Control in Cancer.International journal of molecular sciences · 2021
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. An Expression Signature as an Aid to the Histologic Classification of Non-Small Cell Lung Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2016
    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

28 authors at 8 institutions in 5 countries.

Vladimir LazarDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@ki.se.
Chen Suo
Cedric Orear
Joost van den Oord
Zsofia Balogh
Justine Guegan
Bastien Job
Guillaume Meurice
Hugues Ripoche
Stefano Calza
Johanna Hasmats
Joakim Lundeberg
Ludovic Lacroix
Philippe Vielh
Fabienne Dufour
Janne Lehtiö
Rudolf Napieralski
Alexander Eggermont
Manfred Schmitt
Jacques Cadranel
Benjamin Besse
Philippe Girard
Fiona Blackhall
Pierre Validire
Jean-Charles Soria
Philippe Dessen
Johan Hansson
Yudi Pawitan
Institut Gustave Roussy · FRKarolinska Institutet · SEInstitute Mutualiste Montsouris · FRKTH Royal Institute of Technology · SETechnical University of Munich · DEHôpital Tenon · FRKU Leuven · BEUniversity of Manchester · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNon-small cell lung cancer (NSCLC), a leading cause of cancer deaths, represents a heterogeneous group of neoplasms, mostly comprising squamous cell carcinoma (SCC), adenocarcinoma (AC) and large-cell carcinoma (LCC). The objectives of this study were to utilize integrated genomic data including copy-number alteration, mRNA, microRNA expression and candidate-gene full sequencing data to characterize the molecular distinctions between AC and SCC.

methodsComparative genomic hybridization followed by mutational analysis, gene expression and miRNA microarray profiling were performed on 123 paired tumor and non-tumor tissue samples from patients with NSCLC.

resultsAt DNA, mRNA and miRNA levels we could identify molecular markers that discriminated significantly between the various histopathological entities of NSCLC. We identified 34 genomic clusters using aCGH data; several genes exhibited a different profile of aberrations between AC and SCC, including PIK3CA, SOX2, THPO, TP63, PDGFB genes. Gene expression profiling analysis identified SPP1, CTHRC1 and GREM1 as potential biomarkers for early diagnosis of the cancer, and SPINK1 and BMP7 to distinguish between AC and SCC in small biopsies or in blood samples. Using integrated genomics approach we found in recurrently altered regions a list of three potential driver genes, MRPS22, NDRG1 and RNF7, which were consistently over-expressed in amplified regions, had wide-spread correlation with an average of ~800 genes throughout the genome and highly associated with histological types. Using a network enrichment analysis, the targets of these potential drivers were seen to be involved in DNA replication, cell cycle, mismatch repair, p53 signalling pathway and other lung cancer related signalling pathways, and many immunological pathways. Furthermore, we also identified one potential driver miRNA hsa-miR-944.

conclusionsIntegrated molecular characterization of AC and SCC helped identify clinically relevant markers and potential drivers, which are recurrent and stable changes at DNA level that have functional implications at RNA level and have strong association with histological subtypes.

Indexed as

GenomicsAdultAgedAged, 80 and overCarcinoma, Non-Small-Cell LungDNA Copy Number VariationsFemaleGene Expression ProfilingHumansLung NeoplasmsMaleMicroRNAsMiddle AgedRNA, MessengerMicroRNAsRNA, Messenger

Identifiers

PMID24299561
PMCPMC4222074
OpenAlexW1975310196

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.