Evidence map›Paper›PMID 24563515›Full record

ArticleJournal of the National Cancer Institute2014

Transcriptomic architecture of the adjacent airway field cancerization in non-small cell lung cancer.

Humam Kadara, Junya Fujimoto, Suk-Young Yoo, Yuho Maki, Adam C Gower, Mohamed Kabbout, Melinda M Garcia, Chi-Wan Chow, Zuoming Chu, Gabriella Mendoza and 8 more

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed
4.7field-weighted citation impact, top 4% 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

61 citing papers in PubMed, 85 citations in OpenAlex.

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  17. A computational model for the cancer field effect.Frontiers in artificial intelligence · 2023
    Article
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  20. Observational

1 more citing papers are in PubMed but not listed here.

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

18 authors at 1 institution in 1 country.

Humam KadaraAffiliations of authors: Department of Translational Molecular Pathology (HK, JF, TM, MMG, C-WC, ZC, GM, IIW), Department of Thoracic/Head and Neck Medical Oncology (MK, WKH, IIW), Department of Pathology (NK, CM), Department of Bioinformatics (S-YY, LS, JW, KRC), University of Texas MD Anderson Cancer Center, Houston, TX; Section of Computational Medicine, Department of Medicine, Boston University, Boston, MA (ACG, AS).
Junya Fujimoto
Suk-Young Yoo
Yuho Maki
Adam C Gower
Mohamed Kabbout
Melinda M Garcia
Chi-Wan Chow
Zuoming Chu
Gabriella Mendoza
Li Shen
Neda Kalhor
Waun Ki Hong
Cesar Moran
Jing Wang
Avrum Spira
Kevin R Coombes
Ignacio I Wistuba
The University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
UNIVERSITY OF TEXAS--SPORE IN LUNG CANCERP50CA070907 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI GIBBONS, DON LYNN · 1996 to 2024
$57.4M
NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA070907NCI NIH HHS P50 CA70907
6 · The paper itself

Abstract

backgroundEarlier work identified specific tumor-promoting abnormalities that are shared between lung cancers and adjacent normal bronchial epithelia. We sought to characterize the yet unknown global molecular and adjacent airway field cancerization (FC) in early-stage non-small cell lung cancer (NSCLC).

methodsWhole-transcriptome expression profiling of resected early-stage (I-IIIA) NSCLC specimens (n = 20) with matched tumors, multiple cytologically controlled normal airways with varying distances from tumors, and uninvolved normal lung tissues (n = 194 samples) was performed using the Affymetrix Human Gene 1.0 ST platform. Mixed-effects models were used to identify differentially expressed genes among groups. Ordinal regression analysis was performed to characterize site-dependent airway expression profiles. All statistical tests were two-sided, except where noted.

resultsWe identified differentially expressed gene features (n = 1661) between NSCLCs and airways compared with normal lung tissues, a subset of which (n = 299), after gene set enrichment analysis, statistically significantly (P < .001) distinguished large airways in lung cancer patients from airways in cancer-free smokers. In addition, we identified genes (n = 422) statistically significantly and progressively differentially expressed in airways by distance from tumors that were found to be congruently modulated between NSCLCs and normal lung tissues. Furthermore, LAPTM4B, with statistically significantly increased expression (P < .05) in airways with shorter distance from tumors, was upregulated in human immortalized cells compared with normal bronchial epithelial cells (P < .001) and promoted anchorage-dependent and -independent lung cancer cell growth.

conclusionsThe adjacent airway FC comprises both site-independent profiles as well as gradient and localized airway expression patterns. Profiling of the airway FC may provide new insights into NSCLC oncogenesis and molecular tools for detection of the disease.

Indexed as

TranscriptomeAdultAgedCarcinoma, Non-Small-Cell LungFemaleGene Expression Regulation, NeoplasticHumansLung NeoplasmsMaleMembrane ProteinsMicroarray AnalysisMiddle AgedNeoplasm StagingOncogene ProteinsProtein Serine-Threonine KinasesReal-Time Polymerase Chain ReactionLAPTM4B protein, humanMembrane ProteinsNETO2 protein, humanOncogene ProteinsProtein Serine-Threonine KinasesReceptors, Transforming Growth Factor betaReceptors, Vasoactive Intestinal Polypeptide, Type IReceptor, Transforming Growth Factor-beta Type IIVIPR1 protein, human

Identifiers

PMID24563515
PMCPMC3982778
OpenAlexW2145371947

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.