Evidence map›Paper›PMID 22206016›Full record

ArticlePloS one2011

Multiple analytical approaches reveal distinct gene-environment interactions in smokers and non smokers in lung cancer.

Rakhshan Ihsan, Pradeep Singh Chauhan, Ashwani Kumar Mishra, Dhirendra Singh Yadav, Mishi Kaushal, Jagannath Dev Sharma, Eric Zomawia, Yogesh Verma, Sujala Kapur, Sunita Saxena

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 11 pooled it
5.1field-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

38 citing papers in PubMed, 11 syntheses or guidelines pooled it, 69 citations in OpenAlex.

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  7. Association between microsomal epoxide hydrolase 1 T113C polymorphism and susceptibility to lung cancer.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2013
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  9. EPHX1 A139G polymorphism and lung cancer risk: a meta-analysis.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2013
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  12. Article
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  17. Differential Effect of Smoking on Gene Expression in Head and Neck Cancer Patients.International journal of environmental research and public health · 2018
    Article
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  20. Transdisciplinary approaches enhance the production of translational knowledge.Translational research : the journal of laboratory and clinical medicine · 2017
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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

10 authors at 6 institutions in 1 country.

Rakhshan IhsanNational Institute of Pathology (Indian Council of Medical Research) Safdarjung Hospital Campus, New Delhi, India.
Pradeep Singh Chauhan
Ashwani Kumar Mishra
Dhirendra Singh Yadav
Mishi Kaushal
Jagannath Dev Sharma
Eric Zomawia
Yogesh Verma
Sujala Kapur
Sunita Saxena
National Institute of Pathology · INSafdarjang Hospital · INAizawl Adventist Hospital · INBhubaneswar Borooah Cancer Institute · INGovernment of Sikkim · INIndian Council of Medical Research · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complex disease such as cancer results from interactions of multiple genetic and environmental factors. Studying these factors singularly cannot explain the underlying pathogenetic mechanism of the disease. Multi-analytical approach, including logistic regression (LR), classification and regression tree (CART) and multifactor dimensionality reduction (MDR), was applied in 188 lung cancer cases and 290 controls to explore high order interactions among xenobiotic metabolizing genes and environmental risk factors. Smoking was identified as the predominant risk factor by all three analytical approaches. Individually, CYP1A1*2A polymorphism was significantly associated with increased lung cancer risk (OR = 1.69;95%CI = 1.11-2.59,p = 0.01), whereas EPHX1 Tyr113His and SULT1A1 Arg213His conferred reduced risk (OR = 0.40;95%CI = 0.25-0.65,p<0.001 and OR = 0.51;95%CI = 0.33-0.78,p = 0.002 respectively). In smokers, EPHX1 Tyr113His and SULT1A1 Arg213His polymorphisms reduced the risk of lung cancer, whereas CYP1A1*2A, CYP1A1*2C and GSTP1 Ile105Val imparted increased risk in non-smokers only. While exploring non-linear interactions through CART analysis, smokers carrying the combination of EPHX1 113TC (Tyr/His), SULT1A1 213GG (Arg/Arg) or AA (His/His) and GSTM1 null genotypes showed the highest risk for lung cancer (OR = 3.73;95%CI = 1.33-10.55,p = 0.006), whereas combined effect of CYP1A1*2A 6235CC or TC, SULT1A1 213GG (Arg/Arg) and betel quid chewing showed maximum risk in non-smokers (OR = 2.93;95%CI = 1.15-7.51,p = 0.01). MDR analysis identified two distinct predictor models for the risk of lung cancer in smokers (tobacco chewing, EPHX1 Tyr113His, and SULT1A1 Arg213His) and non-smokers (CYP1A1*2A, GSTP1 Ile105Val and SULT1A1 Arg213His) with testing balance accuracy (TBA) of 0.6436 and 0.6677 respectively. Interaction entropy interpretations of MDR results showed non-additive interactions of tobacco chewing with SULT1A1 Arg213His and EPHX1 Tyr113His in smokers and SULT1A1 Arg213His with GSTP1 Ile105Val and CYP1A1*2C in nonsmokers. These results identified distinct gene-gene and gene environment interactions in smokers and non-smokers, which confirms the importance of multifactorial interaction in risk assessment of lung cancer.

Indexed as

Gene-Environment InteractionSmokingAdultAgedAged, 80 and overCytochrome P-450 CYP1A1Data MiningEpoxide HydrolasesFalse Positive ReactionsGenetic Predisposition to DiseaseHaplotypesHumansLogistic ModelsLung NeoplasmsMaleMiddle AgedCytochrome P-450 CYP1A1EPHX1 protein, humanEpoxide Hydrolases

Identifiers

PMID22206016
PMCPMC3242784
OpenAlexW2089966905

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.