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Weak sharing of genetic association signals in three lung cancer subtypes: evidence at the SNP, gene, regulation, and pathway levels

Overview of attention for article published in Genome Medicine, February 2018
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  • Above-average Attention Score compared to outputs of the same age (60th percentile)

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Title
Weak sharing of genetic association signals in three lung cancer subtypes: evidence at the SNP, gene, regulation, and pathway levels
Published in
Genome Medicine, February 2018
DOI 10.1186/s13073-018-0522-9
Pubmed ID
Authors

Timothy D. O’Brien, Peilin Jia, Neil E. Caporaso, Maria Teresa Landi, Zhongming Zhao

Abstract

There are two main types of lung cancer: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). NSCLC has many subtypes, but the two most common are lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). These subtypes are mainly classified by physiological and pathological characteristics, although there is increasing evidence of genetic and molecular differences as well. Although some work has been done at the somatic level to explore the genetic and biological differences among subtypes, little work has been done that interrogates these differences at the germline level to characterize the unique and shared susceptibility genes for each subtype. We used single-nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) of European samples to interrogate the similarity of the subtypes at the SNP, gene, pathway, and regulatory levels. We expanded these genotyped SNPs to include all SNPs in linkage disequilibrium (LD) using data from the 1000 Genomes Project. We mapped these SNPs to several lung tissue expression quantitative trait loci (eQTL) and enhancer datasets to identify regulatory SNPs and their target genes. We used these genes to perform a biological pathway analysis for each subtype. We identified 8295, 8734, and 8361 SNPs with moderate association signals for LUAD, LUSC, and SCLC, respectively. Those SNPs had p < 1 × 10- 3in the original GWAS or were within LD (r2> 0.8, Europeans) to the genotyped SNPs. We identified 215, 320, and 172 disease-associated genes for LUAD, LUSC, and SCLC, respectively. Only five genes (CHRNA5, IDH3A, PSMA4, RP11-650 L12.2, and TBC1D2B) overlapped all subtypes. Furthermore, we observed only two pathways from the Kyoto Encyclopedia of Genes and Genomes shared by all subtypes. At the regulatory level, only three eQTL target genes and two enhancer target genes overlapped between all subtypes. Our results suggest that the three lung cancer subtypes do not share much genetic signal at the SNP, gene, pathway, or regulatory level, which differs from the common subtype classification based upon histology. However, three (CHRNA5, IDH3A, and PSMA4) of the five genes shared between the subtypes are well-known lung cancer genes that may act as general lung cancer genes regardless of subtype.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Student > Bachelor 4 13%
Student > Master 4 13%
Researcher 4 13%
Professor 1 3%
Other 2 7%
Unknown 8 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 33%
Agricultural and Biological Sciences 4 13%
Computer Science 3 10%
Medicine and Dentistry 3 10%
Psychology 1 3%
Other 0 0%
Unknown 9 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 November 2018.
All research outputs
#7,299,930
of 23,025,074 outputs
Outputs from Genome Medicine
#1,124
of 1,448 outputs
Outputs of similar age
#127,782
of 330,058 outputs
Outputs of similar age from Genome Medicine
#20
of 24 outputs
Altmetric has tracked 23,025,074 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,448 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,058 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.