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Deep Sequence Analysis of Non-Small Cell Lung Cancer: Integrated Analysis of Gene Expression, Alternative Splicing, and Single Nucleotide Variations in Lung Adenocarcinomas with and without Oncogenic…

Overview of attention for article published in Frontiers in oncology, January 2012
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  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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2 X users

Citations

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46 Dimensions

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116 Mendeley
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Title
Deep Sequence Analysis of Non-Small Cell Lung Cancer: Integrated Analysis of Gene Expression, Alternative Splicing, and Single Nucleotide Variations in Lung Adenocarcinomas with and without Oncogenic KRAS Mutations
Published in
Frontiers in oncology, January 2012
DOI 10.3389/fonc.2012.00012
Pubmed ID
Authors

Krishna R. Kalari, David Rossell, Brian M. Necela, Yan W. Asmann, Asha Nair, Saurabh Baheti, Jennifer M. Kachergus, Curtis S. Younkin, Tiffany Baker, Jennifer M. Carr, Xiaojia Tang, Michael P. Walsh, High-Seng Chai, Zhifu Sun, Steven N. Hart, Alexey A. Leontovich, Asif Hossain, Jean-Pierre Kocher, Edith A. Perez, David N. Reisman, Alan P. Fields, E. Aubrey Thompson

Abstract

KRAS mutations are highly prevalent in non-small cell lung cancer (NSCLC), and tumors harboring these mutations tend to be aggressive and resistant to chemotherapy. We used next-generation sequencing technology to identify pathways that are specifically altered in lung tumors harboring a KRAS mutation. Paired-end RNA-sequencing of 15 primary lung adenocarcinoma tumors (8 harboring mutant KRAS and 7 with wild-type KRAS) were performed. Sequences were mapped to the human genome, and genomic features, including differentially expressed genes, alternate splicing isoforms and single nucleotide variants, were determined for tumors with and without KRAS mutation using a variety of computational methods. Network analysis was carried out on genes showing differential expression (374 genes), alternate splicing (259 genes), and SNV-related changes (65 genes) in NSCLC tumors harboring a KRAS mutation. Genes exhibiting two or more connections from the lung adenocarcinoma network were used to carry out integrated pathway analysis. The most significant signaling pathways identified through this analysis were the NFκB, ERK1/2, and AKT pathways. A 27 gene mutant KRAS-specific sub network was extracted based on gene-gene connections from the integrated network, and interrogated for druggable targets. Our results confirm previous evidence that mutant KRAS tumors exhibit activated NFκB, ERK1/2, and AKT pathways and may be preferentially sensitive to target therapeutics toward these pathways. In addition, our analysis indicates novel, previously unappreciated links between mutant KRAS and the TNFR and PPARγ signaling pathways, suggesting that targeted PPARγ antagonists and TNFR inhibitors may be useful therapeutic strategies for treatment of mutant KRAS lung tumors. Our study is the first to integrate genomic features from RNA-Seq data from NSCLC and to define a first draft genomic landscape model that is unique to tumors with oncogenic KRAS mutations.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 3%
United States 3 3%
Nepal 1 <1%
France 1 <1%
Australia 1 <1%
Norway 1 <1%
Canada 1 <1%
United Kingdom 1 <1%
Unknown 104 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 29%
Researcher 30 26%
Student > Master 10 9%
Student > Postgraduate 8 7%
Student > Doctoral Student 6 5%
Other 18 16%
Unknown 10 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 40%
Biochemistry, Genetics and Molecular Biology 28 24%
Medicine and Dentistry 15 13%
Computer Science 8 7%
Social Sciences 2 2%
Other 6 5%
Unknown 11 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 02 June 2012.
All research outputs
#16,047,334
of 25,374,647 outputs
Outputs from Frontiers in oncology
#5,632
of 22,416 outputs
Outputs of similar age
#163,297
of 250,101 outputs
Outputs of similar age from Frontiers in oncology
#57
of 161 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,416 research outputs from this source. They receive a mean Attention Score of 3.0. This one has gotten more attention than average, scoring higher than 71% of its peers.
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 250,101 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 161 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.