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Platform-independent gene expression signature differentiates sessile serrated adenomas/polyps and hyperplastic polyps of the colon

Overview of attention for article published in BMC Medical Genomics, December 2017
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Title
Platform-independent gene expression signature differentiates sessile serrated adenomas/polyps and hyperplastic polyps of the colon
Published in
BMC Medical Genomics, December 2017
DOI 10.1186/s12920-017-0317-7
Pubmed ID
Authors

Yasir Rahmatallah, Magomed Khaidakov, Keith K. Lai, Hannah E. Goyne, Laura W. Lamps, Curt H. Hagedorn, Galina Glazko

Abstract

Sessile serrated adenomas/polyps are distinguished from hyperplastic colonic polyps subjectively by their endoscopic appearance and histological morphology. However, hyperplastic and sessile serrated polyps can have overlapping morphological features resulting in sessile serrated polyps diagnosed as hyperplastic. While sessile serrated polyps can progress into colon cancer, hyperplastic polyps have virtually no risk for colon cancer. Objective measures, differentiating these types of polyps would improve cancer prevention and treatment outcome. RNA-seq training data set and Affimetrix, Illumina testing data sets were obtained from Gene Expression Omnibus (GEO). RNA-seq single-end reads were filtered with FastX toolkit. Read mapping to the human genome, gene abundance estimation, and differential expression analysis were performed with Tophat-Cufflinks pipeline. Background correction, normalization, and probe summarization steps for Affimetrix arrays were performed using the robust multi-array method (RMA). For Illumina arrays, log2-scale expression data was obtained from GEO. Pathway analysis was implemented using Bioconductor package GSAR. To build a platform-independent molecular classifier that accurately differentiates sessile serrated and hyperplastic polyps we developed a new feature selection step. We also developed a simple procedure to classify new samples as either sessile serrated or hyperplastic with a class probability assigned to the decision, estimated using Cantelli's inequality. The classifier trained on RNA-seq data and tested on two independent microarray data sets resulted in zero and three errors. The classifier was further tested using quantitative real-time PCR expression levels of 45 blinded independent formalin-fixed paraffin-embedded specimens and was highly accurate. Pathway analyses have shown that sessile serrated polyps are distinguished from hyperplastic polyps and normal controls by: up-regulation of pathways implicated in proliferation, inflammation, cell-cell adhesion and down-regulation of serine threonine kinase signaling pathway; differential co-expression of pathways regulating cell division, protein trafficking and kinase activities. Most of the differentially expressed pathways are known as hallmarks of cancer and likely to explain why sessile serrated polyps are more prone to neoplastic transformation than hyperplastic. The new molecular classifier includes 13 genes and may facilitate objective differentiation between two polyps.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 14%
Student > Ph. D. Student 5 14%
Student > Bachelor 5 14%
Student > Doctoral Student 3 8%
Professor 3 8%
Other 8 22%
Unknown 8 22%
Readers by discipline Count As %
Medicine and Dentistry 11 30%
Biochemistry, Genetics and Molecular Biology 4 11%
Immunology and Microbiology 3 8%
Agricultural and Biological Sciences 2 5%
Psychology 2 5%
Other 5 14%
Unknown 10 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 February 2019.
All research outputs
#12,602,576
of 14,257,572 outputs
Outputs from BMC Medical Genomics
#648
of 751 outputs
Outputs of similar age
#239,941
of 276,630 outputs
Outputs of similar age from BMC Medical Genomics
#13
of 16 outputs
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