Triple-layer dissection of the lung adenocarcinoma transcriptome – regulation at the gene, transcript, and exon levels
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Min-Kung Hsu1, I-Ching Wu2, Ching-Chia Cheng3, Jen-Liang Su3,4,5,6, Chang-Huain Hsieh7, Yeong-Shin Lin1, Feng-Chi Chen1,2,8
1Department of Biological Science and Technology, National Chiao-Tung University, Hsinchu, Taiwan
2Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
3National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
4Graduate Institute of Cancer Biology, China Medical University, Taichung, Taiwan
5Department of Biotechnology, Asia University, Taichung, Taiwan
6Center for Molecular Medicine, China Medical University Hospital, Taichung, Taiwan
7Cloud Computing and System Integration Division, National Center for High-Performance Computing, Taichung, Taiwan
8Department of Dentistry, China Medical University, Taichung, Taiwan
Feng-Chi Chen, e-mail: firstname.lastname@example.org
Jen-Liang Su, e-mail: email@example.com
Keywords: lung adenocarcinoma, transcriptome analysis, alternative splicing, differential expression, transcript-specific regulation
Received: February 27, 2015 Accepted: August 21, 2015 Published: September 02, 2015
Lung adenocarcinoma is one of the most deadly human diseases. However, the molecular mechanisms underlying this disease, particularly RNA splicing, have remained underexplored. Here, we report a triple-level (gene-, transcript-, and exon-level) analysis of lung adenocarcinoma transcriptomes from 77 paired tumor and normal tissues, as well as an analysis pipeline to overcome genetic variability for accurate differentiation between tumor and normal tissues. We report three major results. First, more than 5,000 differentially expressed transcripts/exonic regions occur repeatedly in lung adenocarcinoma patients. These transcripts/exonic regions are enriched in nicotine metabolism and ribosomal functions in addition to the pathways enriched for differentially expressed genes (cell cycle, extracellular matrix receptor interaction, and axon guidance). Second, classification models based on rationally selected transcripts or exonic regions can reach accuracies of 0.93 to 1.00 in differentiating tumor from normal tissues. Of the 28 selected exonic regions, 26 regions correspond to alternative exons located in such regulators as tumor suppressor (GDF10), signal receptor (LYVE1), vascular-specific regulator (RASIP1), ubiquitination mediator (RNF5), and transcriptional repressor (TRIM27). Third, classification systems based on 13 to 14 differentially expressed genes yield accuracies near 100%. Genes selected by both detection methods include C16orf59, DAP3, ETV4, GABARAPL1, PPAR, RADIL, RSPO1, SERTM1, SRPK1, ST6GALNAC6, and TNXB. Our findings imply a multilayered lung adenocarcinoma regulome in which transcript-/exon-level regulation may be dissociated from gene-level regulation. Our described method may be used to identify potentially important genes/transcripts/exonic regions for the tumorigenesis of lung adenocarcinoma and to construct accurate tumor vs. normal classification systems for this disease.
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