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. 2013;8(3):e59319.
doi: 10.1371/journal.pone.0059319. Epub 2013 Mar 22.

Investigation of radiation-induced transcriptome profile of radioresistant non-small cell lung cancer A549 cells using RNA-seq

Hee Jung Yang  1 , Namshin Kim, Ki Moon Seong, HyeSook Youn, BuHyun Youn
Affiliations

Investigation of radiation-induced transcriptome profile of radioresistant non-small cell lung cancer A549 cells using RNA-seq

Hee Jung Yang et al. PLoS One. 2013.

Abstract

Radioresistance is a main impediment to effective radiotherapy for non-small cell lung cancer (NSCLC). Despite several experimental and clinical studies of resistance to radiation, the precise mechanism of radioresistance in NSCLC cells and tissues still remains unclear. This result could be explained by limitation of previous researches such as a partial understanding of the cellular radioresistance mechanism at a single molecule level. In this study, we aimed to investigate extensive radiation responses in radioresistant NSCLC cells and to identify radioresistance-associating factors. For the first time, using RNA-seq, a massive sequencing-based approach, we examined whole-transcriptome alteration in radioresistant NSCLC A549 cells under irradiation, and verified significant radiation-altered genes and their chromosome distribution patterns. Also, bioinformatic approaches (GO analysis and IPA) were performed to characterize the radiation responses in radioresistant A549 cells. We found that epithelial-mesenchymal transition (EMT), migration and inflammatory processes could be meaningfully related to regulation of radiation responses in radioresistant A549 cells. Based on the results of bioinformatic analysis for the radiation-induced transcriptome alteration, we selected seven significant radiation-altered genes (SESN2, FN1, TRAF4, CDKN1A, COX-2, DDB2 and FDXR) and then compared radiation effects in two types of NSCLC cells with different radiosensitivity (radioresistant A549 cells and radiosensitive NCI-H460 cells). Interestingly, under irradiation, COX-2 showed the most significant difference in mRNA and protein expression between A549 and NCI-H460 cells. IR-induced increase of COX-2 expression was appeared only in radioresistant A549 cells. Collectively, we suggest that COX-2 (also known as prostaglandin-endoperoxide synthase 2 (PTGS2)) could have possibility as a putative biomarker for radioresistance in NSCLC cells.

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Conflict of interest statement

Competing Interests: KMS is currently employees of Radiation Health Research Institute, Korea Hydro & Nuclear Power Co., Ltd. Other authors declare that there are no conflicts of interest. There are no further patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Design of RNA-seq study in radioresistant NSCLC A549 cells.
(A) Determination of appropriate irradiation condition based on expression of representative radioresponsive proteins. (B) A schematic diagram for design and goals of our study. TopHat aligns RNA-seq reads to genome reference (hg19) and finds transcript splice sites. Cufflinks assemble the reads generated from TopHat alignment into transcripts. Cufflinks package consists of the following software - Cufflinks, assembles transcrips; Cuffcompare, compares transcript assemblies to annotation; Cuffdiff, finds differentially expressed genes and transcripts.
Figure 2
Figure 2. Chromosome distribution patterns of differentially expressed genes of radioresistant A549 cells in response to IR.
(x-axis: chromosome coordinate, y-axis: the number of differentially expressed genes of A549 cells under irradiation in 400 kb sliding window).
Figure 3
Figure 3. GOEAST graphical output of enriched GO terms in molecular function ontology for IR-induced transcripts from radioresistant A549 cells.
Each box has GO terms labeled by its GO ID, term definition and detailed information representing ‘q/m|t/k (p-value)’. q is the number of genes associated with the listed GO ID (directly or indirectly) in our data set, m is the number of genes associated with the listed GO ID (directly or indirectly) on the selected platform, k is the total number of genes in our data set, t is the total number of genes on the selected platform, and p-value represent significance of the enrichment in the data set of the listed GO ID with hypergeometric distribution. Branches of the GO hierarchical tree without significantly enriched GO terms are not presented. The degree of color saturation of each box is positively associated with the enrichment significance of the corresponding GO term. Significantly enriched GO terms are indicated in yellow boxes. Insignificant GO terms within the hierarchical tree are shown as white boxes. Arrows show correlations between different GO terms. Red arrows reveal relationships between two enriched GO terms, black solid arrows reveal relationships between enriched and unenriched terms, and black dashed arrows reveal relationships between two unenriched GO terms.
Figure 4
Figure 4. The top three ranked networks identified by IPA from the entire transcripts of IR-induced radioresistant A549 cells.
(A) Network for cell death, post-translational modification, and protein folding (score: 38). (B) Network for cellular development, connective tissue development and function, and cancer (score: 37). (C) Network for cell cycle, cellular development, and hematological system development and function (score: 37). Networks are displayed graphically as nodes (genes/gene products) and edges (biological relationships between the nodes). Intensity of the node color indicates the degree of regulation (red: up-regulation, green: down-regulation, white: not differentially expressed but related to this network).
Figure 5
Figure 5. Accurate validation of our RNA-seq data.
(A) Correlation of differential expression between RNA-seq and real-time RT-PCR. The log2 ratios were generated by comparing expression levels in irradiated to non-irradiated radioresistant A549 cells. (B) Suggestion of gene expression-based putative biomarkers of radioresistance in NSCLC cells. Amplification of the GAPDH fragment in PCR was used as control. The results were confirmed by three independent experiments. Data was presented as mean ± standard deviation (SD) and analyzed using the one-way ANOVA on ranked data, followed by a Tukey's honestly significant difference test and the two-way ANOVA on ranked data, followed by a Bonferroni post test using Prism 4 (GraphPad Software, San Diego, CA). *p-value <0.05; irradiated cells vs. control cells, **p-value <0.01; irradiated cells vs. control cells, ***p-value <0.001; irradiated cells vs. control cells.
Figure 6
Figure 6. Protein expression of candidate genes for radioresistance-associating factors in NSCLC cells.
IR-altered expression of Sestrin2, TRAF4, p21, COX-2 and FDXR was assayed by western blot analysis. The results were confirmed by three independent experiments.
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Grants and funding

This work was supported by Nuclear Research & Development Program (2012-0006383) and by Basic Science Research Program (2012-0003201) through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.