Background/Aims: Recent studies have revealed that many long non-coding RNAs (lncRNAs) play oncogenic or tumor-suppressive roles in various cancers. Lung cancer is the leading cause of cancer-related death worldwide, and many lung cancer patients frequently relapse after surgery, even those in the early stages. However, the oncogenic or tumor-suppressive roles and clinical implications of lncRNAs in lung cancer have not been fully elucidated. Methods: The association between an E2F-mediated cell proliferation enhancing lncRNA (EPEL) expression and lung cancer patient survival was accessed using public microarray data with clinical information. Cancer-related phenotypes were analyzed by the siRNA knockdown of EPEL in two lung cancer cell lines. Gene set analysis of gene expression data were performed to identify pathways regulated by EPEL. RNA immunoprecipitation, RT-qPCR, and ChIP assays were performed to explore the functions of selected target genes regulated by EPEL. Results: EPEL, known as LOC90768 and MGC45800, was associated with the relapse and survival of lung cancer patients and promoted lung cancer cell proliferation through the activation of E2F target genes. EPEL knockdown specifically down-regulated the expression of cell cycle-related E2F target genes, including Cyclin B1 (CCNB1), in lung cancer cells but not that of apoptosis- or metabolism-related E2F target genes. EPEL interacted with E2F1 and regulated the expression of the E2F target genes by changing the binding efficiency of E2F1 to the E2F target promoters. Moreover, the expression levels of EPEL and CCNB1 both alone and in combination were robust prognostic markers for lung cancer. Conclusions: Considering its specific effects on cell cycle-related E2F target genes and its significant association with the prognosis of lung cancer patients, we suggest that the transcriptional regulation of EPEL through E2F target genes is potentially a target for the development of novel therapeutic strategies for lung cancer patients.

Recent genome-wide studies have identified thousands of long noncoding RNAs (lncRNAs), RNAs longer than 200 nucleotides without open reading frames, that were determined to be associated with various cellular processes, such as pluripotency, proliferation, development, differentiation and apoptosis [1-9]. Additionally, many differentially expressed lncRNAs have been characterized in various cancer types with oncogenic or tumor-suppressive roles [10-16], with some lncRNAs showing potential as diagnostic or prognostic markers for cancer patients [12, 13, 15-18]. By interacting with chromatin remodelers [12, 14] and transcription factors, such as p53 [19, 20] and E2F [21, 22], many lncRNAs mediate target gene transcription and regulate cancer-related functions, such as cell cycle progression, apoptosis, metastasis and senescence. For cancer progression, lncRNAs often play roles as cell cycle regulators in cyclin-dependent [23-25] or p53-dependent manners [19, 20]. Thus, the characterization of cancer-related lncRNAs is important for the study of cancer biology.

The E2F/Rb pathway is a major pathway regulating the mammalian cell cycle [26, 27], and E2Fs are a large family of transcription factors that play various biological roles, including cell cycle control [28, 29]. In a growth control model of E2F1, E2F1 mediates various functions, and even opposing functions, such as cell cycle progression and apoptosis, depending on its binding partners [26, 27, 30]. For cell cycle progression, E2F1 regulates the G1/S [31], S/G2 [32] and G2/M [33] transitions depending on its binding partners. For example, two lncRNAs, TUG1 and MALAT1/NEAT2, regulate the growth control activity of E2F1 by mediating E2F1 SUMOylation [22]. E2F target genes revealed by genome-wide-studies [33-38] imply that lncRNAs can regulate E2F target gene expression and various cancer-related functions, including the cell cycle.

Lung cancer is the leading cause of cancer-related death worldwide, and lung cancer patients frequently relapse after surgery, even those in the early stages [39-41]. Because understanding lung cancer prognosis is important for developing therapeutic strategies, such as post-operative adjuvant therapy, histological markers have been used for this purpose, but randomized studies for prognosis determination have shown controversial effectiveness [42-45]. Recently, several lncRNAs were reported as potential prognostic markers for lung cancer [46-48]. Thus, studying lncRNAs is a promising way to identify novel molecular markers of lung cancer prognosis and novel therapeutic targets.

In this study, we characterized a novel lncRNA, herein named E2F-mediated cell proliferation enhancing lncRNA (EPEL), that promotes lung cancer cell proliferation through E2F target gene activation. Because EPEL over-expression was significantly associated with poor lung cancer prognosis, we suggest its feasibility as a promising prognostic marker for this disease.

Public data analysis

Two public gene expression datasets (GSE31210 and GSE50081) were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database (http://www.ncbi.nlm.nih.gov/geo/). From the GSE50081 dataset, we used only data for adenocarcinoma patients, and gene expression data were globally normalized using the Robust Multi-array Average (RMA) method [49]. All statistical tests were performed using the R programming language (https://www.r-project.org/), and graphs and heatmaps were prepared using the Excel, R and MeV (http://www.tm4.org/mev.html) programs.

Cell culture, siRNAs and transfection

A549 and NCI-H1299 cells (ATCC) were maintained in complete RPMI 1640 medium (HyClone) at 37°C in a humidified 5% CO2 incubator. Complete media was supplemented with 10% fetal bovine serum (HyClone), 100 U/ml penicillin/streptomycin (WelGENE), and 2 mM L-glutamine (HyClone). The siRNA target sequences were designed using the AsiDesigner program [50] and are listed (for all online suppl. material, see www.karger.com/doi/10.1159/000487460) in Suppl. Table 1. For siRNA transfection, 1.5 ~ 2.0 x 105 lung cancer cells were plated in 6-well plates and incubated overnight. The siRNAs or non-targeting controls at a concentration of 50 nM were transfected into lung cancer cells using Lipofectamine 2000 in Opti-MEM media. After 4 hours of incubation, the media were changed to complete media, and 48 hours after transfection, gene knockdown was confirmed by RT-qPCR.

Reverse transcription qPCR

Total RNA was extracted using the RNeasy Mini Kit (QIAGEN) according to the manufacturer’s instructions. Reverse transcription was performed with 1 μg of total RNA as the template and M-MLV Reverse Transcriptase (Promega). Quantitative realtime PCR (qPCR) reactions were performed in triplicate on a LightCycler 480 machine (Roche) using LightCycler 480 SYBR Green I Master Mix (Roche). cDNA expression was normalized to that of β-actin, and at least three independent biological replicates were included for each reaction. The primers used for qPCR were designed either manually or with the Primer3 program (http://biotools.umassmed.edu/bioapps/primer3_www.cgi). All primer sequences are listed (see suppl. material) in Suppl. Table 2.

Proliferation assay

Suspensions of 1.0 to 2.0 x 103 cells were seeded into 96-well plates. After 48 to 72 hours of incubation at 37°C, a CyQUANT NF Cell Proliferation dye reagent and deliverer (Invitrogen) mixture was added. After 30 min of incubation, the fluorescence intensity ratio of 530 nm to 485 nm was measured.

Invasion assay

Transwell chambers (Corning) were coated with Matrigel Basement Membrane Matrix (BD). Cells were suspended in serum-free media and seeded into the upper chamber at a density of 2.0 to 5.0 x 104 cells per well, and serum-containing media were placed into the lower chamber. After incubation for 24 ∼ 72 hours, cells penetrating the pores were stained with Diff-Quik staining solution (Sysmex) and observed under a microscope.

Microarray analysis

In total, 750 ng of each cRNA library was prepared from the total RNA samples, hybridized to the HumanHT-12 Gene Expression BeadChip (Illumina), and measured according to the manufacturer’s instructions. The intensity values were analyzed with the GenomeStudio program and globally normalized using the quantile method. Gene set and pathway analyses of differentially expressed genes were performed using the Gene Set Enrichment Analysis (GSEA) program (http://software.broadinstitute.org/gsea/index. jsp).

RNA immunoprecipitation assay

RNA immunoprecipitation (RIP) was performed with 5.0 x 106 cells using the Magna RIP kit (Millipore) according to the manufacturer’s instructions. Primary antibodies used for the RIP assay are listed (see suppl. material) in Suppl. Table 3. RIP-qPCR amplifications were performed in triplicate on the LightCycler 480 machine using LightCycler 480 SYBR Green I Master Mix. Data were normalized to the input levels, and at least three independent biological replicates were included for each RIP-qPCR. The primer sequences are listed (see suppl. material) in Suppl. Table 2.

Nucleus/cytoplasm fractionation

Nuclear and cytosolic fractions were separated using the PARIS kit (Ambion) according to the manufacturer’s instructions

Chromatin immunoprecipitation assay

Chromatin immunoprecipitation (ChIP) was performed using Dynabeads Protein A and G (Thermo Fisher Scientific). First, 5.0 x 106 cells were cross-linked with 1% formaldehyde for 10 min at 25°C. Then, the cells were lysed and sonicated using the truChIP Chromatin Shearing Reagent Kit (Covaris) according to the manufacturer’s instructions. The expected fragment size was between approximately 200 and 500 base pairs. Samples were diluted five-fold with low-salt RIPA buffer (0.1% SDS, 1% Triton X-100, 1 mM EDTA, 140 mM NaCl, 4% deoxycholate) and pre-cleared with 50 µl of Dynabeads Protein A and G for 1 hour at 4°C. Primary antibodies were added to pre-cleared supernatants, and the mixtures were incubated overnight at 4°C. The antibodies used for the ChIP assay are listed (see suppl. material) in Suppl. Table 3. Next, 50 µl of Dynabeads Protein A and G were added to the samples, and the mixtures were incubated for 2 hours at 4°C. The beads were subsequently washed with wash buffer (low-salt RIPA, high-salt RIPA, LiCl, and TE). After 15 min of incubation at 65°C, precipitated chromatin was eluted twice in 250 µl of elution buffer (0.1 M NaHCO3 and 1% SDS). Reverse cross-linking was performed for 4 hours at 65°C, and chromatin was then treated with RNase A for 1 hour at 37°C and proteinase K for 1 hour at 45°C. DNA was purified with phenol/ chloroform extraction or a QIAquick PCR Purification Kit (QIAGEN).

ChIP-qPCR reactions were performed in triplicate on the LightCycler 480 machine using LightCycler 480 SYBR Green I Master Mix. The data were normalized to the input levels, and at least three independent biological replicates were included for each ChIP-qPCR. The primer sequences are listed (see suppl. material) in Supp. Table 2.

Data availability

Microarray data have been deposited in the GEO database under accession number GSE102356.

Promotion of lung cancer progression by EPEL

The human chromosome 4 (NCBI37/hg19) genomic locus from 183, 059, 813 to 183, 065, 668 is annotated as LOC90768 or MGC45800 and encodes a not yet characterized lncRNA that we named EPEL. According to Encyclopedia of DNA Elements (ENCODE) data, the locus is occupied by active histone markers, and EPEL is transcribed in various cell lines (GM12878, H1-hESC, HeLa-S3, HepG2, HSMM, HUVEC, K562, NHEK, NHLF cell lines) (see suppl. material, Suppl. Fig. 1). EPEL expression was higher in tumor lung tissues (lung-C) than in normal lung tissues (lung-N) according to the Gene Expression across Normal and Tumor tissue (GENT) database (see suppl. material, Suppl. Fig. 2) [51]. We analyzed two public gene expression datasets (GSE31210 and GSE50081) to evaluate the clinical significance of EPEL in lung cancer patients [52, 53]. We first compared EPEL expression between non-relapsed (or non-recurrent) and relapsed (or recurrent) patients and found that EPEL was up-regulated among lung cancer patients with relapse or recurrence (Fig. 1a). Increased EPEL expression was associated with poor survival after surgery (Fig. 1b). By analyzing additional public datasets, we found that increased EPEL expression was also associated with the poor survival of other cancer patients, including breast cancer, Ewing’s sarcoma and melanoma (see suppl. material, Suppl. Fig. 3). Thus, we suggest that the level of EPEL expression is associated with cancer progression. Using multivariate Cox proportional hazard analysis (Table 1), we found that only EPEL expression and histological staging could predict poor survival in both datasets. Thus, we suggest that EPEL expression is a good prognostic marker for lung cancer.

Table 1.

Multivariate Cox proportional hazard analysis for the prediction of lung adenocarcinoma patient survival

Multivariate Cox proportional hazard analysis for the prediction of lung adenocarcinoma patient survival
Multivariate Cox proportional hazard analysis for the prediction of lung adenocarcinoma patient survival
Fig. 1.

Promotion of lung cancer progression by EPEL. a. Comparison of EPEL expression in relapsed (or recurrent) and non-relapsed (or non-recurrent) lung cancer patients (GSE31210: Okayama et al. 2012, n = 204, p = 1.0 x 10-6, GSE50081 adenocarcinoma: Der et al. 2014, n = 128, p = 1.4 x 10-2, t-test). b. Prognosis of two groups of lung cancer patients classified by EPEL expression (GSE31210: Okayama et al. 2012, n = 204, p = 4.5 x 10-3 for overall survival, p = 2.4 x 10-3 for relapse-free survival, GSE50081 adenocarcinoma: Der et al. 2014, n = 128, p = 9.6 x 10-4 for overall survival, p = 3.8 x 10-4 for disease-free survival, log-rank test). Red: high expression group; Blue: low expression group.

Fig. 1.

Promotion of lung cancer progression by EPEL. a. Comparison of EPEL expression in relapsed (or recurrent) and non-relapsed (or non-recurrent) lung cancer patients (GSE31210: Okayama et al. 2012, n = 204, p = 1.0 x 10-6, GSE50081 adenocarcinoma: Der et al. 2014, n = 128, p = 1.4 x 10-2, t-test). b. Prognosis of two groups of lung cancer patients classified by EPEL expression (GSE31210: Okayama et al. 2012, n = 204, p = 4.5 x 10-3 for overall survival, p = 2.4 x 10-3 for relapse-free survival, GSE50081 adenocarcinoma: Der et al. 2014, n = 128, p = 9.6 x 10-4 for overall survival, p = 3.8 x 10-4 for disease-free survival, log-rank test). Red: high expression group; Blue: low expression group.

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Promotion of lung cancer cell proliferation and invasion by EPEL

As the level of EPEL expression was associated with lung cancer progression, we examined which cancer-related phenotypes were influenced by EPEL expression in lung cancer cells. Using siRNAs for EPEL, we knocked down EPEL in A549 and NCI-H1299 lung cancer cell lines. We first validated that EPEL was successfully knocked down by three siRNAs in both A549 and NCI-H1299 lung cancer cells using the RT-qPCR assay (Fig. 2a) and then performed cell proliferation assays. EPEL knockdown significantly decreased the proliferation of A549 and NCI-H1299 cells (A549: 62% decrease for siEPEL #1, 26% decrease for siEPEL #2, 31% decrease for siEPEL #3; NCI-H1299: 31% decrease for siEPEL #1, 41% decrease for siEPEL #2, 46% decrease for siEPEL #3). We next performed an invasion assay with cells transfected with treatment siRNAs and a control siRNA. EPEL knockdown significantly decreased the invasion of A549 and NCI-H1299 cells (Fig. 2c) (A549: 65% decrease for siEPEL #1, 59% decrease for siEPEL #2, 52% decrease for siEPEL #3; NCI-H1299: 53% decrease for siEPEL #1, 44% decrease for siEPEL #2, 58% decrease for siEPEL #3). Thus, we concluded that EPEL promoted lung cancer proliferation and invasion, which is important for lung cancer progression.

Fig. 2.

Regulation of lung cancer cell invasion and proliferation by EPEL. After EPEL knockdown: a. RT-qPCR (A549: p = 2.7 x 10-3 for siEPEL #1, p = 3.8 x 10-3 for siEPEL #2, p = 2.7 x 10-2 for siEPEL #3; NCI-H1299: p = 5.9 x 10-3 for siEPEL #1, p = 5.7 x 10-4 for siEPEL #2, p = 2.1 x 10-4 for siEPEL #3, t-test). b. Proliferation assay (A549: p = 4.0 x 10-5 for siEPEL #1, p = 7.7 x 10-4 for siEPEL #2, p = 6.4 x 10-4 for siEPEL #3; NCI-H1299: p = 1.4 x 10-2 for siEPEL #1, p = 3.4 x 10-3 for siEPEL #2, p = 1.6 x 10-3 for siEPEL #3, t-test). c. Invasion assay (A549: p = 2.2 x 10-4 for siEPEL #1, p = 1.9 x 10-2 for siEPEL #2, p = 3.8 x 10-4 for siEPEL #3; NCI-H1299: p = 3.4 x 10-4 for siEPEL #1, p = 2.5 x 10-5 for siEPEL #2, p = 3.0 x 10-4 for siEPEL #3, t-test). Data are representative of three independent experiments. The error bars represent the standard error of the mean (s.e.m). *p<0.05, **p<0.01, ***p<0.001, t-test. Scale bar, 200 μm.

Fig. 2.

Regulation of lung cancer cell invasion and proliferation by EPEL. After EPEL knockdown: a. RT-qPCR (A549: p = 2.7 x 10-3 for siEPEL #1, p = 3.8 x 10-3 for siEPEL #2, p = 2.7 x 10-2 for siEPEL #3; NCI-H1299: p = 5.9 x 10-3 for siEPEL #1, p = 5.7 x 10-4 for siEPEL #2, p = 2.1 x 10-4 for siEPEL #3, t-test). b. Proliferation assay (A549: p = 4.0 x 10-5 for siEPEL #1, p = 7.7 x 10-4 for siEPEL #2, p = 6.4 x 10-4 for siEPEL #3; NCI-H1299: p = 1.4 x 10-2 for siEPEL #1, p = 3.4 x 10-3 for siEPEL #2, p = 1.6 x 10-3 for siEPEL #3, t-test). c. Invasion assay (A549: p = 2.2 x 10-4 for siEPEL #1, p = 1.9 x 10-2 for siEPEL #2, p = 3.8 x 10-4 for siEPEL #3; NCI-H1299: p = 3.4 x 10-4 for siEPEL #1, p = 2.5 x 10-5 for siEPEL #2, p = 3.0 x 10-4 for siEPEL #3, t-test). Data are representative of three independent experiments. The error bars represent the standard error of the mean (s.e.m). *p<0.05, **p<0.01, ***p<0.001, t-test. Scale bar, 200 μm.

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Regulation of E2F target and cell cycle-related genes by EPEL

To identify genes regulated by EPEL, we performed microarray-based gene expression analysis after treating A549 lung cancer cells with either EPEL or control siRNAs. To identify functional gene categories influenced by EPEL knockdown, we performed GSEA analysis using the microarray data. By GSEA analysis with the Hallmark gene set, we found five gene sets to be significantly down-regulated by EPEL knockdown, including two cell cycle-related gene sets (G2M_CHECKPOINT and MITOTIC_SPINDLE) and one E2F-related gene set (E2F_TARGETS) (FWER p < 0.01) (Fig. 3a and see suppl. material, Suppl. Table 4). Then, we performed GSEA analysis with Transcription Factor Target (TFT) gene sets, and among the top 20 TFT gene sets, the top 18 were E2F target-related (see suppl. material, Suppl. Table 5). Moreover, all TFT gene sets significantly down-regulated by EPEL knockdown were also E2F target-related gene sets (FWER p < 0.01) (Fig. 3b). Thus, the results implied the significance of E2F target regulation by EPEL in lung cancer.

Fig. 3.

Microarray-based global analysis for transcriptional regulation via EPEL. a. GSEA analysis with the Hallmarks gene set (FWER p<0.01). b. GSEA analysis with the TFT gene set (FWER p<0.01). c. Expression patterns of well-known E2F target genes (Poppy Roworth et al. [30]).

Fig. 3.

Microarray-based global analysis for transcriptional regulation via EPEL. a. GSEA analysis with the Hallmarks gene set (FWER p<0.01). b. GSEA analysis with the TFT gene set (FWER p<0.01). c. Expression patterns of well-known E2F target genes (Poppy Roworth et al. [30]).

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E2F promotes various cancer-related functions, such as cell cycle progression and metabolism, while also promoting opposite cancer-related functions, such as apoptosis, according to its binding partners [30, 54]. Roworth et al. reported that each E2F function (cell cycle progression, metabolism, or apoptosis) is associated with its corresponding target genes [30]. When we examined the expression patterns of E2F target genes in our microarray data categorized by cancer-related functions, only cell cycle-related E2F target genes were down-regulated by EPEL knockdown, while metabolism-related and apoptosis-related E2F target genes did not change consistently (Fig. 3c). Thus, we concluded that EPEL specifically regulated the cell cycle through E2F target gene regulation.

Transcriptional regulation of the E2F1 target genes by EPEL

According to GSEA analysis of our microarray data, a set of cell cycle-related E2F1 target genes was the most down-regulated by EPEL knockdown (Fig. 3b and see suppl. material, Suppl. Table 5). We hypothesized three possibilities for E2F1 target gene regulation by EPEL: 1) transcriptional regulation of E2F1 by EPEL, 2) post-transcriptional or translational regulation of E2F1, and 3) regulation of E2F1 activity by direct interaction between EPEL and E2F1. The first hypothesis was rejected, as E2F1 expression was not down-regulated in our EPEL knockdown microarray data (Fig. 3c). For the second hypothesis, we performed an immunoblot assay with an E2F1-specific antibody after EPEL knockdown in A549 lung cancer cells (see suppl. material, Suppl. Fig. 4). However, because the protein level of E2F1 was not changed significantly by EPEL knockdown, the second hypothesis was also rejected. For the third hypothesis, we first tested direct interaction between EPEL and E2F1 using RIP and qPCR assays with an E2F1-specific antibody and an EPEL-specific primer and found that EPEL directly interacted with the E2F1 transcription factor (p = 1.0 x 10-4, t-test) (Fig. 4a). Because EPEL should be located in the nucleus for direct interaction with the E2F1 transcription factor, we separated the nuclear and cytoplasmic fractions of A549 cells and performed RT-qPCR assays using primers specific for EPEL, RNA-U1 (nucleus-specific RNA) and GAPDH (cytoplasm-specific RNA) to identify the subcellular localization of EPEL (Fig. 4b). EPEL was mostly detected in the nuclei of A549 cells, and we thus concluded that EPEL directly interacted with the E2F1 transcription factor in the nucleus.

Fig. 4.

Transcriptional regulation of an E2F1 target gene by EPEL. a. RIP assay between EPEL and E2F1 (p = 1.0 x 10-4, t-test). b. Subcellular localization of EPEL, RNA-U1 (nucleus-specific RNA) and GAPDH (cytoplasm-specific RNA). c. ChIP-qPCR assay with an E2F1 antibody on the E2F1 target promoters after EPEL knockdown (p = 0.0021 for CCNB1, p = 0.0023 for FEN1, p = 0.016 for TOP2A, t-test). d. qPCR assay with a pol II CTD (phospho-S5) antibody on the E2F1 target promoters after EPEL knockdown (p = 5.3 x 10-5 for CCNB1, p = 0.0062 for FEN1, p = 0.0012 for TOP2A, t-test). e. RT-qPCR assay for the E2F1 target genes after EPEL knockdown (p = 0.0018 for CCNB1, p = 0.0024 for FEN1, p = 0.0023 for TOP2A, t-test). Data are representative of three independent experiments. The error bars represent the s.e.m. *p<0.05, **p<0.01, ***p<0.001, t-test.

Fig. 4.

Transcriptional regulation of an E2F1 target gene by EPEL. a. RIP assay between EPEL and E2F1 (p = 1.0 x 10-4, t-test). b. Subcellular localization of EPEL, RNA-U1 (nucleus-specific RNA) and GAPDH (cytoplasm-specific RNA). c. ChIP-qPCR assay with an E2F1 antibody on the E2F1 target promoters after EPEL knockdown (p = 0.0021 for CCNB1, p = 0.0023 for FEN1, p = 0.016 for TOP2A, t-test). d. qPCR assay with a pol II CTD (phospho-S5) antibody on the E2F1 target promoters after EPEL knockdown (p = 5.3 x 10-5 for CCNB1, p = 0.0062 for FEN1, p = 0.0012 for TOP2A, t-test). e. RT-qPCR assay for the E2F1 target genes after EPEL knockdown (p = 0.0018 for CCNB1, p = 0.0024 for FEN1, p = 0.0023 for TOP2A, t-test). Data are representative of three independent experiments. The error bars represent the s.e.m. *p<0.05, **p<0.01, ***p<0.001, t-test.

Close modal

To examine the target genes of EPEL transcriptional regulation through E2F1 binding, we selected CCNB1 (cyclin B1), FEN1 (Flap endonuclease 1) and TOP2A (DNA topoisomerase 2-alpha) as putative targets [30, 55] (Fig. 3c). We examined whether EPEL regulated the binding of E2F1 to its target genes. Using ChIP-qPCR with an E2F1 antibody after EPEL knockdown in A549 cells, we found that EPEL knockdown decreased E2F1 occupancy in the E2F1 target promoter regions, including the transcription start site (TSS) (Fig. 4c). To examine the effect of EPEL knockdown on the TSS transcriptional machinery, we performed ChIP-qPCR with an RNA polymerase II (pol II) carboxy-terminal domain (CTD) phospho-serine 5 (p-S5) antibody after EPEL knockdown in A549 cells. EPEL knockdown decreased the occupancy of the active form of pol II (pol II CTD p-S5) in the promoter regions, including the TSS (Fig. 4d). Then, we performed an RT-qPCR assay after EPEL knockdown in A549 cells to validate the regulation of the expression of E2F1 target genes by EPEL (Fig. 4e). Consistent with our microarray data, the expression was down-regulated by EPEL knockdown, and we thus concluded that EPEL affects the proliferation of lung cancer cells by regulating the transcription of E2F1 target genes including CCNB1 through interaction with the E2F1 transcription factor.

Clinical implications of CCNB1 transcriptional regulation by EPEL

Previously, we showed that high EPEL expression levels were significantly associated with the poor prognosis of lung cancer patients (Fig. 1b). Using the same dataset, we analyzed the clinical relevance of CCNB1 transcriptional regulation by EPEL (Fig. 5a). CCNB1 expression was significantly associated with the prognosis of lung adenocarcinoma patients in both datasets (Fig. 5a; 3.7 x 10-4 for the overall survival of GSE31210 (n = 204), p = 1.2 x 10-4 for the relapse-free survival of GSE31210 (n = 204), 5.9 x 10-4 for the overall survival of GSE50081 (n = 128), p = 3.5 x 10-4 for the disease-free survival of GSE50081 (n = 128), log-rank test). We also examined the effect of combined EPEL and CCNB1 expression on the prognosis of lung cancer patients. We divided patients into four groups according to their EPEL and CCNB1 expression levels and performed survival analyses (Fig. 5b). Patients in the high EPEL and high CCNB1 expression groups exhibited the worst prognosis (3.7 x 10-4 for the overall survival of GSE31210 (n = 204), p = 1.2 x 10-4 for the relapse-free survival of GSE31210 (n = 204), 5.9 x 10-4 for the overall survival of GSE50081 (n = 128), p = 3.5 x 10-4 for the disease-free survival of GSE50081 (n = 128), stratified analysis), implying that increased CCNB1 expression induced by EPEL may play a critical role in lung cancer progression. We also examined the effect of combined EPEL and other E2F1 target gene expression on the prognosis of lung cancer patients (see suppl. material, Suppl. Fig. 5). Thus, we suggest that the transcriptional regulation of CCNB1 by EPEL is an important mechanism for lung cancer progression.

Fig. 5.

Promotion of lung cancer by EPEL and CCNB1 in combination. a. Prognosis of two groups of lung cancer patients classified by CCNB1 expression. (2.0 x 10-4 for the overall survival of GSE31210 (n = 204), p = 2.0 x 10-5 for the relapse-free survival of GSE31210 (n = 204), 5.5 x 10-5 for the overall survival of GSE50081 (n = 128), p = 2.1 x 10-5 for the disease-free survival of GSE50081 (n = 128), log-rank test) (red: high expression group, blue: low expression group). b. Prognosis of lung adenocarcinoma patient groups stratified by EPEL and CCNB1 expression (3.7 x 10-4 for the overall survival of GSE31210 (n = 204), p = 1.2 x 10-4 for the relapse-free survival of GSE31210 (n = 204), 5.9 x 10-4 for the overall survival of GSE50081 (n = 128), p = 3.5 x 10-4 for the disease-free survival of GSE50081 (n = 128), stratified analysis) (blue: low EPEL, low CCNB1; orange: low EPEL, high CCNB1; purple: high EPEL, low CCNB1; red: high EPEL, high CCNB1).

Fig. 5.

Promotion of lung cancer by EPEL and CCNB1 in combination. a. Prognosis of two groups of lung cancer patients classified by CCNB1 expression. (2.0 x 10-4 for the overall survival of GSE31210 (n = 204), p = 2.0 x 10-5 for the relapse-free survival of GSE31210 (n = 204), 5.5 x 10-5 for the overall survival of GSE50081 (n = 128), p = 2.1 x 10-5 for the disease-free survival of GSE50081 (n = 128), log-rank test) (red: high expression group, blue: low expression group). b. Prognosis of lung adenocarcinoma patient groups stratified by EPEL and CCNB1 expression (3.7 x 10-4 for the overall survival of GSE31210 (n = 204), p = 1.2 x 10-4 for the relapse-free survival of GSE31210 (n = 204), 5.9 x 10-4 for the overall survival of GSE50081 (n = 128), p = 3.5 x 10-4 for the disease-free survival of GSE50081 (n = 128), stratified analysis) (blue: low EPEL, low CCNB1; orange: low EPEL, high CCNB1; purple: high EPEL, low CCNB1; red: high EPEL, high CCNB1).

Close modal

While recent high-throughput genomics tools, including microarray and RNA-seq, have allowed researchers to identify thousands of novel disease-associated lncRNAs, detailed functional characterization has been limited to only a small portion of those identified. Here, we report the functional characterization of a new lncRNA, EPEL, as a promising prognostic marker in lung cancer.

We first analyzed several public microarray datasets, identified many novel lung cancer-associated lncRNAs and selected EPEL as a good candidate for subsequent analysis for the following reasons: EPEL was significantly associated with the relapse and survival of lung cancer patients, was transcribed in various cell lines according to ENCODE data, was stabilized by the poly(A) tail and was not genomic DNA contamination, as it was composed of four exons. Then, we characterized EPEL, including analysis of its direct interacting proteins, its subcellular localization, and the transcriptional regulation of its target genes in lung cancer.

E2F1 is a multi-functional transcription factor that interacts with various proteins and is regulated by multiple mechanisms, including the phosphorylation of its binding partner Rb, the SUMOylation of E2F1, and its binding with lncRNAs [22, 26, 27, 30]. Interestingly, several lncRNAs, such as NEAT2, TUG1 and GASL1, were recently identified as E2F1 regulators [22, 56], and we add EPEL as another lncRNA that directly interacts with E2F1 in lung cancer. As we showed, EPEL interacted with E2F1 and regulated the transcription of E2F1 target genes, including CCNB1 in lung cancer cells. Many lncRNAs are known to be expressed and function in a tissue-specific manner [1, 3, 47, 57-60]. By surveying previous studies, we found eight lncRNAs that directly interact with E2F1 transcription factors. Among them, four (GASL1 [56], lncRNA-HIT [61], GAS5 [62, 63] and ANRIL [64]), interacted with E2F1 in lung tissues or cells, while the other factors, including Khps1 [58], ERIC [59], TUG1 [22] and NEAT2 [22], had no known functions in lung tissues or cells.

The expression level of EPEL was higher in tumor tissues than normal tissues and was associated with not only the prognosis of lung cancer but also that of other cancer types (tumor-normal difference: skin, bladder, blood, colon, esophagus, ovary, pancreas; prognosis: melanoma, breast cancer, Ewing’s sarcoma) (see suppl. material, Suppl. Fig. 2 and 3). While we focused on the lung-specific function of EPEL in this study, we expect that EPEL may interact with E2F1 and exert other specific functions in other cancers based on the tissue-specific patterns of EPEL expression in other cancers.

Due to the lack of catalytic activity, lncRNAs play mostly structural roles, including acting as decoys, scaffolds, guides or enhancers [6, 9, 14]. E2F1 has two opposite roles in cancer cell growth: cell cycle control and apoptosis [30]. By changing its lncRNA partners, E2F1 can play opposite roles in cell growth by transcribing different sets of specific target genes. For example, the E2F1-GAS5 interaction inhibits cellular proliferation by targeting P27Kip1 [63], while the E2F1-lncRNA-HIT interaction promotes cellular proliferation by targeting Survivin, FOXM1, SKP2, NELL2 and DOK1 [61]. Similar to other lncRNAs, EPEL does not have catalytic activity but fine-tunes the transcriptional regulation of E2F1 by determining the specificity of E2F1 target genes. According to our results, EPEL had specific effects on the expression of cell growth-related E2F1 target genes, including CCNB1, in lung cancer, but not E2F1 target genes related to apoptosis and metabolism, implying that EPEL regulates the specific function of E2F1 by managing the selection of E2F1 target genes.

CCNB1 is a key regulator of cell cycle and proliferation, and we demonstrated that EPEL directly regulated CCNB1 transcription by regulating the E2F1 binding efficiency on the promoter. Both EPEL and CCNB1 had potential as prognostic markers for lung cancer by themselves and highly effective at identifying patients with poor prognoses when expressed in combination, implying that the dysregulated transcription of CCNB1 by E2F1 controlled by EPEL is an essential mechanism for lung cancer tumorigenesis and progression.

Considering that the combination of EPEL and E2F1 target gene expression is closely associated with lung cancer prognosis and that EPEL specifically affects the cell cycle-related target genes of E2F, targeting the interplay between EPEL and E2F1 would be a more precise method than targeting E2F1 itself. Thus, we suggest that the interplay between EPEL and E2F1 has translational therapeutic potential.

This work was supported by grants from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037583 to S.-M.P., NRF-2017M3A9F9030565 to S.-Y.K. and NRF-2015R1C1A1A02036803 to Y.-J.K.) and the National Cancer Center Grant (NCC-1710260 to Y.-J.K.).We thank Jin Young Min for technical assistance.

No conflicts of interest to disclose.

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S.-M. Park and E.-Y. Choi contributed equally to this work.

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