Purpose: Global DNA hypomethylation plays a crucial role in genomic instability and carcinogenesis. DNA methylation of the long interspersed nucleotide element-1, L1 (LINE-1) repetitive element is a good indicator of the global DNA methylation level, and is attracting interest as a useful marker for predicting cancer prognosis. Our previous study using more than 200 esophageal squamous cell carcinoma (ESCC) specimens demonstrated the significant relationship between LINE-1 hypomethylation and poor prognosis. However, the mechanism by which LINE-1 hypomethylation affects aggressive tumor behavior has yet to be revealed.

Experimental Design: To examine the relationship between LINE-1 hypomethylation and DNA copy number variations, we investigated LINE-1–hypomethylated and LINE-1–hypermethylated ESCC tumors by comparative genomic hybridization array.

Results: LINE-1–hypomethylated tumors showed highly frequent genomic gains at various loci containing candidate oncogenes such as CDK6. LINE-1 methylation levels were significantly associated with CDK6 mRNA and CDK6 protein expression levels in ESCC specimens. In our cohort of 129 patients with ESCC, cases with CDK6-positive expression experienced worse clinical outcome compared with those with CDK6-negative expression, supporting the oncogenic role of CDK6 in ESCC. In addition, we found that the prognostic impact of LINE-1 hypomethylation might be attenuated by CDK6 expression.

Conclusion: LINE-1 hypomethylation (i.e., global DNA hypomethylation) in ESCC might contribute to the acquisition of aggressive tumor behavior through genomic gains of oncogenes such as CDK6. Clin Cancer Res; 20(5); 1114–24. ©2014 AACR.

Translational Relevance

The level of long interspersed nucleotide element-1 (LINE-1) methylation is regarded to be a surrogate marker of global DNA methylation. We have previously demonstrated that LINE-1 hypomethylation in esophageal squamous cell carcinoma (ESCC) is associated with a shorter survival, thus suggesting that it has potential for use as a prognostic biomarker. However, the mechanism by which LINE-1 hypomethylation affects aggressive tumor behavior has yet to be revealed. In this study, comparative genomic hybridization array revealed that LINE-1–hypomethylated ESCC tumors presented highly frequent genomic gains at various loci containing CDK6. In addition, LINE-1 methylation levels were associated with CDK6 expression in ESCCs and that the prognostic impact of LINE-1 hypomethylation in patients with ESCC was attenuated by CDK6 expression. Collectively, these findings may suggest that global DNA hypomethylation in ESCC might contribute to the acquisition of aggressive tumor behavior through genomic gains of oncogenes such as CDK6.

Esophageal squamous cell carcinoma (ESCC), the major histologic type of esophageal cancer in East Asian countries, is one of the most aggressive malignant tumors (1). Despite remarkable advances in multimodal therapies, including surgery, chemotherapy, radiotherapy, and chemoradiotherapy, the prognosis of patients remains poor, even for those whose carcinomas have been completely resected (2–4). To develop innovative strategies for treating ESCC, especially those that are molecularly targeted (5), it is of particular importance to increase our understanding of the cellular and molecular basis of this disease. Importantly, epigenetic changes, including alterations in DNA methylation, are reversible, and can thus be targets for cancer therapy or chemoprevention (6–8).

DNA methylation alterations occurring in human cancers include global DNA hypomethylation and site-specific CpG island promoter hypermethylation (9). Global DNA hypomethylation seems to play a crucial role in genomic instability, leading to cancer development (10–12). Because long interspersed nucleotide element-1 (LINE-1; L1 retrotransposon) constitutes a substantial portion (approximately 17%) of the human genome, the level of LINE-1 methylation is regarded to be a surrogate marker of global DNA methylation (13). In many types of human neoplasms, LINE-1 methylation has been shown to be highly variable (14–16), and LINE-1 hypomethylation is strongly associated with a poor prognosis (17–22). In a previous study of 217 curatively resected ESCC specimens (23), we demonstrated that LINE-1 hypomethylation is associated with a poor prognosis, suggesting that LINE-1 hypomethylation may be a biomarker that can be used to identify patients who will experience an inferior outcome. However, the mechanism by which LINE-1 hypomethylation (and thus global DNA hypomethylation) may confer a poor prognosis remains to be fully explored. Given the known relationship between genome-wide DNA hypomethylation and genomic instability (24–27), we hypothesized that global DNA hypomethylation might be an important influential factor for DNA copy number variations, leading to altered expression levels of oncogenes or tumor suppressor genes.

To test this hypothesis, we investigated LINE-1–hypomethylated and LINE-1–hypermethylated ESCC tumors using array-based comparative genomic hybridization (CGH array). LINE-1–hypomethylated tumors showed highly frequent genomic gains at various loci containing candidate oncogenes such as CDK6. Thus, we further examined the correlation between LINE-1 methylation levels, CDK6 amplification, CDK6 protein and mRNA expression, and clinical outcome.

Study subjects

All samples used in this study were collected at Kumamoto University Hospital (Kumamoto, Japan) between April 2005 and December 2011. LINE-1 methylation levels were quantified in 40 ESCC tumors for which freshly frozen specimens were available. Total RNA was obtained from 20 ESCC tumors, matched with 15 macroscopically normal esophageal tissues from the same patients. To assess the prognostic impact of CDK6 expression, we performed immunohistochemical staining of 129 ESCC samples. No patient received any preoperative treatment (chemotherapy, radiation therapy, or chemoradiotherapy). All 129 patients underwent curative resection. Tumor staging was performed according to the American Joint Committee on Cancer Staging Manual (seventh edition; ref. 28). Patients were observed at 1- to 3-month intervals until death or until March 31, 2013, whichever came first. Disease-free survival was defined as the period following surgical cancer treatment during which the patient survived with no sign of cancer recurrence. Among the 129 patients, 48 experienced disease recurrence during the follow-up period. This current analysis represents a new analysis of CDK6 on the existing ESCC database that has been previously characterized for LINE-1 methylation and clinical outcome (23). We have not examined CDK6 expression in any of our previous studies. Written informed consent was obtained from each subject, and the study procedures were approved by the institutional review board.

Pyrosequencing to measure LINE-1 methylation

Genomic DNA was extracted from frozen esophageal cancer specimens using a QIAamp DNA Mini Kit (Qiagen) according to the manufacturer's protocol. Genomic DNA was modified with sodium bisulfite using an EpiTect Bisulfite kit (Qiagen). PCR and subsequent pyrosequencing for LINE-1 were performed as previously described by Ogino and colleagues, using the PyroMark Kit (Qiagen; ref. 14). This assay amplifies a region of LINE-1 element (position 305 to 331, accession no. X58075), which includes four CpG sites. Pyrosequencing reactions were performed using the PyroMark Q24 System (Qiagen). Bisulfite pyrosequencing consists of three steps; bisulfite conversion, PCR amplification, and pyrosequencing analysis. Unmethylated cytosine and methylated cytosine are differentiated by bisulfite treatment followed by PCR. In the pyrosequencing step, the cytosine:methylated cytosine ratio at each CpG site is measured as the ratio of thymine:cytosine. The cytosine content relative to the cytosine plus thymine content at each CpG site is expressed as a percentage. In this study, the average relative cytosine content at the four CpG sites was used as the overall LINE-1 methylation level in a given tumor. In published literature, we have validated our LINE-1 methylation pyrosequencing assay; we performed bisulfite conversion on five different DNA specimen aliquots and repeated PCR pyrosequencing five times using four macrodissected cancers. Bisulfite-to-bisulfite (between-bisulfite treatment) SDs ranged from 1.4 to 2.9 (median, 2.3), and run-to-run (between-PCR pyrosequencing run) SDs ranged from 0.6 to 3.3 (median, 1.2; ref. 29).

Immunohistochemical staining

Paraffin-embedded tumor sections were dewaxed in xylene and ethanol, and antigen–epitope retrieval was performed using a streamer autoclave at 120°C for 15 minutes in antigen retrieval solution (pH9, Histofine; Nichirei Biosciences Inc.) and then allowed to cool. Endogenous peroxidase activity was blocked using 3% hydrogen peroxide. Primary antibodies against CDK6 (1:50 dilution; Santa Cruz Biotechnology Inc.) and cyclin D1 (1:50 dilution; Leica Biosystems Newcastle Ltd) were applied, and the slides were incubated at 4°C overnight. The secondary antibody used was a ready-for-use anti-mouse EnVision Peroxidase system (Dako Japan Inc.). The remaining procedure was performed using a Dako EnVision+ System (Dako Japan Inc). The stained slides were counterstained with hematoxylin and bluing reagent. One of the investigators, blinded to any other participant data, recorded nuclear CDK6 and cyclin D1 expression as absent, weak, moderate, or strong. In this study, tumors with weak to strong expression were defined as “positive,” whereas tumors not expressing these proteins were defined as “negative.” Among 129 ESCC tumors, 61 tumors (47%) and 52 tumors (40%) tested positive for CDK6 and cyclin D1, respectively.

Quantitative reverse transcription PCR

Total RNA extraction, cDNA synthesis, and quantitative reverse transcription PCR (qRT-PCR) were carried out as previously described (30, 31). Total cellular RNA was extracted using the RNeasy Mini Kit (Qiagen), and cDNA was synthesized with the SuperScript III Transcriptor First Strand cDNA Synthesis System for RT-PCR (Invitrogen) according to the manufacturers' instructions. qRT-PCR was carried out using a LightCycler 480 II instrument (Roche). To determine the differences in the gene expression levels between specimens, the 2−ΔΔCt method was used to measure the fold changes among the samples. To carry out qRT-PCR, primers were designed using the Universal Probe Library (Roche) following the manufacturer's recommendations. The primer sequences and probes used for real-time PCR were as follows: CDK6, 5- TGATCAACTAGGAAAAATCTTGGA-3′, 5-GTCGACCGGTGCAATCTT-3′, and universal probe #2; glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 5-AGCCACATCGCTCAGACAC-3′, 5-GCCCAATACGACCAAATCC-3′, and universal probe #60.

Array CGH

Array CGH was carried out using the Agilent oligonucleotide array–based CGH microarray kit (SurePrint G3 Human CGH Microarray Kit 2 × 400 K; Agilent Technologies) according to the manufacturer's protocol. Commercial male human genomic DNA was used as the control DNA. Following hybridization, washing, and drying steps, the microarray slides were scanned at 3 μm resolution, using the G2505C microarray scanner (Agilent Technologies). Features were extracted from the scanned images using the Feature Extraction software (version 1.5.1.0; Agilent Technologies). The extracted features were analyzed using the Agilent Cytogenomics software (version 2.0.6.0; Agilent Technologies).

Cell lines

Human esophageal cancer cell lines (TE-1, TE-4, TE-6, TE-8, TE-9, TE-10, TE-11, TE-14, and TE-15) were obtained from the Institute of Development, Aging, and Cancer, Tohoku University (Sendai, Japan) and were cultured in a medium supplemented with 10% FBS in 5% CO2 atmosphere at 37°C.

FISH

CDK6-specific FISH probe was prepared using two human Bacterial Artificial Chromosomes (BAC) DNA clones RP11-809H24 and RP11-1102K14 by labeling with Cy3. As a control, a centromeric region-specific FISH probe for chromosome 7 was prepared using RP11-90C3. The images were captured with the CW4000 FISH application program (Leica Microsystems Imaging Solution).

Statistical methods

For the statistical analyses, we used the JMP software (version 9; SAS Institute). For the survival analysis, the Kaplan–Meier method was used to assess the survival time distribution, and the log-rank test was used. We constructed a CDK6-adjusted Cox proportional hazard model to compute the HR according to the LINE-1 methylation status. Statistical differences were considered significant if P values were <0.05. All reported P values are two-sided.

Pyrosequencing assay for LINE-1 methylation status using frozen ESCC tissues

We quantified the LINE-1 methylation levels of 40 fresh-frozen esophageal cancer tissues using a bisulfite PCR pyrosequencing assay, and obtained valid results in all 40 cases. Representative pyrograms for LINE-1 methylation levels are shown in Fig. 1A. LINE-1 methylation levels in ESCC fresh-frozen tissues were distributed widely and approximately normally. The distribution was as follows: mean, 51.0; median, 52.6; SD, 14.8; range, 24.4–79.7; interquartile range, 38.6–62.5 (all in 0–100 scale; Fig. 1B).

Figure 1.

LINE-1 methylation levels in esophageal cancers. A, representative pyrograms for LINE-1 methylation level (cases #1 and #4). B, LINE-1 methylation levels in 40 resected esophageal cancers. Dark stained bars indicate six selected cases for CGH array (#1–3 as LINE-1–hypomethylated cases and #4–6 as LINE-1–hypermethylated cases).

Figure 1.

LINE-1 methylation levels in esophageal cancers. A, representative pyrograms for LINE-1 methylation level (cases #1 and #4). B, LINE-1 methylation levels in 40 resected esophageal cancers. Dark stained bars indicate six selected cases for CGH array (#1–3 as LINE-1–hypomethylated cases and #4–6 as LINE-1–hypermethylated cases).

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Relationship between LINE-1 methylation levels and chromosomal aberrations

We have previously demonstrated that LINE-1 hypomethylation is associated with a poor prognosis in a study of 217 ESCC paraffin-embedded specimens (23). However, the biologic mechanism by which LINE-1 hypomethylation affects aggressive tumor behavior is not yet fully understood. We hypothesized that global DNA hypomethylation might be a crucial influential factor for DNA copy number variations. Thus, we carried out a CGH array analysis using three LINE-1–hypomethylated tumors (LINE-1 methylation level, 24.4% for case #1; 31.4% for case #2; and 33.5% for case #3) and three LINE-1–hypermethylated tumors (65.3% for case #4; 65.9% for case #5; and 79.2% for case #6). The analyzed cases were selected based on sufficient amount of freshly frozen tissues and sufficient DNA quality. Their clinical and pathologic characteristics are shown in Supplementary Table S1. Pyrograms for cases #1 and #4 are shown in Fig. 1A.

The average number of chromosomal aberrations in 6 ESCC tumors was 168.2 (range, 24–423). Interestingly, LINE-1–hypomethylated tumors showed higher frequency of chromosomal gain or loss compared with LINE-1–hypermethylated tumors. Figure 2A shows a summary of DNA copy number aberrations for LINE-1–hypomethylated tumors and LINE-1–hypermethylated tumors. The number of aberrations was significantly higher in LINE-1–hypomethylated cases (median, 310.0) than in LINE-1–hypermethylated cases (median, 26.3; P = 0.0076; Fig. 2B). A similar result was observed for total length of aberrations (P = 0.0013; Fig. 2B). Regions with an abnormal number of chromosome copies in all hypomethylated tumors are shown in Table 1. Genomic gains were much more common than losses, and genomic imbalance was not found in chromosome 13, 14, 15, or 16. Amplification of 7q21–22 containing a candidate oncogene CDK6 was observed in all LINE-1–hypomethylated tumors, whereas this locus was not amplified in all LINE-1–hypermethylated tumors. Previous studies have shown that CDK6 amplification is associated with CDK6 overexpression and poor survival in esophageal cancer, supporting that CDK6 might mark malignant esophageal cancer (32, 33). From these findings, we further hypothesize that global DNA hypomethylation in ESCC contributes to aggressive phenotype by genomic gain of oncogenes such as CDK6. Thus, the correlation between CDK6 aberrant expression (protein and mRNA levels) and LINE-1 hypomethylation in ESCCs was further investigated.

Figure 2.

Relationship between chromosomal aberrations and LINE-1 methylation. A, summary of DNA copy number aberrations for LINE-1–hypomethylated cases (top) and LINE-1–hypermethylated cases (bottom). Losses (green) are displayed to the left and gains (red) plotted to the right. B, relationship of LINE-1 methylation status with number of aberrations (top) and total length of aberrations (bottom).

Figure 2.

Relationship between chromosomal aberrations and LINE-1 methylation. A, summary of DNA copy number aberrations for LINE-1–hypomethylated cases (top) and LINE-1–hypermethylated cases (bottom). Losses (green) are displayed to the left and gains (red) plotted to the right. B, relationship of LINE-1 methylation status with number of aberrations (top) and total length of aberrations (bottom).

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Table 1.

Chromosome imbalance regions in LINE-1–hypomethylated tumors

CytobandAberrationStart positionStop positionSizeCandidate gene−log10 (P)
1q21 Gain 143224322 150145845 6921524 LOC100130000 1.30102999 
1q21 Gain 150145845 150437139 291295 ANP32E, CA14 1.30102999 
1q21 Gain 150437139 153949859 3512721  1.30102999 
1q21 Gain 153949859 154295703 345845 JTB, RAB13 1.30102999 
1q21 Gain 154295703 155309112 1013410  1.30102999 
1q22 Gain 155309112 155904332 595221 ASH1L, MIR555 1.30102999 
1q21 Gain 155904332 156796795 892464 S100A4 1.30102999 
1q21–22 Gain 156815942 161569102 4753161 NTRK1,INSRR 1.30102999 
1q42 Gain 196800817 225328624 28527808 CNIH3 1.30102999 
1q42 Gain 225334959 227316832 1981874 DNAH14,LBR 1.30102999 
1q44 Gain 248738898 248789540 50643 OR2T10 1.30102999 
1q21 Gain 248789540 249211884 422345 LOC100130000 1.30102999 
2q21 Gain 50765550 52053468 1287919 FSHR, NRXN1 1.30102999 
2q35 Loss 219296581 219500621 204041 VIL1,USP37 1.30102999 
3q21 Loss 50733803 50889948 156146 DOCK3,p21.2 1.30102999 
3q21 Loss 51474959 51486485 11527 VPRBP 1.30102999 
3p14 Loss 71118465 71129430 10966 FOXP1,p13 1.30102999 
3q13 Gain 117950181 162209974 44259794 IGSF11 1.30102999 
3q29 Gain 193132436 193156879 24444 ATP13A4,q29 1.30102999 
3q29 Gain 193156879 194964380 1807502 C3orf21,q29 1.30102999 
4q25 Loss 113504214 113802089 297876 C4orf21,LARP7 1.30102999 
4q28 Loss 128854114 129114533 260420 MFSD8 1.30102999 
5p15 Gain 223613 256351 32739 SDHA, p15.33 1.30102999 
5p15 Gain 822005 887520 65516 ZDHHC11 1.30102999 
5p15 Gain 9197190 14749366 5552177 SEMA5A 1.30102999 
5q31 Loss 125891676 125896749 5074 ALDH7A1 1.30102999 
6p21 Loss 32542694 32565064 22371 HLA-DRB1 1.30102999 
6q22 Gain 125315619 125934949 619331 RNF217 1.30102999 
6q23 Gain 134625874 134958897 333024 SGK1,CpG:21 1.30102999 
7p22 Gain 1886724 2203806 317083 MAD1L1,CpG 1.30102999 
7p21 Gain 7283309 26893878 19610570 C1GALT1 1.30102999 
7q31 Gain 26930268 55712859 28782592 LANCL2 1.30102999 
7q11 Gain 69085548 70537428 1451881 AUTS2,CpG 1.30102999 
7q21 Gain 78545354 86367244 7821891 MAGI2 1.30102999 
7q21–22 Gain 92247365 96665717 4418353 CDK6 1.30102999 
7q31 Gain 107825148 110714112 2888965 NRCAM 1.30102999 
8q11 Gain 49434350 54585979 5151630 EFCAB1,SNAI2 1.30102999 
8q21 Gain 75130234 79574535 4444302 JPH1,GDAP1 1.30102999 
8q22 Gain 105068014 120115974 15047961 RIMS2,TM7SF4 1.30102999 
8q24 Gain 131275342 140955982 9680641 ASAP1 1.30102999 
9q21 Gain 82261109 94649728 12388620 TMC1,TLE4,TLE1 1.30102999 
9q22 Gain 104105604 109937886 5832283 BAAT, MRPL50 1.30102999 
9q33 Gain 117848315 121937952 4089638 TNC, DEC1 1.30102999 
10q11 Gain 52685729 63413583 10727855 PRKG1,MIR605,ZNF488,RBP3 1.30102999 
10q11 Gain 77139109 83286274 6147166 LOC441666,ZNF503 1.30102999 
10q26 Gain 124348251 124351778 3528 DMBT1,q26.13 1.30102999 
11p11 Gain 49170280 49714750 544471 FOLH1 1.30102999 
11q22 Gain 107091691 107502415 410725 CWF19L2 1.30102999 
12p13 Gain 2690550 2786288 95739 CACNA1C 1.30102999 
12p12 Gain 21613937 21622974 9038 PYROXD1 1.30102999 
12p11 Gain 33663998 34591668 927671 ALG10,CpG:25 1.30102999 
17q11 Gain 31344645 33156978 1812334 ACCN1,CCL2 1.30102999 
17q21 Gain 39092472 39890632 798161 KRT23,KRT39 1.30102999 
20p13 Gain 6343032 25493634 19150603 DEFB125,BMP2,HAO1 1.30102999 
20q12–13 Gain 40674508 41626812 952305 PLCG1,ZHX3,PTPRT, q12 1.30102999 
20q13 Gain 60895001 62070367 1175367 BMP7,SPO11,LAMA5,RPS21 1.30102999 
Gain 62063537 154788597 92725061 SPIN4 1.30102999 
Loss 17972056 26066972 8094917  1.30102999 
Loss 28548426 28784095 235670 CpG:32,q11.23 1.30102999 
CytobandAberrationStart positionStop positionSizeCandidate gene−log10 (P)
1q21 Gain 143224322 150145845 6921524 LOC100130000 1.30102999 
1q21 Gain 150145845 150437139 291295 ANP32E, CA14 1.30102999 
1q21 Gain 150437139 153949859 3512721  1.30102999 
1q21 Gain 153949859 154295703 345845 JTB, RAB13 1.30102999 
1q21 Gain 154295703 155309112 1013410  1.30102999 
1q22 Gain 155309112 155904332 595221 ASH1L, MIR555 1.30102999 
1q21 Gain 155904332 156796795 892464 S100A4 1.30102999 
1q21–22 Gain 156815942 161569102 4753161 NTRK1,INSRR 1.30102999 
1q42 Gain 196800817 225328624 28527808 CNIH3 1.30102999 
1q42 Gain 225334959 227316832 1981874 DNAH14,LBR 1.30102999 
1q44 Gain 248738898 248789540 50643 OR2T10 1.30102999 
1q21 Gain 248789540 249211884 422345 LOC100130000 1.30102999 
2q21 Gain 50765550 52053468 1287919 FSHR, NRXN1 1.30102999 
2q35 Loss 219296581 219500621 204041 VIL1,USP37 1.30102999 
3q21 Loss 50733803 50889948 156146 DOCK3,p21.2 1.30102999 
3q21 Loss 51474959 51486485 11527 VPRBP 1.30102999 
3p14 Loss 71118465 71129430 10966 FOXP1,p13 1.30102999 
3q13 Gain 117950181 162209974 44259794 IGSF11 1.30102999 
3q29 Gain 193132436 193156879 24444 ATP13A4,q29 1.30102999 
3q29 Gain 193156879 194964380 1807502 C3orf21,q29 1.30102999 
4q25 Loss 113504214 113802089 297876 C4orf21,LARP7 1.30102999 
4q28 Loss 128854114 129114533 260420 MFSD8 1.30102999 
5p15 Gain 223613 256351 32739 SDHA, p15.33 1.30102999 
5p15 Gain 822005 887520 65516 ZDHHC11 1.30102999 
5p15 Gain 9197190 14749366 5552177 SEMA5A 1.30102999 
5q31 Loss 125891676 125896749 5074 ALDH7A1 1.30102999 
6p21 Loss 32542694 32565064 22371 HLA-DRB1 1.30102999 
6q22 Gain 125315619 125934949 619331 RNF217 1.30102999 
6q23 Gain 134625874 134958897 333024 SGK1,CpG:21 1.30102999 
7p22 Gain 1886724 2203806 317083 MAD1L1,CpG 1.30102999 
7p21 Gain 7283309 26893878 19610570 C1GALT1 1.30102999 
7q31 Gain 26930268 55712859 28782592 LANCL2 1.30102999 
7q11 Gain 69085548 70537428 1451881 AUTS2,CpG 1.30102999 
7q21 Gain 78545354 86367244 7821891 MAGI2 1.30102999 
7q21–22 Gain 92247365 96665717 4418353 CDK6 1.30102999 
7q31 Gain 107825148 110714112 2888965 NRCAM 1.30102999 
8q11 Gain 49434350 54585979 5151630 EFCAB1,SNAI2 1.30102999 
8q21 Gain 75130234 79574535 4444302 JPH1,GDAP1 1.30102999 
8q22 Gain 105068014 120115974 15047961 RIMS2,TM7SF4 1.30102999 
8q24 Gain 131275342 140955982 9680641 ASAP1 1.30102999 
9q21 Gain 82261109 94649728 12388620 TMC1,TLE4,TLE1 1.30102999 
9q22 Gain 104105604 109937886 5832283 BAAT, MRPL50 1.30102999 
9q33 Gain 117848315 121937952 4089638 TNC, DEC1 1.30102999 
10q11 Gain 52685729 63413583 10727855 PRKG1,MIR605,ZNF488,RBP3 1.30102999 
10q11 Gain 77139109 83286274 6147166 LOC441666,ZNF503 1.30102999 
10q26 Gain 124348251 124351778 3528 DMBT1,q26.13 1.30102999 
11p11 Gain 49170280 49714750 544471 FOLH1 1.30102999 
11q22 Gain 107091691 107502415 410725 CWF19L2 1.30102999 
12p13 Gain 2690550 2786288 95739 CACNA1C 1.30102999 
12p12 Gain 21613937 21622974 9038 PYROXD1 1.30102999 
12p11 Gain 33663998 34591668 927671 ALG10,CpG:25 1.30102999 
17q11 Gain 31344645 33156978 1812334 ACCN1,CCL2 1.30102999 
17q21 Gain 39092472 39890632 798161 KRT23,KRT39 1.30102999 
20p13 Gain 6343032 25493634 19150603 DEFB125,BMP2,HAO1 1.30102999 
20q12–13 Gain 40674508 41626812 952305 PLCG1,ZHX3,PTPRT, q12 1.30102999 
20q13 Gain 60895001 62070367 1175367 BMP7,SPO11,LAMA5,RPS21 1.30102999 
Gain 62063537 154788597 92725061 SPIN4 1.30102999 
Loss 17972056 26066972 8094917  1.30102999 
Loss 28548426 28784095 235670 CpG:32,q11.23 1.30102999 

Relationship between CDK6 expression and LINE-1 methylation in ESCC tissues

We measured CDK6 mRNA expression levels by qRT-PCR in 15 ESCCs tissues and matched normal esophageal mucosa. Cancer tissues showed significantly higher levels of CDK6 mRNA expression than matched esophageal mucosa (P = 0.0046; Fig. 3A). Then, CDK6 expression at the protein level in ESCCs was evaluated by immunohistochemistry. CDK6 immunoreactivity was absent in normal squamous cell epithelium (Fig. 3B-a). Among 40 tumors, 24 tumors (60%) showed CDK6-positive expression (Fig. 3B-b), and 16 tumors (40%) showed CDK6-negative expression (Fig. 3B-c). These observations of CDK6 overexpression may demonstrate that CDK6 has vital roles in ESCC tumorigenesis. To evaluate the impact of LINE-1 hypomethylation on CDK6 expression, we assessed the association between CDK6 expression and LINE-1 methylation levels in ESCC tissues. In 20 ESCCs for which LINE-1 data and RNA were available, CDK6 mRNA expression levels were inversely associated with LINE-1 methylation levels (P = 0.039; Fig. 3C). Tumors with CDK6-positive immunoreactivity exhibited significantly lower LINE-1 methylation levels (median, 42.3; mean, 45.1; SD, 13.1) than tumors with negative immunoreactivity (median, 60.1; mean, 60.0; SD, 12.8; P = 0.0001 by the paired t test; Fig. 3D). These results collectively suggest that LINE-1 methylation level (and therefore global DNA methylation level) might have an influence on CDK6 expression through genomic gain of CDK6.

Figure 3.

Association of CDK6 expression and LINE-1 methylation levels. A, CDK6 mRNA expression levels in esophageal cancers and matched normal mucosa. The cancer tissues showed significantly higher levels of expression than the matched normal mucosa (P = 0.0046 by the paired t test). B, a, CDK6 immunostaining for esophageal cancer and normal esophageal mucosa. Cancerous lesion shows positive staining, whereas normal mucosa shows negative staining. b, positive expression of CDK6 in nuclei of esophageal cancer cells. c, negative expression of CDK6 in nuclei of esophageal cancer cells. C, correlation between LINE-1 methylation levels and CDK6 mRNA expression levels. D, CDK6-positive tumors showed significantly lower levels of LINE-1 methylation than CDK6-negative tumors (P = 0.00010 by the paired t test).

Figure 3.

Association of CDK6 expression and LINE-1 methylation levels. A, CDK6 mRNA expression levels in esophageal cancers and matched normal mucosa. The cancer tissues showed significantly higher levels of expression than the matched normal mucosa (P = 0.0046 by the paired t test). B, a, CDK6 immunostaining for esophageal cancer and normal esophageal mucosa. Cancerous lesion shows positive staining, whereas normal mucosa shows negative staining. b, positive expression of CDK6 in nuclei of esophageal cancer cells. c, negative expression of CDK6 in nuclei of esophageal cancer cells. C, correlation between LINE-1 methylation levels and CDK6 mRNA expression levels. D, CDK6-positive tumors showed significantly lower levels of LINE-1 methylation than CDK6-negative tumors (P = 0.00010 by the paired t test).

Close modal

Relationship between CDK6 expression and LINE-1 methylation in ESCC cell lines

We then tested this correlation between LINE-1 methylation levels and CDK6 expression levels using nine ESCC cell lines. LINE-1 methylation levels in nine ESCC cell lines were widely distributed (range, 22.8–67.0). The inverse relationship between LINE-1 methylation levels and CDK6 mRNA expression levels was observed (P = 0.028; Fig. 4A). Furthermore, to confirm the gene amplification of CDK6 in ESCCs, we applied the FISH analysis for TE11 cell line (black dot in Fig. 4B), which demonstrated lower levels of LINE-1 methylation and high levels of CDK6 mRNA expression in vitro. We counted the number of signals for CDK6 and chromosome 7 centromere (control) in 100 TE11 cells. In 91 of 100 cells, the number of signals for CDK6 was larger than that for chromosome 7 centromere (Supplementary Table S2), confirming the presence of CDK6 amplification in ESCC cells.

Figure 4.

CDK6 mRNA expression and CDK6 amplification in ESCC cell lines. A, relationship between LINE-1 methylation levels and CDK6 mRNA expression levels. Black spots indicate TE11 cell lines analyzed by FISH. B, FISH images with the CDK6 probe (red) and the chromosome 7 centromere probe (green; control).

Figure 4.

CDK6 mRNA expression and CDK6 amplification in ESCC cell lines. A, relationship between LINE-1 methylation levels and CDK6 mRNA expression levels. Black spots indicate TE11 cell lines analyzed by FISH. B, FISH images with the CDK6 probe (red) and the chromosome 7 centromere probe (green; control).

Close modal

CDK6 expression, LINE-1 hypomethylation, and patient survival

To understand the relationship between CDK6 and tumor malignant behavior in ESCC, we examined the prognostic role of CDK6 in 129 cases with available LINE-1 methylation data and CDK6 expression data. All cases received no preoperative treatment. The relationships between levels of LINE-1 methylation and clinical and pathologic features are summarized in Supplementary Table S3. The median follow-up time for censored patients was 2.7 years. Patients with tumor expressing CDK6 (n = 61) experienced lower disease-free survival rates than patients not expressing CDK6 (n = 68; log-rank P = 0.043, Fig. 5A). This result certainly implies that CDK6 may have oncogenic roles in ESCC and contribute to ESCC progression.

Figure 5.

Survival analyses of CDK6 expression and LINE-1 methylation level. A, Kaplan–Meier curves according to CDK6 expression status. B, Kaplan–Meier curves according to LINE-1 methylation status. C, patients with LINE-1 hypomethylation experienced a significantly higher disease recurrence rate (HR, 2.69). In the CDK6-adjusted Cox model, the HR of LINE-1 hypomethylation for disease recurrence was decreased to 2.25. D, possible mechanism by which LINE-1 hypomethylation confers a poor prognosis in ESCC.

Figure 5.

Survival analyses of CDK6 expression and LINE-1 methylation level. A, Kaplan–Meier curves according to CDK6 expression status. B, Kaplan–Meier curves according to LINE-1 methylation status. C, patients with LINE-1 hypomethylation experienced a significantly higher disease recurrence rate (HR, 2.69). In the CDK6-adjusted Cox model, the HR of LINE-1 hypomethylation for disease recurrence was decreased to 2.25. D, possible mechanism by which LINE-1 hypomethylation confers a poor prognosis in ESCC.

Close modal

We next examined whether the influence of LINE-1 hypomethylation on patient prognosis was modified by CDK6 expression status. In this current study, LINE-1 hypomethylation was defined as “<55.5” according to our previous article (23). In Kaplan–Meier analysis, patients with LINE-1 hypomethylation (n = 31) experienced significantly shorter disease-free survival compared with LINE-1–hypermethylated cases (n = 98; log-rank P = 0.0005; Fig. 5B). In univariate Cox regression analysis, compared with LINE-1–hypermethylated cases, patients with LINE-1 hypomethylation experienced a significantly higher disease recurrence rate [HR, 2.69; 95% confidence interval (CI), 1.48–4.78; P = 0.0015; Fig. 5C]. In the CDK6-adjusted Cox model, the HR of LINE-1 hypomethylation for disease recurrence was decreased to 2.25 (95% CI, 1.22–4.04). Thus, this result shows the proportional reduction in the regression coefficient for LINE-1 methylation due to the inclusion of CDK6 expression in the Cox regression model. In the multivariate Cox model adjusted for sex (male vs. female), age at surgery (<64 vs. 65≤), tumor location (upper vs. lower), tumor stage (stage I vs. II, III), histologic grade (G1 vs. G2–4), and CDK6 expression (positive vs. negative), LINE-1 hypomethylation was found to be associated with a significantly higher recurrence rate (multivariate HR, 2.43; 95% CI, 1.19–4.89; P = 0.016).

Cyclin D1 expression, LINE-1 methylation level, and patient survival

Given that cyclin D1 is the positive regulatory partner of CDK6, we further examined the relationship between cyclin D1 expression, LINE-1 methylation, and patient outcome. No relationship was observed between cyclin D1 expression and level of LINE-1 methylation (P = 0.94; Supplementary Fig. S2A and S2B). We also found no association between status of cyclin D1 expression and disease-free survival (log-rank P = 0.60; Supplementary Fig. S2C). Furthermore, the prognostic impact of LINE-1 hypomethylation was not reduced by cyclin D1 expression (cyclin D1-adjusted HR, 2.74; 95% CI, 1.51–4.88).

We previously demonstrated the correlation between LINE-1 hypomethylation (i.e., global DNA hypomethylation) and poor prognosis in ESCCs (23). In this current study, to clarify the mechanism by which LINE-1 hypomethylation affects ESCC aggressive behavior, we investigated LINE-1–hypomethylated and LINE-1–hypermethylated tumors using CGH array. We found that LINE-1–hypomethylated tumors presented highly frequent genomic gains at various loci containing candidate oncogenes including CDK6. The association between CDK6 amplification, CDK6 expression, and LINE-1 methylation levels in ESCCs was also observed. CDK6 expression was significantly associated with unfavorable prognosis in 128 patients with ESCC. Moreover, we have shown the proportional reduction in the regression coefficient for LINE-1 methylation due to the inclusion of CDK6 expression in the Cox regression model. Collectively, CDK6 may be an intermediate factor explaining the relationship between LINE-1 hypomethylation and poor prognosis. Global DNA hypomethylation in ESCC might contribute to the acquisition of tumor aggressive behavior through genomic gains of oncogenes such as CDK6 (Fig. 5D).

LINE-1 represents a major repetitive element and occupies approximately 17% of the human genome. Thus, the level of LINE-1 methylation is regarded to be a surrogate marker of global DNA methylation (13). Tumor LINE-1 hypomethylation has been associated with inferior prognosis in not only ESCC but also many different human tumor types such as colon cancer, glioma, ovarian cancer, lung cancer, and gastric cancer (17–22). In addition, LINE-1 hypomethylation was associated with clinically aggressive disease in patients with prostate cancer and gastrointestinal stromal tumors (16, 34). Nonetheless, the mechanism by which tumoral LINE-1 hypomethylation correlates with malignant phenotype or patient prognosis in human cancers remains to be fully explored. Experimental evidence supports the relationship between LINE-1 hypomethylation (i.e., global DNA hypomethylation) and genomic instability. A study using Nf1+/−p53+/− mice has shown that introduction of a hypomorphic Dnmt1 allele causes genome-wide DNA hypomethylation that leads to significant increases in the loss of heterozygosity rate and tumor development (35). Another study has shown that genomic hypomethylation in ApcMin/+ mice leads to increases in microadenoma formation through loss of heterozygosity at the Apc locus (36). Thus, we carried out an array CGH analysis to assess the potential implication of LINE-1 hypomethylation in genomic instability. Interestingly, LINE-1–hypomethylated tumors showed highly frequent genomic gains at various loci containing numerous candidate oncogenes. Our finding is likely consistent with aforementioned experimental results.

In recent years, high-resolution array-based CGH has been applied to identify target oncogenes and tumor suppressor genes through defining recurrent gains and losses in various cancers (37). Two studies with CGH array analysis for ESCC samples have revealed high-level amplifications at 3q27.1, 7p11, 8q21.11, 8q24.21, 11q13.3, 11q22, 12q15–q21.1, 18q11.2, and 19q13.11–q13.12, and homozygous deletions at 4q34.3–q35.1 and 9p21.3 (38, 39). Bandla and colleagues conducted a comprehensive analysis of DNA copy number abnormalities in both ESCCs and esophageal adenocarcinoma. They reported a number of oncogenes that were amplified in both histologic types, including CDK6, MCL1, EGFR, SMURF1, KRAS, ERBB2, CCNE1, VEGFA, MET, and IGF1R (32). Among these genes, CDK6 amplification has been associated with CDK6 overexpression and a poor survival in esophageal cancer (33), supporting that CDK6 might mark esophageal cancer with aggressive biologic behavior. We also found that CDK6 expression was related with a poor prognosis in ESCCs. These results certainly imply that CDK6 has oncogenic roles in ESCC and contributes to ESCC progression. Our finding of the correlation between LINE-1 hypomethylation, CDK6 amplification, and CDK6 aberrant expression suggests that LINE-1–hypomethylated tumors might acquire malignant aggressive behavior by increasing genomic amplification and by altering the expression levels of candidate oncogenes represented by CDK6. However, other mechanisms may also be involved. In addition to its role as a surrogate marker for global DNA methylation, LINE-1 methylation status by itself likely has biologic effects, because retrotransposons such as LINE-1 elements can provide alternative promoters and contribute to noncoding RNA expression, which regulates the functions of a number of genes.

CDK6, which belongs to a family of serine–threonine kinases, is an important regulator of G1–S-phase progression. Together with its binding partner cyclin D1, CDK6 forms active complexes that promote cellular proliferation by phosphorylating and inactivating the retinoblastoma protein (40). CDK6 amplification has been previously reported in human neoplasms including esophageal cancer, in which (in most cases) it is correlated with an adverse prognosis (33). Our current study revealed a relationship between CDK6 expression and LINE-1 hypomethylation (i.e., global DNA hypomethylation) and poor prognosis, suggesting that aberrant expression of CDK6 might be epigenetically regulated, and mark an aggressive type of esophageal cancer. Importantly, the current study found no relationship between cyclin D1 expression and LINE-1 hypomethylation, suggesting that, with regard to G1/transition regulation, cyclin D1 is regulated by an entirely different mechanism from that of CDK6.

Interestingly, CDK6 is capable of blocking cell differentiation apart from the classical role in promoting cell proliferation (41, 42). This uniqueness makes it an attractive candidate for the development of small-molecule inhibitors. PD-0332991 is a recently developed specific inhibitor targeting CDK4/6-specific phosphorylation of retinoblastoma protein, and is currently in phase I clinical trials for advanced cancers (43). This small-molecule inhibitor has been shown to potently suppress proliferation and anchorage-independent growth of esophageal cancer cell lines (33). Hereafter, CDK6 expression in the resected specimens might attract increasing attention as a biomarker for patient selection. In this respect, our findings may have clinical implications.

In summary, LINE-1–hypomethylated ESCC tumors presented highly frequent genomic gains at various loci containing CDK6. In addition, we found that LINE-1 methylation levels were associated with CDK6 expression in ESCCs and that the prognostic impact of LINE-1 hypomethylation in patients with ESCC might be attenuated by CDK6 expression. Collectively, these findings may suggest that global DNA hypomethylation in ESCC might contribute to the acquisition of aggressive tumor behavior through genomic gains of oncogenes such as CDK6. Future studies are needed to confirm our findings, and also to examine other potential mechanism(s) by which genome-wide DNA hypomethylation affects tumor behavior.

No potential conflicts of interest were disclosed.

Conception and design: Y. Baba, H. Baba

Development of methodology: Y. Baba, H. Baba

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): Y. Baba, A. Murata, H. Shigaki, K. Miyake, H. Baba

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Y. Baba, M. Watanabe, T. Ishimoto, K. Sakamaki, H. Baba

Writing, review, and/or revision of the manuscript: Y. Baba, M. Watanabe, H. Baba

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): H. Shigaki

Study supervision: M. Watanabe, M. Iwatsuki, N. Yoshida, E. Oki, M. Nakao

This work was supported in part by a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (JSPS; grant number 24659617).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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