Skip to main content
Scientific Reports logoLink to Scientific Reports
. 2016 Jan 12;6:19239. doi: 10.1038/srep19239

Prognostic Significance of EZH2 Expression in Non-Small Cell Lung Cancer: A Meta-analysis

Xinhua Wang 1, Hongqing Zhao 1, Lei Lv 1, Liang Bao 1, Xun Wang 1, Shuguang Han 1,a
PMCID: PMC4709684  PMID: 26754405

Abstract

Various studies examined the relationship between EZH2 overexpression with the clinical outcome in patients with non-small cell lung cancer (NSCLC), but yielded inconsistent results. Electronic databases updated to Dec 2014 were searched to find relevant studies. A meta-analysis was conducted with eligible studies which quantitatively evaluated the relationship between EZH2 overexpression and survival of patients with NSCLC Survival data were aggregated and quantitatively analyzed. We performed a meta-analysis of 10 studies (n = 1,695 patients) that evaluated the correlation between EZH2 overexpression and survival in patients with lung cancer. Combined hazard ratios suggested that EZH2 overexpression was associated with poor prognosis of overall survival (OS) (HR = 1.68, 95% CI: 1.42–1.93) in patients with lung cancer. In the stratified analysis, significantly risks were found among Asians (HR = 1.33, 95% CI: 1.62–1.70), lung adenocarcinoma patients (HR = 1.75, 95% CI: 1.38–2.52, in stage I NSCLC patients (HR = 2.51, 95% CI: 1.23–3.79), but not among Caucasians. EZH2 overexpression indicates a poor prognosis for patients with NSCLC, this effect appears also significant when the analysis is restricted in Asian population, lung AC and stage I patients, but not among Caucasians.


Lung cancer is a leading cause of cancer deaths in the world. Approximately 80–85% of all lung cancers are non-small-cell lung cancer (NSCLC)1. The prognosis for lung cancer patients is generally poor, with an overall 5 year survival rate of approximately 15%, and it has shown little improvement in recent decades2,3. Several independent prognostic factors for survival have been identified: performance status (PS), disease stage, age, sex and amount of weight lost4. Some of these factors are useful when choosing treatment options for an individual, principally disease stage and PS. However, the discriminant value of most potential prognostic biological markers is insufficient to predict the optimal therapeutic course for an individual5.

Enhancer of zeste homolog 2 (EZH2) is a key component of the polycomb repressive complex 2, which possesses histone methyltransferase activity and mediates gene silencing through posttranslational histone modifications6. EZH2 is frequently overexpressed in a wide variety of human malignancies such as breast cancer7, prostate cancer8, gastric cancer9, colorectal cancer10 and lung cancer. In addition, it also promotes cancer development and progression through chromatin modification by epigenetic activation of oncogenic signaling cascades and silencing of tumor suppressor genes, and has been implicated in cell proliferation, differentiation, invasion, and metastasis11. Thus, it is acting with oncogenic properties.

Many studies have evaluated whether the overexpression of EZH2 may be a prognostic factor for survival in patients with lung cancer. However, the results of the studies are inconclusive and no consensus has been reached. It is unknown whether differences in these investigations have been mostly due to their limited sample size or genuine heterogeneity. Thus, we conducted a meta-analysis of all available studies relating EZH2 with the clinical outcome in patients with lung cancer.

Materials and Methods

Search strategy and study selection

The electronic databases PubMed, Embase and CNKI (China National Knowledge Infrastructure) were searched for studies to include in the present meta-analysis. An upper date limit of Dec 01, 2014 was applied; we used no lower date limit. Searches included the terms “lung”, “cancer or carcinoma or tumour or neoplasm”, “EZH2”, “Enhancer of zeste homolog 2”, and “prognosis”. We also reviewed the Cochrane Library for relevant articles. The references reported in the identified studies were also used to complete the search.

Studies eligible for inclusion in this meta-analysis met the following criteria: (1) measure EZH2 expression in the primary lung cancer with IHC (immunohistochemistry) or Realtime-PCR (polymerase chain reaction); (2) provide information on survival (i.e. overall survival [OS], studies investigating response rates only were excluded); (3) When the same author reported results obtained from the same patient population in more than one publication, only the most recent report, or the most complete one, was included in the analysis. Two reviewers (X.W. and H.Z.) independently determined study eligibility. Disagreements were resolved by consensus.

Data extraction and quality assessment

The final articles included were assessed independently by two reviewers (X.W. and H.Z.). Data retrieved from the reports included author, publication year, patient source, histology, study design, test method, positive, follow-up and survival data (Table 1). If data from any of the above categories were not reported in the primary study, items were treated as “not applicable”. Only four studies did not provide the data of positive of EZH2, which did not affect the subsequent statistical analysis. Thus, we did no contact the author of the primary study to request the information. We did not use prespecified quality-related inclusion or exclusion criteria and did not weigh each study by a quality score, because the quality score has not received general agreement for use in a meta-analysis, especially observational studies12.

Table 1. Main characteristics and results of the eligible studies.

First author-year Patients source Histology stage N pts Method Positive (%) covariates of multivariate analysis HR estimation Survival results
Behrens C 2013-set 1 USA AC I-III 320 IHC NA Age, gender, race, smoking status, disease stage HR and 95% CI 1.49(1.38–2.72) Poor
Behrens C 2013-set 2 USA AC I-III 91 IHC NA Age, gender, race, disease stage HR and 95% CI 2.74(2.30–5.80) Poor
Zhang J 2013 China NSCLC I 84 IHC 65.5 Age, gender, race, histology, smoking status HR and 95% CI 3.10(1.23–6.89) Poor
Chen X-2013 Japan NSCLC I 42 IHC 56 Age, gender, race, histology HR and 95% CI 1.97(0.77–5.03) NS
Huqun 2012 Japan NSCLC I 106 IHC 58.5 Age, gender, race, smoking status, histology HR and 95% CI 2.80(1.19–6.59) Poor
Takawa-2011 Japan NSCLC I-III 292 IHC 46.2 Age, gender, race, histology, disease stage HR and 95% CI 1.14(0.79–1.65) NS
Kikuchi J 2010 Japan NSCLC I-IV 157 IHC NA Age, gender, race, histology, smoking status, disease stage HR and 95% CI 1.59(0.86–2.94) NS
  Japan NSCLC I 83 IHC NA Age, gender, race, histology HR and 95% CI 2.56(1.01–6.67) Poor
Xu C-2014 China NSCLC IIIB- IV 360 IHC 56.7 Age, gender, race, histology, smoking status HR and 95% CI 2.72(1.14–6.50) Poor
Riquelme-2014 USA AC I-III 149 RealTime-PCR 49 Age, gender, race, disease stage HR and 95% CI 1.83(1.04–3.21) Poor
Cao W 2012 USA NSCLC I-IV 94 RealTime-PCR 50 Age, gender, race, histology, disease stage HR and 95% CI 2.13(1.64–2.51) Poor

IHC, immunohistochemistry; NSCLC, non-small-cell lung cancer; AC, adenocarcinoma; NS, not significant; NA: not applicable; HR, hazard ratio; N pts, number of patients; PCR, polymerase chain reaction.

Statistical methods

For the quantitative aggregation of the survival results, we measured the impact of EZH2 overexpression on survival by HR between the two survival distributions. HRs and 95% confidence intervals (CIs) were used to combine as the effective value. If the HRs and their 95% CIs were given explicitly in the articles, we used crude ones. When these variables were not given explicitly, they were calculated from the available numerical data using methods reported by Parmar et al.13.

Heterogeneity of the individual HRs was calculated with χ2 tests according to Peto’s method14. Heterogeneity test with inconsistency index (Ι2) statistic and Q statistic was performed. If HRs were found to have fine homogeneity, a fixed effect model was used for secondary analysis; if not, a random-effect model was used. DerSimonian-Laird random effects analysis15 was used to estimate the effect of EZH2 overexpression on survival. By convention, an observed HR > 1 implies worse survival for the group with EZH2 overexpression. The impact of VEGF on survival was considered to be statistically significant if the 95% confidence interval (CI) did not overlap with 1. Horizontal lines represent 95% CIs. Each box represents the HR point estimate, and its area is proportional to the weight of the study. The diamond (and broken line) represents the overall summary estimate, with CI represented by its width. The unbroken vertical line is set at the null value (HR = 1.0).

Evidence of publication bias was sought using the methods of Egger et al.16 and of Begg et al.17. Intercept significance was determined by the t test suggested by Egger (P < 0.05 was considered representative of statistically significant publication bias). All of the calculations were performed by STATA version 11.0 (Stata Corporation, College Station, TX).

Results

Study selection and characteristics

Ten studies18,19,20,21,22,23,24,25,26 published between 2010 and 2014 were eligible for this meta-analysis. All reported the prognostic value of EZH2 status for survival in lung cancer patients. The total number of patients included was 1695, ranging from 83 to 360 patients per study (median 169). The major characteristics of the 10 eligible publications are reported in Table 1. The studies were conducted in 3 countries (China, USA and Japan). Among the 10 studies, 6 studies (1041 patients, 61.4%) were performed in Asian populations, and the remaining 4 studies (654 patients, 38.6%) followed Non-Asian patients. All patients in the eligible studies were determined by pathological stage.

All of the studies reported the prognostic value of EZH2 status for survival in patients with lung cancer. All of the 10 studies reported HRs (multivariate analysis) explicitly. A HR on OS could be extracted for 10 studies. Eight of the 10 studies identified EZH2 overexpression as an indicator of poor OS, and the other 2 studies showed no statistically significant impact of EZH2 overexpression on OS.

Meta-Analysis

The results of the meta-analysis were shown in Table 2 and Fig. 1. Overall, the combined HR for all 10 eligible studies evaluating EZH2 overexpression on OS was 1.68(1.42–1.93), suggesting that EZH2 overexpression was associated with poor prognosis for lung cancer. No significant heterogeneity was observed among the studies (Q = 7.39, I2 = 36.9%, P = 0.113). When grouped according to the geographic settings of individual studies, the combined HRs of Asian studies and Caucasian studies were 1.33(1.62–1.70) and 1.96(0.42–2.30), respectively, indicating EZH2 was an indicator of poor prognosis in Asian populations but not for Caucasians.

Table 2. Meta-analysis: HR value in lung cancer subgroups according to histology and stage.

  Nb Patients Combined HR (95% CI) χ2heterogeneity test (P)
Overall 10 1695 1.68(1.42–1.93) 0.113
Asian 6 1041 1.33(1.62–1.70) 0.415
Caucasian 4 654 1.96(0.42–2.30) 0.348
Adenocarcinoma 3 560 1.75(1.38–2.52) 0.134
Stage I 4 315 2.51(1.23–3.79) 0.928

Abbreviations: HR: hazard ratio; Nb: number of studies; CIs: confidence intervals.

Figure 1. Meta-analysis (Forest plot) of the 10 evaluable studies assessing EZH2 in lung cancer stratified by patient source for overall survival.

Figure 1

The data were adequate to aggregate the studies of lung adenocarcinoma and stage I NSCLC for subgroup analyses. When we aggregated three studies that reported results for adenocarcinoma, the combined HR was statistically significant: HR 1.75 (95% CI: 1.38–2.52, I2 = 46.2%, P = 0.154 for heterogeneity). We also observed a statistically significant effect of EZH2 expression on survival in stage I patients with an HR of 2.51 (95% CI: 1.23–3.79, I2 = 0%, P = 0.928 for heterogeneity) (Fig. 2).

Figure 2. Meta-analysis (Forest plot) of the 4 evaluable studies assessing EZH2 in lung cancer stratified by patient source for overall survival.

Figure 2

Publication Bias

Begg’s funnel plot and Egger’s test were performed to assess the publication bias in the literature. All 10 eligible studies investigating EZH2 overexpression on overall survival yielded a Begg’s test score of p = 0.260 and an Egger’s test score of p = 0.620, meanwhile according to the funnel plot (Fig. 3), the absence of publication bias was found. Similar results were found for investigating EZH2 overexpression on overall survival of stage I NSCLC patients (a Begg’s test score of p = 0.774 and an Egger’s test score of p = 0.221) (Fig. 4). These results suggested that there were no publication biases in these subgroup analyses.

Figure 3. Funnel plot of the 10 evaluable studies assessing EZH2 in lung cancer for overall survival.

Figure 3

Figure 4. Funnel plot of the 4 evaluable studies assessing EZH2 in stage I lung cancer for overall survival.

Figure 4

Discussion

EZH2 is the catalytic subunit of PRC2, which catalyses the addition of methyl groups to lysine 27 on histone H3 (H3K27) in the promoters of target genes, leading to repression of gene transcription27,28. The gene of EZH2, encoding a polycomb group protein, plays an important role in tumorigenesis and cancer progression through epigenetic gene silencing and chromatin remodeling29.

Our present meta-analysis is the first to evaluate the correlation between EZH2 overexpression and survival in patients with lung cancer. The meta analysis combined 10 publications including 1,695 patients with lung cancer to yield statistics, indicating statistically significant role of EZH2 on overall survival in lung cancer. Combined hazard ratios suggested that EZH2 overexpression was associated with poor prognosis of overall survival (OS) (HR = 1.68, 95% CI: 1.42–1.93) in patients with lung cancer. In the stratified analysis, significantly risks were found among Asians (HR = 1.33, 95% CI: 1.62–1.70), lung adenocarcinoma patients (HR = 1.75, 95% CI: 1.38–2.52, in stage I NSCLC patients (HR = 2.51, 95% CI: 1.23–3.79), but not among Caucasians (HR = 1.96, 95% CI: 0.42–2.30). When analysis was restricted to stage I lung cancer, we found that the combined HR (2.51) was larger than the combined HR for all 10 eligible studies of stages I-III (1.68), suggesting that EZH2 expression could be an important prognostic factor for early-stage lung cancer.

The heterogeneity issue was complicated in the systematic review and meta-analysis was. We found no significant heterogeneity among all studies included and subgroup analysis. Another potential source of bias is related to the method of HR and 95% CI extrapolation. If these statistics were not reported by the authors, we calculated them from the data available in the article. If this was not possible, we extrapolated them from the survival curves, necessarily making assumptions about the censoring process. Data for multivariate survival analysis reported in the article were included in the present systematic review with meta-analysis; if these data were not available, data calculated from survival curves by univariate analysis were included. These results should be confirmed by an adequately designed prospective study. Furthermore, the exact value of EZH2 overexpression status needs to be determined by appropriate multivariate analysis. Unfortunately, few prospectively designed prognostic studies concerning biomarkers have been reported; thus, our collection of many retrospective studies revealed more significance.

Our data were not consistent with the results of a previous meta-analysis30 included four studies with gastric cancer patients, that meta-analysis results demonstrated that the OS of EZH2-negative patients was shorter than that of patients with positive expression of EZH2 (HR = 0.54, 95% CI = 0.05–1.03). The previous meta-analysis included only 4 Asian studies, and the data were insufficient to determine the prognostic value of EZH2 for subgroups divided according to histology or disease stage. In our study, however, we have focused on the prognostic significance of EZH2 in NSCLC patients. We performed this systematic review and meta-analysis to yield summary statistics by including more recent related studies and by generally using a more comprehensive search strategy. Screening, study selection, and quality assessment were performed. We also explored heterogeneity and potential publication bias in accordance with published guidelines.

Publication bias31 is a major concern for all forms of meta-analysis; positive results tend to be accepted by journals, while negative results are often rejected or not even submitted. The present analysis does not support publication bias; the obtained summary statistics likely approximate the actual average. However, it should be noted that our meta-analysis could not completely exclude biases. For example, the study was restricted to papers published in English and Chinese, which probably introduced bias.

In conclusion, despite the limitations described above, our meta-analysis is the first study to systematically estimate the association between EZH2 expression detected by IHC or Realtime-PCR and lung cancer survival. As determined in our meta-analysis, we concluded that EZH2 overexpression was associated with poor overall survival in lung cancer, this effect appears also significant when the analysis is restricted in Asian population, lung AC and stage I patients, but not among Caucasians. To strengthen our findings, well-designed prospective studies with better standardized assessment of prognostic markers should help to explore the relation between EZH2 overexpression and survival of lung cancer.

Additional Information

How to cite this article: Wang, X. et al. Prognostic Significance of EZH2 Expression in Non-Small Cell Lung Cancer: A Meta-analysis. Sci. Rep. 6, 19239; doi: 10.1038/srep19239 (2016).

Footnotes

Author Contributions Conceived and designed the experiments: S.G.H. and X.H.W. Performed the experiments: S.G.H., X.H.W. and H.Q.Z. Analyzed the data: S.G.H., X.H.W. and L.L. Contributed reagents/materials/analysis tools: L.L., L.B. and X.W. Wrote the paper: S.G.H. and X.H.W. Designed the software used in analysis: S.G.H., X.H.W. All authors reviewed the manuscript.

References

  1. Siegel R., Ma J., Zou Z. & Jemal A. Cancer statistics, 2014. CA Cancer J Clin. 64, 9–29 (2014). [DOI] [PubMed] [Google Scholar]
  2. Molina J. R. et al. Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clin Proc. 83, 584–594 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alberg A., Ford J. & Samet J. Epidemiology of Lung Cancer:ACCP Evidence-Based Clinical Practice Guidelines. Chest. 132, 29–55S (2007). [DOI] [PubMed] [Google Scholar]
  4. Paesmans M. et al. Prognostic factors for survival in advanced non-small-cell lung cancer: univariate and multivariate analyses including recursive partitioning and amalgamation algorithms in 1,052 patients. The European Lung Cancer Working Party. J Clin Oncol. 13, 1221–1230 (1995). [DOI] [PubMed] [Google Scholar]
  5. Donnem T., Bremnes R. M., Busund L. T., Andersen S. & Pezzella F. Gene expression assays as prognostic and predictive markers in early stage non-small cell lung cancer. J Thorac Dis. 4, 212–213 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Margueron R. & Reinberg D. The polycomb complex PRC2 and its mark in life. Nature. 469, 343–9 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Kleer C. G. et al. EZH2 is a marker of aggressive breast cancer and promotes neoplastic transformation of breast epithelial cells. Proc Natl Acad Sci USA. 100, 11606–11 (2003). [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Varambally S. et al. The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature. 419, 624–9 (2002). [DOI] [PubMed] [Google Scholar]
  9. Matsukawa Y. et al. Expression of the enhancer of zeste homolog 2 is correlated with poor prognosis in human gastric cancer. Cancer Sci. 97, 484–91 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Wang C. G. et al. EZH2 and STAT6 expression profiles are correlated with colorectal cancer stage and prognosis. World J Gastroenterol. 16, 2421–7 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Lu C. et al. Regulation of tumor angiogenesis by EZH2. Cancer Cell. 18, 185–97 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Altman D. G. Systematic reviews of evaluations of prognostic variables. BMJ. 323, 224–228 (2001). [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Parmar M. K., Torri V. & Stewart L. Extracting summary statistics to perform metaanalyses of the published literature for survival endpoints. Stat Med. 17, 2815–2834 (1998). [DOI] [PubMed] [Google Scholar]
  14. Yusuf S. et al. Blockade during and after myocardial infarction: an overview of the randomized trials. Prog Cardiovasc Dis. 27, 335–371 (1985). [DOI] [PubMed] [Google Scholar]
  15. DerSimonian R. & Laird N. Meta-analysis in clinical trials. Controlled Clinical Trials. 7, 177–188 (1986). [DOI] [PubMed] [Google Scholar]
  16. Egger M., Smith G. D. & Schneider M. Bias in meta-analysis detected by a simple, graphical test. BMJ. 315, 629–634 (1997). [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Begg C. B. & Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 50, 1088–1101 (1994). [PubMed] [Google Scholar]
  18. Behrens C. et al. EZH2 protein expression associates with the early pathogenesis, tumor progression, and prognosis of non-small cell lung carcinoma. Clin Cancer Res. 19, 6556–65 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Zhang J. L. et al. Expression of EZH2 in non-small cell lung cancer and its clinical signification. J of inner Mongolia med univ. 35, 12–16 (2013).[in Chinese]. [Google Scholar]
  20. Chen X. et al. High expression of trimethylated histone H3 at lysine 27 predicts better prognosis in non-small cell lung cancer. Int J Oncol. 43, 1467–80 (2013). [DOI] [PubMed] [Google Scholar]
  21. Huqun. et al. Enhancer of zeste homolog 2 is a novel prognostic biomarker in nonsmall cell lung cancer. Cancer. 118, 1599–606 (2012). [DOI] [PubMed] [Google Scholar]
  22. Takawa M. et al. Validation of the histone methyltransferase EZH2 as a therapeutic target for various types of human cancer and as a prognostic marker. Cancer Sci. 102, 1298–305 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kikuchi J. et al. Distinctive expression of the polycomb group proteins Bmi1 polycomb ring finger oncogene and enhancer of zeste homolog 2 in nonsmall cell lung cancers and their clinical and clinicopathologic significance. Cancer. 116, 3015–24 (2010). [DOI] [PubMed] [Google Scholar]
  24. Xu C. et al. Expression of the enhancer of zeste homolog 2 in biopsy specimen predicts chemoresistance and survival in advanced non-small cell lung cancer receiving first-line platinum-based chemotherapy. Lung Cancer. 86, 268–73 (2014). [DOI] [PubMed] [Google Scholar]
  25. Riquelme E. et al. VEGF/VEGFR-2 upregulates EZH2 expression in lung adenocarcinoma cells and EZH2 depletion enhances the response to platinum-based and VEGFR-2-targeted therapy. Clin Cancer Res. 20, 3849–61 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Cao W. et al. EZH2 promotes malignant behaviors via cell cycle dysregulation and its mRNA level associates with prognosis of patient with non-small cell lung cancer. PLoS One. 7, e52984 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Yu J. et al. Integrative genomics analysis reveals silencing of beta-adrenergic signaling by polycomb in prostate cancer. Cancer Cell. 12, 419–31(2007). [DOI] [PubMed] [Google Scholar]
  28. Cao Q. et al. Repression of E-cadherin by the polycomb group protein EZH2 in cancer. Oncogene. 27, 7274–84 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Varambally S. et al. The polycomb group protein EZH2 is involved in progression of prostate cancer. Nature. 419, 624–9 (2002). [DOI] [PubMed] [Google Scholar]
  30. Guo L., Yang T. F., Liang S. C., Guo J. X. & Wang G. Q. Role of EZH2 protein expression in gastric carcinogenesis among Asians: a meta-analysis. Tumor Biol. 35, 6649–6656 (2014). [DOI] [PubMed] [Google Scholar]
  31. Begg C. B. & Berlin J. A. Publication bias: A problem in interpreting medical data. J R Stat Soc A. 151, 419–463 (1988). [Google Scholar]

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES