Association between P(16INK4a) promoter methylation and non-small cell lung cancer: a meta-analysis

Abstract

Background: Aberrant methylation of CpG islands acquired in tumor cells in promoter regions plays an important role in carcinogenesis. Accumulated evidence demonstrates P(16INK4a) gene promoter hypermethylation is involved in non-small cell lung carcinoma (NSCLC), indicating it may be a potential biomarker for this disease. The aim of this study is to evaluate the frequency of P(16INK4a) gene promoter methylation between cancer tissue and autologous controls by summarizing published studies.

Methods: By searching Medline, EMBSE and CNKI databases, the open published studies about P(16INK4a) gene promoter methylation and NSCLC were identified using a systematic search strategy. The pooled odds of P(16INK4A) promoter methylation in lung cancer tissue versus autologous controls were calculated by meta-analysis method.

Results: Thirty-four studies, including 2 652 NSCLC patients with 5 175 samples were included in this meta-analysis. Generally, the frequency of P(16INK4A) promoter methylation ranged from 17% to 80% (median 44%) in the lung cancer tissue and 0 to 80% (median 15%) in the autologous controls, which indicated the methylation frequency in cancer tissue was much higher than that in autologous samples. We also find a strong and significant correlation between tumor tissue and autologous controls of P(16INK4A) promoter methylation frequency across studies (Correlation coefficient 0.71, 95% CI:0.51-0.83, P<0.0001). And the pooled odds ratio of P(16INK4A) promoter methylation in cancer tissue was 3.45 (95% CI: 2.63-4.54) compared to controls under random-effect model.

Conclusion: Frequency of P(16INK4a) promoter methylation in cancer tissue was much higher than that in autologous controls, indicating promoter methylation plays an important role in carcinogenesis of the NSCLC. Strong and significant correlation between tumor tissue and autologous samples of P(16INK4A) promoter methylation demonstrated a promising biomarker for NSCLC.

Figure 1. PRISMA flowchart of the literature search strategy for systematic review.

(Data from some studies was used more than once, as they reported data in multiple controls.).

Figure 2. Forest plot of P^16INK4A promoter methylation in cancer tissue versus autologous controls.

The squares and horizontal lines represent the study-specific OR and 95% CI. The area of the squares reflects the weight (inverse of the variance). The diamond represents the pooled OR and 95% CI.

Figure 3. The sensitivity analysis by omitting a single study under the random-effect method.

The circles and horizontal lines represents the pooled OR and 95% CI by omitting a certain study. The area of the circles reflects the weight (by sample size). The diamond represents the pooled OR and 95% CI by including all of studies.

Figure 4. Methylation frequency in tumor tissue versus autologous controls.

Figure 5. Correlation of P^16INK4A promoter methylation between tumor tissue and autolougs clinical samples (A:plasm; B:BALF/sputum).

Figure 6. Begg’s funnel plot for assessment of publication bias.

Each hollow circle represents a separate study for the indicated association. The area of the hollow circle reflects the weight (inverse of the variance). Horizontal line stands for the mean magnitude of the effect.