The relationship between RASSF1A gene promoter methylation and the susceptibility and prognosis of melanoma: A meta-analysis and bioinformatics

Abstract

Background: The function of the tumor suppressor gene RASSF1A in cancer cells has been detailed in many studies. However, due to the methylation of its promoter, the expression of RASSF1A is missing in most cancers. In the literature, we found that the conclusion regarding the relationship between RASSF1A gene promoter methylation and the susceptibility and prognosis of melanoma was not unified. This study adopts the use of a meta-analysis and bioinformatics to explore the relationship between RASSF1A gene promoter methylation and the susceptibility and prognosis of melanoma.

Methods: Data on melanoma susceptibility were downloaded from the PubMed, Cochrane Library, Web of Science and Google Scholar databases, which were analyzed via a meta-analysis. The effect sizes were estimated by measuring an odds ratio (OR) with a 95% confidence interval (CI). We also used a chi-squared-based Q test to examine the between-study heterogeneity, and used funnel plots to evaluate publication bias. The data on melanoma prognosis, which were analyzed by bioinformatics methods, were downloaded from The Cancer Genome Atlas (TCGA) project. The effect sizes were estimated by measuring the hazard ratios (HRs) with a 95% confidence interval (CI).

Results: Our meta-analysis included 10 articles. We found that RASSF1A gene promoter methylation was closely related to melanoma susceptibility (OR = 12.67, 95% CI: 6.16 ∼ 26.05, z = 6.90, P<0.0001 according to a fixed effects model and OR = 9.25, 95% CI: 4.37 ∼ 19.54, z = 5.82, P<0.0001 according to a random effects model). The results of the meta-analysis did not reveal any heterogeneity (tau2 = 0.00; H = 1 [1; 1.55]; I2 = 0% [0%; 58.6%], P = 0.5158) or publication bias (t = 0.87, P = 0.4073 by Egger's test; Z = 0.45, P = 0.6547 by Begg's test); therefore, we believe that the results of our meta-analysis were more reliable. To explore the relationship between RASSF1A gene methylation, the prognosis of melanoma and the clinical features of this cancer type, we used the melanoma DNA methylation data and clinical data from TCGA project. We found that RASSF1A gene promoter methylation and melanoma prognosis did not demonstrate any relationship (HR was 0.94 (95% CI = [0.69; 1.27], P = 0.694) with disease-free survival and 0.74 (95% CI = [0.53; 1.05], P = 0.106) for overall survival), and no significant difference was observed between RASSF1A gene promoter methylation and the clinical-pathological features of melanoma.

Conclusions: In conclusion, the meta-analysis of the data in these articles provides strong evidence that the methylation status of the RASSF1A gene promoter was strongly related to melanoma susceptibility. Our bioinformatics analysis revealed no significant difference between RASSF1A gene promoter methylation and the prognosis and clinical-pathological features of melanoma.

Fig 1. Flow chart of study identification.

Fig 2. Combined estimates of the association between RASSF1A gene promoter methylation and melanoma susceptibility with a forest plot.

(A), Meta-analysis of the association between RASSF1A gene promoter methylation and melanoma susceptibility by a random effects model and a fixed effects model. (B), Subgroup meta-analysis based on race by a random effects model and a fixed effects model. (C), Subgroup meta-analysis based on different methylation detection methods by a random effects model and a fixed effects model. (D), Subgroup meta-analysis based on different primer types by a random effects model and a fixed effects model.

Fig 3. Funnel plot for publication bias test and sensitivity analysis of the summary odds ratio coefficients on the relationship between RASSF1A gene promoter methylation and melanoma.

Fig 4. The relationship of RASSF1A gene promoter methylation and the survival curve of patients with skin cutaneous melanoma from TCGA data.

(A, B), Association of patient survival and RASSF1A gene methylation status based on the Kaplan-Meier method.