Prognostic importance of RASSF2 expression in patients with gastric cancer who had undergone radical gastrectomy

Deciphering Promoter Hypermethylation of Genes Encoding for RASSF/Hippo Pathway Reveals the Poor Prognostic Factor of RASSF2 Gene Silencing in Colon Cancers

Simple Summary

Colorectal cancer (CRC) is a major public health issue due to its incidence and mortality. Thus, the development of molecular biomarkers is essential to optimize its therapeutic management. Such markers could be identified among the members of the RASSF/Hippo pathway. Indeed, epigenetic alterations are strongly implicated in colorectal carcinogenesis and this pathway is altered in many cancers, mainly by hypermethylation of the promoter of the gene coding for its members. The objectives of the study were to map the hypermethylation of the RASSF/Hippo pathway promoters in a morphologically, clinically, and prognostically well-characterized population of colon cancers. This first report of a whole systematic analysis of the Hippo pathway in colon cancer highlights RASSF2 gene promoter hypermethylation as a worst prognostic factor and a tool to be sought in clinical practice to improve therapeutic management.

Abstract

The aims of this study were to assess the frequency of promoter hypermethylation of the genes encoding the Ras associated domain family (RASSF)/Hippo pathway, as well as the impact on overall (OS) and progression-free survival (PFS) in a single-center retrospective cohort of 229 patients operated on for colon cancers. Hypermethylation status was investigated by methylation-specific PCR on the promoters of the RASSF1/2, STK4/3 (encoding Mammalian Ste20-like protein 1 and 2 (MST1 and 2), respectively), and LATS1/2 genes. Clinicopathological characteristics, recurrence-free survival, and overall survival were analysed. We found the RASSF/Hippo pathway to be highly silenced in colon cancer, and particularly RASSF2 (86%). The other promoters were hypermethylated with a lesser frequency of 16, 3, 1, 10 and 6%, respectively for RASSF1, STK4, STK3, LATS1, and LATS2 genes. As the hypermethylation of one RASSF/Hippo family member was by no means exclusive from the others, 27% of colon cancers displayed the hypermethylation of at least two RASSF/Hippo member promotors. The median overall survival of the cohort was 60.2 months, and the median recurrence-free survival was 46.9 months. Survival analyses showed a significantly poorer overall survival of patients when the RASSF2 promoter was hypermethylated (p = 0.03). The median OS was 53.5 months for patients with colon cancer with a hypermethylated RASSF2 promoter versus still not reached after 80 months follow-up for other patients, upon univariate analysis (HR = 1.86, [95% CI: 1.05–3.3], p < 0.03). Such difference was not significant for relapse-free survival as in multivariate analysis. A logistic regression model showed that RASSF2 hypermethylation was an independent factor. In conclusion, RASSF2 hypermethylation is a frequent event and an independent poor prognostic factor in colon cancer. This biomarker could be investigated in clinical practice.

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data

Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.

Association Between RASSF2 Methylation and Gastric Cancer: A PRISMA-Compliant Systematic Review and Meta-Analysis

RASSF2 is a tumor suppressor gene closely related to gastric cancer. This meta-analysis was designed to assess the quality in the previous studies and establish the value of RASSF2 methylation in the prediction and prognosis of gastric cancer. The eligible literatures with publication deadline of May 3, 2019 were collected from PubMed, EMBASE, CNKI, Wanfang, and CNVIP databases. The correlation between RASSF2 methylation level and gastric cancer was estimated by odds ratio and 95% confidence interval (OR and 95% CI) values. A total of eight articles were included in the study. A total of 517 gastric cancer tissue samples and 517 adjacent nontumor tissue samples were included. The results of the analysis showed that RASSF2 had a significantly higher level of methylation in gastric cancer (OR = 17.56, 95% CI = 7.11-43.35, p-value = 0.009). Meanwhile, we tested whether there was association of RASSF2 methylation with tumor metastasis, and we also analyzed whether there was a gender difference in RASSF2 methylation. However, our results showed no statistical significance of the two aforementioned tests (p > 0.1). Our study suggested that RASSF2 methylation could predict the risk of gastric cancer. However, it might not be feasible for the prediction of tumor metastasis.