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

Zhou, Kena; Cai, Congbo; He, Yi; Zhou, Cong; Zhao, Shuangying; Ding, Xiaoyun; Duan, Shiwei

doi:10.1089/dna.2019.4922

Cited by 2 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may promote apoptosis and cell cycle arrest, stabilizing STK3/MST2 by protecting it from proteasome degradation ( Cooper et al, 2009 ). Meta-analysis has shown that RASSF2 is significantly more methylated in GC, which can predict the risk of GC ( Zhou et al, 2019c ). Neuronal acetylcholine receptors are homo- or heteropentameric complexes composed of homologous α and β subunits, of which the CHRNB2 gene encodes one of several β subunits.…”

Section: Discussionmentioning

confidence: 99%

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Liu

Wang

et al. 2021

Front. Genet.

View full text Add to dashboard Cite

Background: The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms.Methods: In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes.Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways.Conclusion: Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.

show abstract

Section: Discussionmentioning

confidence: 99%

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Liu

Wang

et al. 2021

Front. Genet.

View full text Add to dashboard Cite

show abstract

Advancements in the clinical application of gene methylation for early cancer detection

Xu,

Guo,

Liu

et al. 2024

Front. Epigenet. Epigenom.

View full text Add to dashboard Cite

This review provides an overview of common assays used to screen for gene methylation and early biomarkers of methylation in various cancers. DNA methylation, one of the most well-studied epigenetic modifications, plays a crucial role in normal cell and tissue development. It is increasingly utilized as a biomarker for early cancer and precancerous lesion detection. In this review, we describe common methods associated with gene methylation, including bisulfite sequencing PCR (BSP), pyrosequencing technology (PYR), methylation-specific polymerase chain reaction (MS-PCR/MSP), methylation-sensitive high-resolution melting (MS-HRM), methylation sensitive single nucleotide primer extension (MS-SnuPE), Epityper, Droplet digital PCR (ddPCR), methylation-sensitive restriction enzyme (MSRE) analysis, COBRA and PacBio SMRT sequencing. Additionally, we summarize methylation markers and their sample types for early cancer screening, focusing on colorectal cancer, hepatocellular carcinoma, gastric cancer, pancreatic cancer, esophageal cancer (digestive system), lung cancer (respiratory system), breast cancer, ovarian cancer, cervical cancer (female reproductive system), bladder cancer, and prostate cancer (urinary system). Furthermore, we discuss the recent detection of methylation biomarkers in clinical samples such as blood, urine, sputum, feces, and tissues. The aim of this review is to summarize early methylation biomarkers that are expected or have already been clinically applied. For future large-scale studies or the integration of available methylome level data, the discovery of sufficiently sensitive clinical biomarkers is essential.

show abstract

Association BetweenRASSF2Methylation and Gastric Cancer: A PRISMA-Compliant Systematic Review and Meta-Analysis

Cited by 2 publications

References 31 publications

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Advancements in the clinical application of gene methylation for early cancer detection

Contact Info

Product

Resources

About