Abstract
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.
Highlights
According to the statistics released by the World Health Organization in 2018, the incidence and mortality rate of gastric cancer (GC) ranked fifth and third, respectively, among cancers worldwide
Yielded a sensitivity of 99.1% and specificity of 81.5% samples (Figure 3A) and a sensitivity of 87.2% and specificity of 63.8% in the validation dataset (Figure 3B). We demonstrated this model could differentiate GC from normal samples both in the training dataset (AUC 0.994) and the validation dataset (AUC 0.829) (Figures 3C,D)
Unsupervised hierarchical clustering of these five markers distinguished GC from normal samples with high specificity and sensitivity (Figures 3E,F). These results indicated that the DNAm diagnostic model could be a significant tool for distinguishing GC from normal samples
Summary
According to the statistics released by the World Health Organization in 2018, the incidence and mortality rate of gastric cancer (GC) ranked fifth and third, respectively, among cancers worldwide. Radiotherapy, chemotherapy, molecular targeting, and immunotherapy have improved overall prognosis, diagnosis of GC is often delayed, resulting in unsatisfactory outcomes (Bang et al, 2017; Cats et al, 2018; Sundar et al, 2019). It is, urgent to explore effective biomarkers for early diagnosis and prognosis prediction of GC. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms
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