Development and Validation of a Prognostic Nomogram for Gastric Cancer Based on DNA Methylation-Driven Genes

Background:Recurrence is the leading cause of treatment failure and death in patients with gastric cancer (GC). However, the mechanism underlying GC recurrence remains unclear, and prognostic markers are still lacking.Methods:We analyzed DNA methylation profiles in gastric cancer cases with shorter survival (<1 year) or longer survival (> 3 years), and identified candidate genes associated with GC recurrence. Then, the biological effects of these genes on gastric cancer were studied.Results:A novel gene, magnesium-dependent phosphatase 1 (mdp1), was identified as a candidate gene whose DNA methylation was higher in GC samples from patients with shorter survival and lower in patients with longer survival. MDP1 protein was highly expressed in GC tissues with longer survival time, and also had a tendency to be expressed in highly differentiated GC samples. Forced expression of MDP1 in GC cell line BGC-823 inhibited cell proliferation, whereas the knockdown of MDP1 protein promoted cell growth. Overexpression of MDP1 in BGC-823 cells also enhanced cell senescence and apoptosis. Cytoplasmic kinase protein c-Jun N-terminal kinase (JNK) and signal transducer and activator of transcription 3 (Stat3) were found to mediate the biological function of MDP1.Conclusion:These results suggest that MDP1 protein suppresses the survival of gastric cancer cells and loss of MDP expression may benefit the recurrence of gastric cancer.

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UHPLC-ESI-MS/MS Quantification of Relevant Substrates and Metabolites of the Kynurenine Pathway Present in Serum and Peritoneal Fluid from Gastric Cancer Patients—Method Development and Validation

International Journal of Molecular Sciences ◽

10.3390/ijms22136972 ◽

2021 ◽

Vol 22 (13) ◽

pp. 6972

Author(s):

Ilona Sadok ◽

Katarzyna Jędruchniewicz ◽

Karol Rawicz-Pruszyński ◽

Magdalena Staniszewska

Keyword(s):

Gastric Cancer ◽

Cancer Patients ◽

Peritoneal Fluid ◽

Method Development ◽

Solid Phase ◽

Kynurenine Pathway ◽

Serum Samples ◽

Esi Ms ◽

Gastric Cancer Patients ◽

Development And Validation

Metabolites and enzymes involved in the kynurenine pathway (KP) are highly promising targets for cancer treatment, including gastrointestinal tract diseases. Thus, accurate quantification of these compounds in body fluids becomes increasingly important. The aim of this study was the development and validation of the UHPLC-ESI-MS/MS methods for targeted quantification of biologically important KP substrates (tryptophan and nicotinamide) and metabolites(kynurenines) in samples of serum and peritoneal fluid from gastric cancer patients. The serum samples were simply pretreated with trichloroacetic acid to precipitate proteins. The peritoneal fluid was purified by solid-phase extraction before analysis. Validation was carried out for both matrices independently. Analysis of the samples from gastric cancer patients showed different accumulations of tryptophan and its metabolites in different biofluids of the same patient. The protocols will be used for the evaluation of tryptophan and kynurenines in blood and peritoneal fluid to determine correlation with the clinicopathological status of gastric cancer or the disease’s prognosis.

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A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽

10.3390/genes12060854 ◽

2021 ◽

Vol 12 (6) ◽

pp. 854

Author(s):

Yishu Wang ◽

Lingyun Xu ◽

Dongmei Ai

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Dna Methylation ◽

Regression Model ◽

The Cancer Genome Atlas ◽

Low Rank ◽

Expression Levels ◽

Rank Regression ◽

Prediction Of Prognosis ◽

Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

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