DNA methylation data-based molecular subtype classification and prediction in patients with gastric cancer

Abstract Background CIMP (CpG island methylator phenotype) is an epigenetic molecular subtype, observed in multiple malignancies and associated with the epigenetic silencing of tumor suppressors. Currently, for most cancers including gastric cancer (GC), mechanisms underlying CIMP remain poorly understood. We sought to discover molecular contributors to CIMP in GC, by performing global DNA methylation, gene expression, and proteomics profiling across 14 gastric cell lines, followed by similar integrative analysis in 50 GC cell lines and 467 primary GCs. Results We identify the cystathionine beta-synthase enzyme (CBS) as a highly recurrent target of epigenetic silencing in CIMP GC. Likewise, we show that CBS epimutations are significantly associated with CIMP in various other cancers, occurring even in premalignant gastroesophageal conditions and longitudinally linked to clinical persistence. Of note, CRISPR deletion of CBS in normal gastric epithelial cells induces widespread DNA methylation changes that overlap with primary GC CIMP patterns. Reflecting its metabolic role as a gatekeeper interlinking the methionine and homocysteine cycles, CBS loss in vitro also causes reductions in the anti-inflammatory gasotransmitter hydrogen sulfide (H2S), with concomitant increase in NF-κB activity. In a murine genetic model of CBS deficiency, preliminary data indicate upregulated immune-mediated transcriptional signatures in the stomach. Conclusions Our results implicate CBS as a bi-faceted modifier of aberrant DNA methylation and inflammation in GC and highlights H2S donors as a potential new therapy for CBS-silenced lesions.

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Accounting for tumor purity improves cancer subtype classification from DNA methylation data

Bioinformatics ◽

10.1093/bioinformatics/btx303 ◽

2017 ◽

Vol 33 (17) ◽

pp. 2651-2657 ◽

Cited By ~ 10

Author(s):

Weiwei Zhang ◽

Hao Feng ◽

Hao Wu ◽

Xiaoqi Zheng

Keyword(s):

Dna Methylation ◽

Methylation Data ◽

Subtype Classification ◽

Tumor Purity ◽

Cancer Subtype

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Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia

BMC Bioinformatics ◽

10.1186/1471-2105-16-s4-s5 ◽

2015 ◽

Vol 16 (Suppl 4) ◽

pp. S5 ◽

Cited By ~ 11

Author(s):

Erdogan Taskesen ◽

Sepideh Babaei ◽

Marcel MJ Reinders ◽

Jeroen de Ridder

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Acute Myeloid Leukemia ◽

Myeloid Leukemia ◽

Molecular Subtype ◽

Subtype Classification ◽

Acute Myeloid

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

SSRN Electronic Journal ◽

10.2139/ssrn.3398525 ◽

2019 ◽

Author(s):

Yi Bai ◽

Chunlian Wei ◽

Yuxin Zhong ◽

Junyu Long ◽

Shan Huang ◽

...

Keyword(s):

Gastric Cancer ◽

Dna Methylation ◽

Development And Validation

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Magnesium-dependent Phosphatase (MDP) 1 is a Potential Suppressor of Gastric Cancer

Current Cancer Drug Targets ◽

10.2174/1568009619666190620112546 ◽

2019 ◽

Vol 19 (10) ◽

pp. 817-827

Author(s):

Jianbo Zhu ◽

Lijuan Deng ◽

Baozhen Chen ◽

Wenqing Huang ◽

Xiandong Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Dna Methylation ◽

Cell Proliferation ◽

Protein C ◽

Biological Function ◽

Prognostic Markers ◽

Biological Effects ◽

Signal Transducer ◽

Gastric Cancer Cells ◽

Transcription 3

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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Classifying gastric cancer using FLORA reveals clinically relevant molecular subtypes and highlights LINC01614 as a biomarker for patient prognosis

Oncogene ◽

10.1038/s41388-021-01743-3 ◽

2021 ◽

Author(s):

Yiyun Chen ◽

Wing Yin Cheng ◽

Hongyu Shi ◽

Shengshuo Huang ◽

Huarong Chen ◽

...

Keyword(s):

Gastric Cancer ◽

Noncoding Rna ◽

Molecular Subtype ◽

Sequencing Data ◽

Cell Growth And Migration ◽

Protein Coding ◽

Over Expression ◽

And Migration ◽

Patient Prognosis ◽

Subtype 3

AbstractMolecular-based classifications of gastric cancer (GC) were recently proposed, but few of them robustly predict clinical outcomes. While mutation and expression signature of protein-coding genes were used in previous molecular subtyping methods, the noncoding genome in GC remains largely unexplored. Here, we developed the fast long-noncoding RNA analysis (FLORA) method to study RNA sequencing data of GC cases, and prioritized tumor-specific long-noncoding RNAs (lncRNAs) by integrating clinical and multi-omic data. We uncovered 1235 tumor-specific lncRNAs, based on which three subtypes were identified. The lncRNA-based subtype 3 (L3) represented a subgroup of intestinal GC with worse survival, characterized by prevalent TP53 mutations, chromatin instability, hypomethylation, and over-expression of oncogenic lncRNAs. In contrast, the lncRNA-based subtype 1 (L1) has the best survival outcome, while LINC01614 expression further segregated a subgroup of L1 cases with worse survival and increased chance of developing distal metastasis. We demonstrated that LINC01614 over-expression is an independent prognostic factor in L1 and network-based functional prediction implicated its relevance to cell migration. Over-expression and CRISPR-Cas9-guided knockout experiments further validated the functions of LINC01614 in promoting GC cell growth and migration. Altogether, we proposed a lncRNA-based molecular subtype of GC that robustly predicts patient survival and validated LINC01614 as an oncogenic lncRNA that promotes GC proliferation and migration.

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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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