Comprehensive analysis of histone modification‑associated genes on differential gene expression and prognosis in gastric cancer

Meng, Xiangyu; Zhao, Yan; Liu, Jingwei; Wang, Lu; Dong, Zhe; Zhang, Tao; Gu, Xiaohu; Zheng, Zhichao

doi:10.3892/etm.2019.7808

Cited by 15 publications

(12 citation statements)

References 69 publications

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“…Hence, during dynamic packaging, maintaining stable and ordered chromatin is vital to ensure normal cellular homeostasis. Factors influencing the number of chromatin-associated cellular events, including transcription, warped histones, and DNA, are subject to covalent posttranslational modifications (50). In the present study, a comparative analysis of histone modifications in tumor and normal tissues was conducted, which revealed that the DEGs were primarily regulated by H3K27me3 in several cancer cell lines.…”

Section: Discussionmentioning

confidence: 85%

An integrated analysis of DNA promoter methylation, microRNA regulation, and gene expression in gastric adenocarcinoma

et al. 2021

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Background: Gastric adenocarcinoma (GAC), a common type of gastric cancer, poses a significant public health threat worldwide. This study aimed to determine the transcriptional regulatory mechanisms of GAC.Methods: HTSeq-FPKM raw data were obtained from The Cancer Genome Atlas Stomach Adenocarcinoma data collection. Subsequently, the limma package in R was used to identify differentially expressed genes (DEGs). Differentially methylated genes (DMGs), DEGs, and differentially expressed microRNAs (miRNAs) in normal, and tumor tissues of the same patients were screened and compared using R software tools. A functional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) for various DEGs, DMGs, promoter methylation, and miRNAs. DEG-specific methylation and transcription factors were analyzed using ENCODE ChIP-seq.Results: DEGs were centrally modified by the histone trimethylation of lysine 27 on histone H3 (H3K27me3). Upstream transcription factors of DEGs were enriched in different ChIP-seq clusters, such as Forkhead Box M1, E2F Transcription Factor 4, and suppressor of zest 12. Integrated regulatory networks of DEGs, promoter methylation, and miRNAs were constructed. Two miRNAs (hsa-mir-1 and hsa-mir-133a) and four DEGs (A disintegrin and metalloproteinase domain 12, transcription factor AP-2 alpha, solute carrier family 5 member 7, and cadherin 19) separately played important roles in the integrated regulatory network. Therefore, these DEGs, DMGs, promoter methylation, and miRNAs may play an important role in GAC pathogenesis. Conclusions:In summary, the present study results provide insights into the oncogenesis and progression of GAC, thus accelerating the development of novel targeted GAC therapies.

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Section: Discussionmentioning

confidence: 85%

An integrated analysis of DNA promoter methylation, microRNA regulation, and gene expression in gastric adenocarcinoma

et al. 2021

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“…However, several studies noticed that the PRDM16 gene was hypermethylated and downregulated in lung cancer ( Table 4 ) [ 48 ], [ 49 ], [ 50 ]. The CLASP2 gene showed differential expression levels in lung, gastric, and bladder cancers [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

“…Promotor hypermethylation and downregulated expression of the PRDM16 gene was observed in lung cancer [ 49 ]. In gastric cancer, decreased PRDM16 expression was associated with an unfavorable prognosis [ 50 ]. Methylation status of the CLASP2 gene has not been studied; however, the gene upregulation was detected in bladder cancer [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

Differentially methylated CpG sites associated with the high-risk group of prostate cancer

Kobelyatskaya

Pudova

Fedorova

et al. 2020

Journal of Integrative Bioinformatics

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Prostate cancer (PC) is one of the most common and socially significant oncological diseases among men. Bioinformatic analysis of omics data allows identifying molecular genetic changes associated with the disease development, as well as markers of prognosis and response to therapy. Alterations in DNA methylation and histone modification profiles widely occur in malignant tumors. In this study, we analyzed changes in DNA methylation in three groups of PC patients based on data from The Cancer Genome Atlas project (TCGA, https://portal.gdc.cancer.gov): (1) high- and intermediate-risk of the tumor progression, (2) favorable and unfavorable prognoses within the high-risk group, and (3) TMPRSS2-ERG-positive (tumors with TMPRSS2-ERG fusion transcript) and TMPRSS2-ERG-free cases within the high-risk group. We found eight CpG sites (cg07548607, cg13533340, cg16643088, cg18467168, cg23324953, cg23753247, cg25773620, and cg27148952) hypermethylated in the high-risk group compared with the intermediate-risk group of PC. Seven differentially methylated CpG sites (cg00063748, cg06834698, cg18607127, cg25273707, cg01704198, cg02067712, and cg02157224) were associated with unfavorable prognosis within the high-risk group. Six CpG sites (cg01138171, cg14060519, cg19570244, cg24492886, cg25605277, and cg26228280) were hypomethylated in TMPRSS2-ERG-positive PC compared to TMPRSS2-ERG-negative tumors within the high-risk group. The CpG sites were localized, predominantly, in regulatory genome regions belonging to promoters of the following genes: ARHGEF4, C6orf141, C8orf86, CLASP2, CSRNP1, GDA, GSX1, IQSEC1, MYOF, OR10A3, PLCD1, PLEC1, PRDM16, PTAFR, RP11-844P9.2, SCYL3, VPS13D, WT1, and ZSWIM2. For these genes, analysis of differential expression and its correlation with CpG site methylation (β-value level) was also performed. In addition, STK33 and PLCD1 had similar changes in colorectal cancer. As for the CSRNP1, the ARHGEF4, and the WT1 genes, misregulated expression levels were mentioned in lung, liver, pancreatic and androgen-independent prostate cancer. The potential impact of changed methylation on the mRNA level was determined for the CSRNP1, STK33, PLCD1, ARHGEF4, WT1, SCYL3, and VPS13D genes. The above CpG sites could be considered as potential prognostic markers of the high-risk group of PC.

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“…The impact of AS events on the prognosis of patients was analyzed, and a survival prediction model was constructed ( Zhang et al, 2019 ). Because the driving factors of tumorigenesis and development are generally differentially expressed in tumors and normal tissues ( Meng et al, 2019 ), this article is limited because the data of adjacent normal tissues were not included in the study cohort. Therefore, we analyzed the differences in AS events between tumor tissues and adjacent normal tissues in our study and performed prognostic analyses based on DEAS events.…”

Section: Discussionmentioning

confidence: 99%

Correlations Between the Characteristics of Alternative Splicing Events, Prognosis, and the Immune Microenvironment in Breast Cancer

Deng

Zhao

et al. 2021

Front. Genet.

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ObjectiveAlternative splicing (AS) is the mechanism by which a few genes encode numerous proteins, and it redefines the concept of gene expression regulation. Recent studies showed that dysregulation of AS was an important cause of tumorigenesis and microenvironment formation. Therefore, we performed a systematic analysis to examine the role of AS in breast cancer (Breast Cancer, BrCa) progression.MethodsThe present study included 993 BrCa patients from The Cancer Genome Atlas (TCGA) database in the genome-wide analysis of AS events. We used differential and prognostic analyses and found differentially expressed alternative splicing (DEAS) events and independent prognostic factors related to patients’ overall survival (OS) and disease-free survival (DFS). We divided the patients into two groups based on these AS events and analyzed their clinical features, molecular subtyping and immune characteristics. We also constructed a splicing factor (SF) regulation network for key AS events and verified the existence of AS events in tissue samples using real-time quantitative PCR.ResultsA total of 678 AS events were identified as differentially expressed, of which 13 and 10 AS events were independent prognostic factors of patients’ OS and DFS, respectively. Unsupervised clustering analysis based on these prognostic factors indicated that the Cluster 1 group had a better prognosis and more immune cell infiltration. SFs were significantly related to the expression of AS events, and AA-RPS21 was significantly upregulated in tumors.ConclusionAlternative splicing expands the mechanism of breast cancer progression from a new perspective. Notably, alternative splicing may affect the patient’s prognosis by affecting the infiltration of immune cells. Our research provides important guidance for subsequent studies of AS in breast cancer.

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Comprehensive analysis of histone modification‑associated genes on differential gene expression and prognosis in gastric cancer

Cited by 15 publications

References 69 publications

An integrated analysis of DNA promoter methylation, microRNA regulation, and gene expression in gastric adenocarcinoma

An integrated analysis of DNA promoter methylation, microRNA regulation, and gene expression in gastric adenocarcinoma

Differentially methylated CpG sites associated with the high-risk group of prostate cancer

Correlations Between the Characteristics of Alternative Splicing Events, Prognosis, and the Immune Microenvironment in Breast Cancer

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