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Expression and prognostic analyses of HDACs in human gastric cancer based on bioinformatic analysis.

ABSTRACT:

Abstract

Gastric cancer (GC) is a common cancerous tumor, and is the third leading cause of cancer mortality worldwide. Although comprehensive therapies of GC have been widely used in clinical set ups, advanced gastric cancer carries is characterized by poor prognosis, probably due to lack of effective prognostic biomarkers. Mammalian histone deacetylase family, histone deacetylases (HDACs), play significant roles in initiation and progression of tumors. Aberrant expression of HDACs is reported in many cancer types including gastric cancer, and may serve as candidate biomarkers or therapeutic targets for GC patients.Gene Expression Profiling Interactive Analysis was used to explore mRNA levels of HDACs in GC. Kaplan-Meier plotter was used to determine the prognostic value of HDACs mRNA expression in GC. Genomic profiles including mutations of HDACs were retrieved from cBioPortal webserver. A protein-protein interaction network was constructed using STRING database. GeneMANIA was used to retrieve additional genes or proteins related to HDACs. R software was used for functional enrichment analyses.Analysis of mRNA levels of HDAC1/2/4/8/9 showed that they were upregulated in GC tissues, whereas HDAC6/10 was downregulated in GC tissues. Aberrant expression of HDAC1/3/4/5/6/7/8/10/11 was all correlated with prognosis in GC. In addition, expression levels of HDACs were correlated with different Lauren classifications, and clinical stages, lymph node status, treatment, and human epidermal growth factor receptor 2 status in GC.The findings of this study showed that HDAC members are potential biomarkers for diagnosis or prognosis of gastric cancer. However, further studies should be conducted to validate these findings.

SUBMITTER: Chen L

PROVIDER: S-EPMC8270587 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The association between the expression of Lysyl oxidase (LOX) and its clinicopathological parameters and prognosis in patients with gastric cancer (GC) is still disputed. We performed this meta-analysis and bioinformatics analysis to clarify the relationship between the expression and methylation level of LOX with its clinicopathological parameters and prognostic value. We applied odds ratios with a 95% confidence interval to study the associations between LOX expression and clinicopathological parameters and overall survival (OS) in GC patients. In addition, association analysis of promoter methylation levels and expression of LOX with its prognostic value was performed using the Cancer Genome Atlas (TCGA) and four Gene Expression Omnibus (GEO) datasets. The PRISMA 2020 checklist was used to guide the data extraction and analysis. This meta-analysis includes seven clinical studies with a total of 1435 GC patients. LOX expression was related to lymph node metastasis and tumor distant metastasis in GC patients, but not to gender, tumor differentiation, Lauren classification, or tumor depth of invasion. Patients with GC grouped in high-expression of LOX had a much worse OS than those in low-expression. In addition, TCGA and four GEO datasets with 1279 samples were included in the bioinformatics analysis. The bioinformatics analysis showed that patients with high LOX levels had poor OS; low levels of methylation at some cg sites in the LOX gene were strongly related to poor OS and PFS; and methylation levels of LOX are negatively correlated with advanced tumor stage. The conclusion from comprehensive DNA methylation and gene expression analysis supports LOX as a specific diagnostic and prognosis biomarker in GC. LOX expression was related to lymph node metastasis, tumor distant metastasis and poor prognosis in GC. Low methylation levels were related to advanced tumor stage and poor prognosis in GC. Integrative analysis supports LOX as a specific diagnostic and prognosis biomarker in GC.

Project description:PurposeGastric cancer (GC) is a common type of cancer worldwide. It can relapse and metastasize even after standard treatment; therefore, it has a poor prognosis. Moreover, sensitive biomarkers for prognosis prediction in GC are lacking. In this study, using a bioinformatics approach, we aimed to examine the value of DAZ Interacting Protein 1 (DZIP1) as a prognostic predictor and therapeutic target in GC.MethodsWe explored the clinical relevance, function, and molecular role of DZIP1 in GC using MethSurv, cBioPortal, TIMER, Gene Expression Profiling Interactive Analysis, IMEx, ONCOMINE, MEXPRESS, and EWAS Atlas databases. The GSE118919 dataset was used to plot receiver operating characteristic curves. Using The Cancer Genome Atlas, we developed a Cox regression model and assessed the clinical significance of DZIPs. In addition, we used the "xCELL" algorithm to make reliable immune infiltration estimations. Western blot and immunohistochemistry were used to examine protein expression. The results were visualized with the 'ggplot2' and "circlize" packages.ResultsIn GC patients, DZIP1 was over-expressed at both the mRNA and protein levels. High levels of DZIP1 were found to be associated with poor survival in patients with GC. Our results indicated that DZIP1 could be involved in multiple cancer-related pathways such as the PI3K-Akt signaling pathway, WNT signaling pathway, and RAS signaling pathway, and its expression was correlated with the infiltration of activated myeloid dendritic cells, naive CD4+ T cells, and naive CD8+ T cells. Furthermore, we found that mutations in DZIP1 were correlated with a good prognosis in GC patients. Finally, we demonstrated a correlation between hypomethylation of the DZIP1 gene promoter and a poor prognosis in GC.ConclusionThis study is the first to demonstrate a significant correlation between high levels of DZIP1 and a poor prognosis in GC patients. Our results clarify multiple potential mechanisms that could contribute to this correlation and may thus provide novel insights into the clinical diagnosis and treatment of GC.

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Expression and prognostic analyses of HDACs in human gastric cancer based on bioinformatic analysis.

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