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.
There is considerable heterogeneity in the genomic drivers of lung adenocarcinoma, which has a dismal prognosis. Bioinformatics analysis was performed on lung adenocarcinoma (LUAD) datasets to establish a multi-autophagy gene model to predict patient prognosis. LUAD data were downloaded from The Cancer Genome Atlas (TCGA) database as a training set to construct a LUAD prognostic model. According to the risk score, a Kaplan-Meier cumulative curve was plotted to evaluate the prognostic value. Furthermore, a nomogram was established to predict the three-year and five-year survival of patients with LUAD based on their prognostic characteristics. Two genes (ITGB1 and EIF2AK3) were identified in the autophagy-related prognostic model, and the multivariate Cox proportional risk model showed that risk score was an independent predictor of prognosis in LUAD patients (HR=3.3, 95%CI= 2.3 to 4.6, P< 0.0001). The Kaplan-Meier cumulative curve showed that low-risk patients had significantly better overall (P<0.0001). The validation dataset GSE68465 further confirmed the nomogram's robust ability to assess the prognosis of LUAD patients. A prognosis model of autophagy-related genes based on a LUAD dataset was constructed and exhibited diagnostic value in the prognosis of LUAD patients. Moreover, real-time qPCR confirmed the expression patterns of EIF2AK3 and ITGB1 in LUAD cell lines. Two key autophagy-related genes have been suggested as prognostic markers for lung adenocarcinoma.
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