BackgroundB7 homolog 1 (B7-H1) overexpression on tumor cells is an important mechanism of immune evasion in gastric cancer (GC). Elucidation of the regulation of B7-H1 expression is urgently required to guide B7-H1-targeted cancer therapy. Interferon gamma (IFN-γ) is thought to be the main driving force behind B7-H1 expression, and epigenetic factors including histone acetylation are recently linked to the process. Here, we investigated the potential role of histone deacetylase (HDAC) in IFN-γ-induced B7-H1 expression in GC. The effect of Vorinostat (SAHA), a small molecular inhibitor of HDAC, on tumor growth and B7-H1 expression in a mouse GC model was also evaluated.ResultsRNA-seq data from The Cancer Genome Atlas revealed that expression of B7-H1, HDAC1–3, 6–8, and 10 and SIRT1, 3, 5, and 6 was higher, and expression of HDAC5 and SIRT4 was lower in GC compared to that in normal gastric tissues; that HDAC3 and HDAC1 expression level significantly correlated with B7-H1 in GC with a respective r value of 0.42 (p < 0.001) and 0.21 (p < 0.001). HDAC inhibitor (Trichostatin A, SAHA, and sodium butyrate) pretreatment suppressed IFN-γ-induced B7-H1 expression on HGC-27 cells. HDAC1 and HDAC3 gene knockdown had the same effect. SAHA pretreatment or HDAC knockdown resulted in impaired IFN-γ signaling, demonstrated by the reduction of JAK2, p-JAK1, p-JAK2, and p-STAT1 expression and inefficient STAT1 nuclear translocation. Furthermore, SAHA pretreatment compromised IFN-γ-induced upregulation of histone H3 lysine 9 acetylation level in B7-H1 gene promoter. In the grafted mouse GC model, SAHA treatment suppressed tumor growth, inhibited B7-H1 expression, and elevated the percentage of tumor-infiltrating CD8+ T cells.ConclusionHDAC is indispensable for IFN-γ-induced B7-H1 in GC. The study suggests the possibility of targeting B7-H1 using small molecular HDAC inhibitors for cancer treatment.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0589-6) contains supplementary material, which is available to authorized users.

miRNA-gene axes have been reported to serve an important role in the carcinogenesis of pancreatic cancer (PC). The aim of the present study was to systematically identity the microRNA signature and hub molecules, as well as hub miRNA-gene axes, and to explore the potential biomarkers and mechanisms associated with the carcinogenesis of PC. Eleven microRNA profile datasets were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) and ArrayExpress databases, and a meta-analysis was performed to identify the differentially expressed miRNAs (DEMs) between tumor tissue and normal tissue. Subsequently, a diagnostic regression model was constructed to identify PC based on The Cancer Genome Atlas (TCGA) miRNA sequence data by using the least absolute shrinkage and selection operator (LASSO) method. In addition, GSE41368 was downloaded, and a weighted gene co-expression network analysis (WGCNA) was performed to obtain the gene module associated with carcinogenesis by using the TCGAbiolinks and WGCNA packages, respectively. Finally, miRNA-gene networks were constructed and visualized using Cytoscape software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses based on the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A total of 14 DEMs were identified, and a 5-microRNA-based score generated by the LASSO regression model provided a high accuracy for identifying PC [area under the curve (AUC)=0.918]. In addition, 44 miRNA-mRNA interactions were constructed, and 4 hub genes were screened on the basis of the above bioinformatic tools and databases. Furthermore, 14 biological process (BP) functions and 6 KEGG pathways were identified according to gene set enrichment analysis (GSEA). In summary, the present study applied integrated bioinformatics approaches to generate a holistic view of PC, thereby providing a basis for further clinical application of the 5-miRNA signature and the identified hub molecules, as well as the miRNA-gene axes, which could serve as diagnostic markers and potential treatment targets.

Increasing evidence suggested DNA methylation may serve as potential prognostic biomarkers; however, few related DNA methylation signatures have been established for prediction of lung cancer prognosis. We aimed at developing DNA methylation signature to improve prognosis prediction of stage I lung adenocarcinoma (LUAD). A total of 268 stage I LUAD patients from the Cancer Genome Atlas (TCGA) database were included. These patients were separated into training and internal validation datasets. GSE39279 was used as an external validation set. A 13‐DNA methylation signature was identified to be crucially relevant to the relapse‐free survival (RFS) of patients with stage I LUAD by the univariate Cox proportional hazard analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazard analysis in the training dataset. The Kaplan‐Meier analysis indicated that the 13‐DNA methylation signature could significantly distinguish the high‐ and low‐risk patients in entire TCGA dataset, internal validation and external validation datasets. The receiver operating characteristic (ROC) analysis further verified that the 13‐DNA methylation signature had a better value to predict the RFS of stage I LUAD patients in internal validation, external validation and entire TCGA datasets. In addition, a nomogram combining methylomic risk scores with other clinicopathological factors was performed and the result suggested the good predictive value of the nomogram. In conclusion, we successfully built a DNA methylation‐associated nomogram, enabling prediction of the RFS of patients with stage I LUAD.