Refametinib

DNA methyltransferase 3A (DNMT3A) has been associated with the occurrence or progression of various tumors, including gastric cancer. However, the role of DNMT3A in the efficacy of immune-cell infiltration in the tumor microenvironment and immunotherapy in gastric cancer remains less explored. DNMT3A expression level was analyzed using TIMER 2.0, Sangerbox 3.0, and The Cancer Genome Atlas database and further verified by immunohistochemical staining and RT-qPCR. The UALCAN, chi-square test, and Kaplan–Meier plotter databases were performed to assess the correlation of DNMT3A with clinicopathological characteristics and prognosis. The GeneMANIA database, STRING database, and R package were used to construct a DNMT3A co-expression gene network. Gene set enrichment analysis was used to identify the signaling pathways related to DNMT3A expression. The correlations between DNMT3A and cancer immune infiltrates were investigated using TIMER 2.0, Sangerbox 3.0, Kaplan–Meier Plotter, R package, and TISIDB databases. The TISIDB database and R package were used to construct the correlation between DNMT3A and immunomodulators and Immune cell Proportion Score. The association of DNMT3A expression with tumor mutational burden (TMB), microsatellite instability, and tumor dryness was evaluated using the TMB function of the R package, TIMER 2.0. Finally, the biological function of DNMT3A in gastric cancer cells was further assessed by CCK-8, cloning formation, and transwell assay. DNMT3A expression was remarkably upregulated in gastric cancer. The high expression of DNMT3A was associated with poor clinical features and poor survival in patients with gastric cancer. Moreover, gene set enrichment analyses showed that DNMT3A and its related genes were involved in various pathways that promoted cancer occurrence and progression by influencing the tumor microenvironment. Finally, DNMT3A was significantly related to tumor-infiltrating immune cells, immunomodulators, TMB, microsatellite instability, and immune checkpoints in gastric cancer. Moreover, knockdown of DNMT3A reduced the proliferation and migration of gastric cancer cells. Our findings highlight the potential of DNMT3A as a prognosis biomarker and an immunotherapeutic target for gastric cancer.

Radiotherapy is a common approach during the treatment of thyroid cancer (THCA). It is urgent to identify the radiotherapy-related gene and explore the underlying mechanisms. An message RNA expression clinical data was gained from the Cancer Genome Atlas. The differential expression genes between normal individuals and THCA patients were identified by the “limma” package of R software. The differential expression genes between the patients without radiation therapy and the patients with radiation therapy were also obtained via the same method. Survival analysis, gene set enrichment analysis, immune analysis, drug sensitivity analysis, gene–miRNA, and nomogram analysis were performed to explore the radiotherapy-related gene value. The results showed that 354 DGEs between the THCA patients without radiation therapy and THCA patients with radiation therapy including the 148 up-regulated genes and 206 down-regulated were screened and displayed by volcano plot. A gene enrichment analysis showed radiation-related genes were enriched in various pathways such as mineral absorption, complement and coagulation cascades, B cell receptor signaling pathway, salivary secretion, and hematopoietic cell lineage. Then the hub-related-radiotherapy prognosis gene LRP1B was identified. The expression analysis showed that the LRP1B expression level was higher in normal individuals than in THCA patients with an obvious difference via T test in independent samples and paired samples. Immune analysis results showed that the stroma score, immune score, and ESTIMATES score were higher in the low-risk score than in the high-risk score. LRP1B is a vital gene that executes function via a variety of pathways in THCA patients with radiotherapy. Radiotherapy could reduce the expression of LRP1B and AL356596.1. Moreover, the constructed nomogram is based on risk score and clinical features, and it had a great function in predicting survival time for patients.