Search

Lung adenocarcinoma (LUAD) accounts for almost 40% of lung cancers, leading to significant associated morbidity and mortality rates. However, the mechanism of LUAD tumorigenesis remains far from clear. Here, we scanned down‐regulated genes involved in LUAD sourced from The Cancer Genome Atlas and Gene Expression Omnibus data and focused on G protein‐coupled receptor 133 (GPR133). We offer compelling evidence that GPR133 was expressed at low levels in the setting of LUAD, and higher expression was positively related to a better prognosis among patients with LUAD. Functionally, GPR133 inhibited cell proliferation and tumor growth in vitro and in vivo. Regarding the mechanism, flow cytometry assays and western blot assays showed that GPR133 enhanced p21 and decreased cyclin B1 expression, thus triggering LUAD cells at G2/M‐phase arrest. Consistent with this, we evaluated the expression levels of cell‐cycle biomarkers and found that bioinformatics analysis combined with N6‐methyladenosine (methylation at the N6 position in adenosine) RNA immunoprecipitation‐qPCR assay indicated that GPR133 expression was down‐regulated by this modification. Moreover, we observed that methyltransferase‐like 3 was impaired in LUAD, and that it is able to significantly increase levels of GPR133 by enhancing its RNA stability. In conclusion, we found that GPR133 expression was down‐regulated in LUAD via N6‐methyladenosine modification. Increasing GPR133 levels could suppress LUAD cell proliferation and tumor growth.

Glioma is the most common primary brain tumor with poor prognosis and high mortality. The purpose of this study was to use the epigenetic signature to predict prognosis and evaluate the degree of immune infiltration in gliomas. We integrated gene expression profiles and DNA methylation data of lower-grade glioma and glioblastoma to explore epigenetic differences and associated differences in biological function. Cox regression and lasso analysis were used to develop an epigenetic signature based on eight DNA methylation sites to predict prognosis of glioma patients. Kaplan–Meier analysis showed that the overall survival time of high- and low-risk groups was significantly separated, and ROC analysis verified that the model had great predictive ability. In addition, we constructed a nomogram based on age, sex, 1p/19q status, glioma type, and risk score. The epigenetic signature was obviously associated with tumor purity, immune checkpoints, and tumor-immune infiltrating cells (CD8+ T cells, gamma delta T cells, M0 macrophages, M1 macrophages, M2 macrophages, activated NK cells, monocytes, and activated mast cells) and thus, it may find application as a guide for the evaluation of immune infiltration or in treatment decisions in immunotherapy.