N6-methyladenosine (m6A) modification is the most abundant modification in long noncoding RNAs (lncRNAs). Current studies have shown that the abnormal expression of m6A-related genes is closely associated with the tumorigenesis and progression of glioma. However, the role of m6A-related lncRNAs in glioma development is still unclear. Herein, we screened 566 m6A-related lncRNAs in glioma from The Cancer Genome Atlas (TCGA) database. The expression pattern of these lncRNAs could cluster samples into two groups, in which various classical tumor-related functions and the tumor immune microenvironment were significantly different. Subsequently, a nine-factor m6A-related lncRNA prognostic signature (MLPS) was constructed by using a LASSO regression analysis in the training set and was validated in the test set and independent datasets. The AUC values of the MLPS were 0.881, 0.918 and 0.887 for 1-, 3- and 5-year survival in the training set, respectively, and 0.856, 0.916 and 0.909 for 1-, 3-, and 5-year survival in the test set, respectively. Stratification analyses of the MLPS illustrated its prognostic performance in gliomas with different characteristics. Correlation analyses showed that the infiltrations of monocytes and tumor-associated macrophages (TAMs) were significantly relevant to the risk score in the MLPS. Moreover, we detected the expression of four MLPS factors with defined sequences in glioma and normal cells by using RT–PCR. Afterwards, we investigated the functions of LNCTAM34A (one of the MLPS factors) in glioma cells, which have rarely been reported. Via in vitro experiments, LNCTAM34A was demonstrated to promote the proliferation, migration and epithelial-mesenchymal transition (EMT) of glioma cells. Overall, our study revealed the critical role of m6A-related lncRNAs in glioma and elucidated that LNCTAM34A could promote glioma proliferation, migration and EMT.

BackgroundMedulloblastoma (MB) is a malignant tumor associated with a poor prognosis in part due to a lack of effective detection methods. Extrachromosomal circular DNA (eccDNA) has been associated with multiple tumors. Nonetheless, little is currently known on eccDNA in MB.MethodsGenomic features of eccDNAs were identified in MB tissues and matched cerebrospinal fluid (CSF) and compared with corresponding normal samples using Circle map. The nucleotides on both sides of the eccDNAs’ breakpoint were analyzed to understand the mechanisms of eccDNA formation. Bioinformatics analysis combined with the Gene Expression Omnibus (GEO) database identified features of eccDNA-related genes in MB. Lasso Cox regression model, univariate and multivariate Cox regression analysis, time-dependent ROC, and Kaplan–Meier curve were used to assess the potential diagnostic and prognostic value of the hub genes.ResultsEccDNA was profiled in matched tumor and CSF samples from MB patients, and control, eccDNA-related genes enriched in MB were identified. The distribution of eccDNAs in the genome was closely related to gene density and the mechanism of eccDNA formation was evaluated. EccDNAs in CSF exhibited similar distribution with matched MB tissues but were differentially expressed between tumor and normal. Ten hub genes prominent in both the eccDNA dataset and the GEO database were selected to classify MB patients to either high- or low-risk groups, and a prognostic nomogram was thus established.ConclusionsThis study provides preliminary evidence of the characteristics and formation mechanism of eccDNAs in MB and CSF. Importantly, eccDNA-associated hub genes in CSF could be used as diagnostic and prognostic biomarkers for MB.

Medulloblastoma (MB) is one of the most common central nervous system tumors in children. At present, the vital role of immune abnormalities has been proved in tumorigenesis and progression. However, the immune mechanism in MB is still poorly understood. In the present study, 51 differentially expressed immune-related genes (DE-IRGs) and 226 survival associated immune-related genes (Sur-IRGs) were screened by an integrated analysis of multi-array. Moreover, the potential pathways were enriched by functional analysis, such as ‘cytokine–cytokine receptor interaction’, ‘Ras signaling pathway’, ‘PI3K-Akt signaling pathway’ and ‘pathways in cancer’. Furthermore, 10 core IRGs were identified from DE-IRGs and Sur-IRGs. And the potential regulatory mechanisms of core IRGs were also explored. Additionally, a new prognostic model, including 7 genes (HDGF, CSK, PNOC, S100A13, RORB, FPR1, and ICAM2) based on IRGs, was established by multivariable COX analysis. In summary, our study revealed the underlying immune mechanism of MB. Moreover, we developed a prognostic model associated with clinical characteristics and could reflect the infiltration of immune cells.