Background: Long non-coding RNAs (lncRNAs) play an important role in the immune processes of glioma. Immune related lncRNAs (IRlncRs) may be a critical prognosis in patients with glioma. The current study aimed to construct a glioma immune-related prognosis model by IRlncRs. Methods: Transcriptome RNA-sequencing data of glioma were obtained from The Cancer Genome Atlas (TCGA) and an immune‑related risk score (IRRS) model was constructed by Lasso and multivariate Cox regression analysis. Receiver Operating Characteristic (ROC) curves were used to assess the sensitivity and specificity of the prognosis on IRRS. A predictive nomogram and a time-dependent ROC curve was performed in training and validation cohort. We explored the relationships between survival‑related IRlncRs (sIRlncRs) and clinicopathologic parameters. Functional annotation of the sIRlncRs was investigated by gene set enrichment analysis (GSEA) and principal component analysis (PCA). The relationships between IRRS model and immune cell infiltration and co-expression network analysis among the sIRlncRs were performed for molecular mechanism study. Results: A total of 10 sIRlncRs were enrolled to build IRRS model. The IRRS was identified as an independent prognostic factor and correlated with the overall survival (AUC =0.880). The nomogram was constructed successfully with IRRS, age and grade as variables. Immune cell infiltration analysis indicated that B cells, neutrophil, dendritic and macrophage cells were positively correlated with IRRS. PCA and GSEA illustrated that the lncRNA signature enrolled the IRRS model was closely related to immune status. Additionally, co-expression network showed that there was a strong correlation between 10 sIRlncRs at the transcriptional level. Conclusion: We successfully constructed a remarkable clinical model of sIRlncRs with potential prognostic value for glioma patients, which provides an insight into immunological research and treatment strategies of glioma.

Abstract Background: N6-methyladenosine (m6A) methylation modification can affect the tumorigenesis, progression, and metastasis of breast cancer (BC). Up to now, a prognostic model based on m6A methylation regulators for BC is still lacking. This study aimed to construct an accurate prediction prognosis model by m6A methylation regulators for BC patients.Methods: After processing of The Cancer Genome Atlas (TCGA) datasets, the differential expression and correlation analysis of m6A RNA methylation regulators were applied. Next, tumor samples were clustered into different groups and clinicopathologic features in different clusters were explored. By univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analysis, m6A regulators with prognostic value were identified to develop a prediction model. Furthermore, we constructed and validated a predictive nomogram to predict the prognosis of BC patients.Results: 19 m6A related genes were extracted and 908 BC patients enrolled from TCGA dataset. After univariate Cox and LASSO analysis, 3 m6A RNA methylation regulators (YTHDF3, ZC3H13 and HNRNPC) were selected to establish the prognosis model based on median risk score (RS) in training and validation cohort. With the increasing of RS, the expression levels of YTHDF3 and ZC3H13 were individually elevated, while the HNRNPC expressed decreasingly. By survival analysis and Receiver Operating Characteristic (ROC) curve, we found that the overall survival (OS) of high-risk group was significantly shorter than that of the low-risk group based on Kaplan-Meier (KM) analysis in each cohort. Univariate and multivariate analysis identified the RS, age, and pathological stage are independent prognostic factors. A nomogram was constructed to predict 1- and 3-year OS and the calibration plots validate the performance. The C-index of nomogram reached 0.757 (95% CI:0.7-0.814) in training cohort and 0.749 (95% CI:0.648-0.85) in validation cohort, respectively.Conclusions: We successfully constructed a predictive prognosis model by m6A RNA methylation regulators. These results indicated that the m6A RNA methylation regulators are potential therapeutic targets of BC patients.