A Hypoxia-Related Long Non-Coding RNAs Signature Associated With Prognosis in Lower-Grade Glioma

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.

Establishment and Validation of a Ferroptosis-Related lncRNA Signature for Prognosis Prediction in Lower-Grade Glioma

Background

The prognosis of lower-grade glioma (LGG) is highly variable, and more accurate predictors are still needed. The aim of our study was to explore the prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in LGG and to develop a novel risk signature for predicting survival with LGG.

Methods

We first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. A least absolute shrinkage and selection operator (LASSO) analysis was then utilized to develop a risk signature for prognostic prediction. Based on the results of multivariate Cox analysis, a prognostic nomogram model for LGG was constructed. Finally, functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), immunity, and m6A correlation analyses were conducted to explore the possible mechanisms by which these ferroptosis-related lncRNAs affect survival with LGG.

Results

A total of 11 ferroptosis-related lncRNAs related to the prognosis of LGG were identified. Based on prognostic lncRNAs, a risk signature consisting of 8 lncRNAs was constructed and demonstrated good predictive performance in both the training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed by combining the risk signature and clinical parameters proved to be more accurate in predicting the prognosis of LGG. In addition, there were differences in the levels of immune cell infiltration, immune-related functions, immune checkpoints, and m6A-related gene expression between the high- and low-risk groups.

Conclusion

In summary, our ferroptosis-related lncRNA signature exhibits good performance in predicting the prognosis of LGG. This study may provide useful insight into the treatment of LGG.