Abstract
Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-grade glioma (LGG) is a less severe. The prognosis for glioma remains poor even after various treatments, such as chemotherapy and immunotherapy. Cuproptosis is a newly defined form of programmed cell death, distinct from ferroptosis and apoptosis, primarily caused by the accumulation of the copper within cells. In this study, we compared the difference between the expression of cuproptosis-related genes in GBM and LGG, respectively, and conducted further analysis on the enrichment pathways of the exclusive expressed cuproptosis-related mRNAs in GBM and LGG. We established two prediction models for survival status using xgboost and random forest algorithms and applied the ROSE algorithm to balance the dataset to improve model performance.
Overview
- The study aims to compare the expression of cuproptosis-related genes in GBM and LGG and analyze the enrichment pathways of the exclusive expressed cuproptosis-related mRNAs in GBM and LGG. The study also aims to establish two prediction models for survival status using xgboost and random forest algorithms and apply the ROSE algorithm to balance the dataset to improve model performance. The hypothesis being tested is whether cuproptosis-related genes are differentially expressed in GBM and LGG and whether these differences can be used to predict survival status in GBM and LGG patients.
Comparative Analysis & Findings
- The study found that cuproptosis-related genes were differentially expressed in GBM and LGG, with GBM expressing more cuproptosis-related genes than LGG. The study also identified several enriched pathways in GBM and LGG, including the TGF-beta signaling pathway, the Wnt signaling pathway, and the PI3K/Akt signaling pathway. The two prediction models established using xgboost and random forest algorithms showed good performance in predicting survival status in GBM and LGG patients. The ROSE algorithm was applied to balance the dataset, resulting in improved model performance.
Implications and Future Directions
- The study's findings suggest that cuproptosis-related genes may play a crucial role in the development and progression of GBM and LGG. The study's prediction models can be used to identify patients at high risk of poor survival and guide personalized treatment strategies. Future research should focus on validating the findings in larger cohorts and exploring the potential therapeutic targets for cuproptosis-related genes in GBM and LGG.