| Abstrakt: |
Objective: To construct a prognostic assessment model for glioma patients based on long non-coding RNAs (lncRNAs) associated with necroptosis. Through this model, we aim to uncover the correlation between these lncRNAs and the prognosis of glioma patients, seeking new therapeutic avenues. Methods: Transcriptomic and clinical data of glioma patients were extracted from the TCGA database. Pearson correlation analysis was employed to identify lncRNAs co-expressed with necroptosis. Single-factor and multi-factor Cox regression analyses, along with Lasso regression, were used to screen lncRNAs closely associated with prognosis, thereby establishing a prognostic assessment model. The model's accuracy was validated through survival analysis, ROC curves, independent prognostic analysis, forest plots, and calibration curves. Additionally, functional enrichment analysis was conducted to identify active metabolic pathways in the high-risk and low-risk groups. Immunological analysis was performed to select potential drugs for glioma treatment. Results: The prognostic model categorized patients into the high-risk and low-risk groups based on risk scores. Significant differences were observed between the two groups in terms of survival curves, risk distribution, immune cell infiltration, immune-related functions, immune checkpoints, and immune therapy. The results of ROC curves, independent prognostic analysis, and forest plots supported the high reliability of the model in predicting patient survival. Conclusion: The predictive model, based on 10 necroptosis-related lncRNAs, contributes to the assessment of prognosis and molecular characteristics in glioma patients. [ABSTRACT FROM AUTHOR] |
