BACKGROUND The extent of glioma resection influences the overall survival (OS) and progression-free survival (PFS). Ferroptosis is a newly recognized type of cell death, which may be associated with low-grade glioma border detection and OS. This study is assessed an optimized ferroptosis gene panel for glioma treatment. MATERIAL AND METHODS We obtained 45 reports on ferroptosis-related proteins in PubMed and conducted a statistical test of the patients' overall survival (OS) in the TCGA GBMLGG and CGGA databases. The statistically significant genes were screened for an optimal panel, followed by GO and KEGG analysis and evaluated its correlation with known prognostic factors of glioma, including IDH1 mutation, methylated MGMT, tumor purity, 1p/19q LOH, and methionine cycle. RESULTS Eight genes panel (ALOX5, CISD1, FTL, CD44, FANCD2, NFE2L2, SLC1A5, and GOT1) were highly related to OS (P<0.001) and PFS (P<0.001) of low-grade glioma (LGG) patients, out of which 6 genes (ALOX5, CISD1, CD44, FTL, FANCD2, and SLC1A5) were correlated with IDH1_p.R132H (P<0.001) and 5 genes (ALOX5, CD44, FTL, NFE2L2, SLC1A5) showed a correlation with tumor purity (P<0.001). Five genes (ALOX5, CD44, CISD1, FTL, and SLC1A5) were associated with methylated MGMT (P<0.001), out of which 6 genes (ALOX5, CD44, FANCD2, NFE2L2, SLC1A5, and GOT1) had significantly different expression in healthy brain tissue vs. glioma (P<0.001). CONCLUSIONS Our panel of 8 ferroptosis genes showed a significant correlation with the diagnostic and prognostic factors of low-grade glioma and can be applied in neuroradiology and surgery.

Glioma is the most common malignant tumor in the central nervous system. This study aims to explore the potential mechanism and identify gene signatures of glioma. The glioma gene expression profile GSE4290 was analyzed for differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied for the enriched pathways. A protein-protein interaction (PPI) network was constructed to find the hub genes. Survival analysis was conducted to screen and validate critical genes. In this study, 775 downregulated DEGs were identified. GO analysis demonstrated that the DEGs were enriched in cellular protein modification, regulation of cell communication, and regulation of signaling. KEGG analysis indicated that the DEGs were enriched in the MAPK signaling pathway, endocytosis, oxytocin signaling, and calcium signaling. PPI network and module analysis found 12 hub genes, which were enriched in synaptic vesicle cycling rheumatoid arthritis and collecting duct acid secretion. The four key genes CDK17, GNA13, PHF21A, and MTHFD2 were identified in both generation (GSE4412) and validation (GSE4271) dataset, respectively. Regression analysis showed that CDK13, PHF21A, and MTHFD2 were independent predictors. The results suggested that CDK17, GNA13, PHF21A, and MTHFD2 might play important roles and potentially be valuable in the prognosis and treatment of glioma.

Background: Some studies have shown a strong link between the central nervous system and peripheral immune system, but the prognostic implications of dynamic peripheral immune-inflammatory responses in patients with traumatic brain injury (TBI) remain unclear. This study aimed to determine the dynamic trajectory patterns of the Systemic Immune Inflammation Index (SII) in patients with TBI and assess its association with all-cause hospital mortality.Methods: This retrospective cohort study utilized a large public database of patients with TBI sourced from the eICU Collaborative Research Database (eICU-CRD). Group-Based Trajectory Modeling (GBTM) was used to analyze daily SII trajectories during the initial 0–7 days of hospitalization. Logistic regression was employed to assess the relationship between different SII trajectory groups and hospital mortality. Receiver Operating Characteristic (ROC) curves were generated based on the logistic regression model.Results: A total of 312 patients were included in this study, 52 of whom died during hospitalization. Using GBTM, three distinct SII trajectories were identified: Group 1 (low-level, rapid decline; 18.90%), Group 2 (moderate-level, slow decline; 60.20%), and Group 3 (sustained high-level; 20.80%). Compared to patients in Group 1, those in Groups 2 and 3 had a higher risk of all-cause hospital mortality (odds ratio [OR] 4.09; 95% confidence interval [CI] 1.21, 19.75) and (OR 5.84; 95% CI 1.52, 30.67), respectively. ROC analysis revealed an area under the curve (AUC) of 0.838, sensitivity: 75.0%, and specificity: 83.8% for mortality in this cohort.Conclusion: This study identified three distinct SII trajectories, suggesting that post-TBI SII trajectories are heterogeneous patterns associated with mortality. The sustained high-level SII trajectory may serve as a marker of disease deterioration, highlighting the need for targeted interventions. Describing the evolution of SII through GBTM and its correlation with clinical outcomes can enhance our understanding of the link between neuroinflammation and the peripheral immune system.