Disease characteristics and clinical specific survival prediction of spinal ependymoma: a genetic and population-based study

This study explores spinal ependymoma (SP-EP), a common spinal cord tumor. Early diagnosis and treatment improve patient outcomes. The research identifies key genes characteristic of SP-EP by analyzing RNA sequencing data and clinical information. A survival-related nomogram is developed to predict patient survival rates. The researchers used data from the Gene Expression Integrated Database (GEO) to find genes that are expressed differently in SP-EP samples compared to normal samples. Machine learning and the CIBERSORT algorithm helped to identify immune characteristic genes specific to SP-EP patients. This enhances the understanding of target genes. The study used data from the Surveillance, Epidemiology, and End Results (SEER) Database, screening for factors that significantly affect patient outcomes. The developed nomogram visualizes predicted overall survival rates at 3, 5, and 8 years post-diagnosis. The model's reliability was assessed using metrics like the consistency index and ROC curves.

• 1
  A total of 5,151 differentially expressed genes (DEGs) were identified between SP-EP and normal samples. These DEGs are involved in cellular processes like cell cycle regulation and cell sensitivity mechanisms, according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
• 2
  Immune infiltration analysis identified CELF4 as a core gene. The 3-year, 5-year, and 8-year survival rates for SP-EP patients were 72.5%, 57.0%, and 40.8%, respectively. Diagnostic age, gender, and surgical approach were independent prognostic factors for overall survival.
• 3
  A nomogram model was constructed based on prognostic factors, showing good consistency between predicted and actual results. More extensive surgical resection could extend patients’ overall survival.