Disease characteristics and clinical specific survival prediction of spinal ependymoma: a genetic and population-based study - Fu, Tengyue; Mao, Chuxiao; Chen, Zhuming; Huang, Yuxiang; Li, Houlin; Wang, Chunhua; Liu, Jie; Li, Shenyu; Lin, Famu (Frontiers in Neurology, 2024)
Background: Spinal Ependymoma (SP-EP) is the most commonly occurring tumor affecting the spinal cord. In this study, we conducted a comprehensive analysis of RNA sequencing data, along with associated clinical information, from patients diagnosed with SP-EP. The aim was to identify key genes that are characteristic of the disease and develop a survival-related nomogram.Methods: We first accessed the Gene Expression Integrated Database (GEO) to acquire the microarray dataset pertaining to SP-EP. Furthermore, machine learning techniques and the CIBERSORT algorithm were employed to extract immune characteristic genes specific to SP-EP patients, thereby enhancing the characterization of target genes. Next, we retrieved comprehensive information on patients diagnosed with SP-EP between 2000 and 2020 from the Surveillance, Epidemiology, and End Results Database (SEER). Using this data, we screened for predictive factors that have a significant impact on patient outcomes. A nomogram was constructed to visualize the predicted overall survival (OS) rates of these patients at 3, 5, and 8 years post-diagnosis. Finally, to assess the reliability and clinical utility of our predictive model, we evaluated it using various metrics including the consistency index (C-index), time-dependent receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA).Results ： Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed that these DEGs were primarily involved in cellular processes, including cell cycle regulation and cell sensitivity mechanisms. Regarding the survival rates of patients with SP-EP, the 3-year, 5-year, and 8-year survival rates were 72.5%, 57.0%, and 40.8%, respectively. Diagnostic age (P<0.001), gender (P<0.001), and surgical approach (P<0.005) were identified as independent prognostic factors for OS. Notably, the study also demonstrated that more extensive surgical resection could extend patients' OS.Conclusions: Through bioinformatics analysis of microarray datasets, we identified CELF4 as a central gene associated with immune infiltration among DEGs. Furthermore, this study developed and validated a prognostic prediction model in the form of a nomogram utilizing the SEER database, enabling clinicians to accurately assess treatment risks and benefits, thereby enhancing personalized therapeutic strategies and prognosis predictions.