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ORIGINAL RESEARCH article

Front. Neurol.
Sec. Neuro-Oncology and Neurosurgical Oncology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1454061
This article is part of the Research Topic Exploring the Potential for Advancements in Spinal Neurosurgery: Revolutionizing Treatment Pathways and Improving Quality of Life View all 13 articles

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

Provisionally accepted
Tengyue Fu Tengyue Fu 1*Chuxiao Mao Chuxiao Mao 1*Zhuming Chen Zhuming Chen 1*Yuxiang Huang Yuxiang Huang 1*Houlin Li Houlin Li 1*Jie Liu Jie Liu 2*Famu Lin Famu Lin 3*Shenyu ,. Li Shenyu ,. Li 2*
  • 1 Guangdong-Hong Kong-Macau Institute of CNS Regeneration (GHMICR), Jinan University, Guangzhou, China
  • 2 The Department of Neurosurgery, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi Zhuang Region, China
  • 3 The Department of Neurosurgery, Fifth Affiliated Hospital, Southern Medical University, Guangzhou, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: Spinal ependymoma, Celf4, SEER, nomogram, prognosis

    Received: 24 Jun 2024; Accepted: 26 Jul 2024.

    Copyright: © 2024 Fu, Mao, Chen, Huang, Li, Liu, Lin and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Tengyue Fu, Guangdong-Hong Kong-Macau Institute of CNS Regeneration (GHMICR), Jinan University, Guangzhou, China
    Chuxiao Mao, Guangdong-Hong Kong-Macau Institute of CNS Regeneration (GHMICR), Jinan University, Guangzhou, China
    Zhuming Chen, Guangdong-Hong Kong-Macau Institute of CNS Regeneration (GHMICR), Jinan University, Guangzhou, China
    Yuxiang Huang, Guangdong-Hong Kong-Macau Institute of CNS Regeneration (GHMICR), Jinan University, Guangzhou, China
    Houlin Li, Guangdong-Hong Kong-Macau Institute of CNS Regeneration (GHMICR), Jinan University, Guangzhou, China
    Jie Liu, The Department of Neurosurgery, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi Zhuang Region, China
    Famu Lin, The Department of Neurosurgery, Fifth Affiliated Hospital, Southern Medical University, Guangzhou, 510900, China
    Shenyu ,. Li, The Department of Neurosurgery, The Second Affiliated Hospital of Guilin Medical University, Guilin, Guangxi Zhuang Region, China

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