ABSTRACT
BACKGROUND: Primary central nervous system neuroblastoma (PCNSN) is a rare disease, and its incidence, treatment modalities, and survival remain poorly understood. METHODS: The SEER (Surveillance Epidemiology and End Results) database was used to identify patients diagnosed with PCNSN from 1973 to 2013. The incidence and survival rates were examined. Clinical features, treatment modalities, and prognosis were also assessed. RESULTS: A total of 280 patients with PCNSN were identified, with annual age-adjusted incidence being 0.37 per 1,000,000 persons in 1973 and decreasing to 0.12 in 2013. Neuroblastoma (NBL) (ganglioneuroblastoma vs. NBL; odds ratio [OR], 25.01; P = 0.008) and tumor with distant metastasis (OR, 0.17; P = 0.002) were more likely to receive conservative treatment over surgery, whereas older age (OR, 1.02; P = 0.011) and tumors located in the brain (other nervous system vs. brain: OR, 0.31; P = 0.001) increased the likelihood of receiving combined surgery and radiotherapy over surgery alone. In addition, younger age, ganglioneuroblastoma, and surgery treatment were significantly associated with improved outcomes (all P < 0.05). Furthermore, a nomogram model was established to effectively estimate survival for patients with PCNSN. CONCLUSIONS: We updated epidemiologic information of PCNSN and showed that age, histologic type, tumor extension, and surgery were independent prognostic factors. Moreover, treatment modalities of these tumors are influenced by patient and tumor characteristics.
Subject(s)
Central Nervous System Neoplasms/diagnosis , Central Nervous System Neoplasms/mortality , Neuroblastoma/diagnosis , Neuroblastoma/mortality , Adolescent , Adult , Central Nervous System Neoplasms/therapy , Child , Child, Preschool , Female , Humans , Incidence , Infant , Male , Neuroblastoma/therapy , Retrospective Studies , SEER Program/trends , Survival Rate/trends , Young AdultABSTRACT
The tumor microenvironment is highly correlated with tumor occurrence, progress, and prognosis. We aimed to investigate the immune-related gene (IRG) expression and immune infiltration pattern in the tumor microenvironment of lower-grade glioma (LGG). We employed the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores and identify prognostic IRG based on The Cancer Genome Atlas data set. The potential molecular functions of these genes were explored with the help of functional enrichment analysis and the protein-protein interaction network. Remarkably, three cohorts that were downloaded from the Chinese Glioma Genome Atlas database were analyzed to further verify the prognostic values of these genes. Moreover, the Tumor IMmune Estimation Resource (TIMER) algorithm was used to estimate the abundance of infiltrating immune cells and explore the immune infiltration pattern in LGG. And unsupervised cluster analysis determined three clusters of the immune infiltration pattern and indicated that CD8+ T cells and macrophages were significantly associated with LGG outcomes. Altogether, our study identified a list of prognostic IRGs and provided a perspective to explore the immune infiltration pattern in LGG.
Subject(s)
Brain Neoplasms/genetics , Brain Neoplasms/immunology , Glioma/genetics , Glioma/immunology , Lymphocytes, Tumor-Infiltrating/immunology , Tumor Microenvironment/genetics , Tumor Microenvironment/immunology , Adult , Algorithms , CD8-Positive T-Lymphocytes/immunology , Databases, Genetic , Female , Gene Expression Regulation, Neoplastic/genetics , Gene Expression Regulation, Neoplastic/immunology , Humans , Male , Prognosis , Protein Interaction Maps/genetics , Protein Interaction Maps/immunologyABSTRACT
A mutation in the isocitrate dehydrogenase 1 (IDH1) gene is the most common mutation in diffuse lower-grade gliomas (LGGs), and it is significantly related to the prognosis of LGGs. We aimed to explore the influence of the IDH1 mutation on the immune microenvironment and develop an IDH1-associated immune prognostic signature (IPS) for predicting prognosis in LGGs. IDH1 mutation status and RNA expression were investigated in two different public cohorts. To develop an IPS, LASSO Cox analysis was conducted for immune-related genes that were differentially expressed between IDH1wt and IDH1mut LGG patients. Then, we systematically analyzed the influence of the IPS on the immune microenvironment. A total of 41 immune prognostic genes were identified based on the IDH1 mutation status. A four-gene IPS was established and LGG patients were effectively stratified into low- and high-risk groups in both the training and validation sets. Stratification analysis and multivariate Cox analysis revealed that the IPS was an independent prognostic factor. We also found that high-risk LGG patients had higher levels of infiltrating B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells, and expressed higher levels of CTLA-4, PD-1 and TIM-3. Moreover, a novel nomogram model was established to estimate the overall survival in LGG patients. The current study provides novel insights into the LGG immune microenvironment and potential immunotherapies. The proposed IPS is a clinically promising biomarker that can be used to classify LGG patients into subgroups with distinct outcomes and immunophenotypes, with the potential to facilitate individualized management and improve prognosis.
