RESUMO
Objectives: Although patients with grade 2 glioma have a relatively better prognosis and longer survival than those with high-grade glioma, there are still a number of patients with disappointing outcomes. In order to accurately predict the prognosis of patients, relevant risk factors were included in the analysis to establish a clinical prediction model so as to provide a basis for clinically individualized treatment. Methods: A retrospective study was conducted in patients diagnosed with grade 2 glioma. Data including clinical features, pathological type, molecular classification, neuroimaging examination, treatment, and survival were collected. The data sets were randomly assigned, with 80% of the data used for model building and 20% for validation. Cox proportional hazard regression analysis was used to construct the model using important risk factors and present it in the form of a nomogram. The nomogram was evaluated a using C-index and calibration chart. Results: A total of 160 patients were enrolled in this analysis, including 128 in the training group and 32 in the validation group. In the training group, eight important risk factors including preoperative KPS, the first presenting symptom, the extent of resection, the gross tumor size, 1p19q, IDH, radiotherapy, and chemotherapy were identified to construct the model. The C-index of the training group and the validation group was 0.832 and 0.801, respectively, indicating that the model had good prediction ability. The calibration charts of the two groups were drawn respectively, which showed that the calibration line and the standard line had a good consistency, which suggested that the model-predicted risk had a good consistency with the actual risk. Conclusions: Based on the data of our center, a nomogram prediction model with eight variables has been established as an off-the-rack tool and verified its accuracy, which can guide clinical work and provide consultation for patients.
RESUMO
BACKGROUND: Glioma is the most frequent brain malignancy presenting very poor prognosis and high recurrence rate. Focal adhesion complexes play pivotal roles in cell migration and act as hubs of several signaling pathways. METHODS: We used bioinformatic databases (CGGA, TCGA, and GEO) and identified a focal adhesion-related differential gene expression (FADG) signature by uniCox and LASSO regression analysis. We calculated the risk score of every patient using the regression coefficient value and expression of each gene. Survival analysis, receiver operating characteristic curve (ROC), principal component analysis (PCA), and stratified analysis were used to validate the FADG signature. Then, we conducted GSEA to identify the signaling pathways related to the FADG signature. Correlation analysis of risk scores between the immune checkpoint was performed. In addition, the correlation of risk scores and genes related with DNA repair was performed. CIBERSORT and ssGSEA were used to explore the tumor microenvironment (TME). A nomogram that involved our FADG signature was also constructed. RESULTS: In total, 1,726 (528 patients diagnosed with WHO II, 591 WHO III, and 603 WHO IV) cases and 23 normal samples were included in our study. We identified 29 prognosis-related genes in the LASSO analysis and constructed an eight FADG signature. The results from the survival analysis, stratified analysis, ROC curve, and univariate and multivariate regression analysis revealed that the prognosis of the high-risk group was significantly worse than the low-risk group. Correlation analysis between risk score and genes that related with DNA repair showed that the risk score was positively related with BRCA1, BRCA2, RAD51, TGFB1, and TP53. Besides, we found that the signature could predict the prognosis of patients who received radiation therapy. SsGSEA indicated that the high-risk score was positively correlated with the ESTIMATE, immune, and stromal scores but negatively correlated with tumor purity. Notably, patients in the high-risk group had a high infiltration of immunocytes. The correlation analysis revealed that the risk score was positively correlated with B7-H3, CTLA4, LAG3, PD-L1, and TIM3 but inversely correlated with PD-1. CONCLUSION: The FADG signature we constructed could provide a sensitive prognostic model for patients with glioma and contribute to improve immunotherapy management guidelines.