[Development and Evaluation of Prognostic Nomogram Model for Adult Ventricle Glioma Patients].

Objective: To explore the prognostic factors of adult ventricle glioma (AVG) and to construct and evaluate a survival-related prognostic nomogram model, which could provide further reference for the clinical management of AVG patients. Methods: The patients covered in the study were selected from the Surveillance Epidemiology and End Results (SEER) database (1973-2016). They all had definite histological diagnosis of AVG. They were assigned randomly to the training cohort and the validation cohort by random number table at a 2/1 ratio. Survival analysis was performed by Kaplan-Meier analysis. Cox regression analysis was employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Then, integrating the basic characteristics of patients, the survival-related nomogram predictive model for OS and CSS in the training cohort was constructed, respectively. After that, internal cross validation and external validation of the model were carried out with the training cohort and the validation cohort in succession. The authenticity and reliability of the nomogram model were evaluated by calculating the concordance index (C-index). Calibration plots were constructed to assess the agreement between the predicted values and the observed values in the training cohort and the validation cohort. Results: A total of 369 AVG patients, including 218 males and 151 females, were included. The median age of the patients was 53. According to the WHO classification of gliomas, 66 (17.9%) patients had grade Ⅱ gliomas, 73 (19.8%) had grade Ⅲ gliomas, and 230 (62.3%) had grade Ⅳ gliomas. Regarding the extent of resection (EOR), 59 (16.0%) had gross total resection (GTR) and 145 (39.3%) had subtotal resection (STR) or partial resection (PR). Of all the patients, 167 (45.3%) received postoperative radiotherapy and 143 (38.8%) received postoperative chemotherapy. Patients were randomized into the training cohort ( n=246) and the validation cohort ( n=123), and there was no significant difference ( P>0.05) in the basic clinical characteristics between the training cohort and the validation cohort. In the training cohort, Cox regression analysis revealed that the independent prognostic factors for OS and CSS included age≥65, grades Ⅲ and Ⅳ according to the WHO classification of gliomas, and not receiving radiotherapy. Furthermore, 5 variables, including age, gender, WHO grades, surgery, and radiotherapy, were used to construct the nomogram model for predicting 6-month, 1-year, and 2-year OS and CSS. The results of internal cross validation in the training cohort showed that the C-indexes of OS and CSS were 0.758 and 0.765, respectively. The external validation results of the validation cohort showed that the C-indexes of OS and CSS were 0.733 and 0.719, respectively. Calibration plots for 6-month, 1-year, and 2-year OS in the training cohort showed relatively good agreement, while in the validation cohort the agreement was relatively low. The 6-month, 1-year, and 2-year CSS calibration plots had results similar to the calibration plots of OS. Conclusion: This nomogram predictive model of OS and CSS showed moderately reliable predictive performance, providing helpful reference information for clinicians to make quick and simple assessment of the survival probability of AVG patients.

Comprehensively investigating the expression levels and the prognostic role of transforming growth factor beta-induced (TGFBI) in glioblastoma multiforme.

BACKGROUND: Transforming growth factor beta-induced (TGFBI) protein has been found expressed in several cancer types, and expression levels of TGFBI can affect the cancer patients' outcomes, but the role of TGFBI in glioblastoma multiforme (GBM) remains obscure. METHODS: The TGFBI expression levels in GBM were performed via Gene Expression Profiling Interactive Analysis (GEPIA) and UALCAN databases. Further, the mutations types of TGFBI were analyzed by using the cBioportal dataset. LinkedOmics selected correlated genes, kinases, and microRNA (miRNA) targets of TGFBI. GEPIA conducted the prognostic value of TGFBI and correlated genes. Then, the relationship between TGFBI and immune infiltrates was performed by Tumor Immune Estimation Resource (Timer). We compared the TGFBI protein expression levels in GBM and control samples through the Human Protein Atlas (HPA). Finally, the GSCAlite was used to achieve the drugs, and molecules target the TGFBI and significantly correlated genes. RESULTS: TGFBI is significantly overexpressed in GBM, but the clinical features do not have considerable influence on TGFBI expression levels. Overexpression of TGFBI acts as an adverse biomarker of GBM. The enrichment function of TGFBI showed that the main biological functions, including extracellular matrix (ECM) organization, angiogenesis, leukocyte migration, T cell activation, cell cycle G2/M phase transition, and growth factor binding. About the significant correlated genes, overexpression of mitogen-activated protein kinase 13 (MAPK13) [Log-rank P=0.08 HR (high) =1.4], myosin IG (MYO1G) [Log-rank P=0.06 HR (high) =1.4], plasminogen activator urokinase receptor (PLAUR) [Log-rank P=0.03 HR (high) =1.5], thrombomodulin (THBD) [Log-rank P=0.028 HR (high) =1.5] indicated the poor prognosis of GBM. Further, TGFBI had a significant association with dendritic cell (DC) infiltrates (cor =0.516, P=9.00e-30). The higher the DC infiltration, the shorter survival of GBM. TGFBI protein expression levels were not significantly different in GBM and normal tissue. Finally, TGFBI is associated with resistance to belinostat, LAQ824, CAY10603, CUDC-101, methotrexate, 5-fluorouracil, and navitoclax. CONCLUSIONS: In the present study, we showed TGFBI was overexpressed in GBM, and TGFBI is associated with DC cell infiltrates. Overexpression of TGFBI and high DC infiltration might be an adverse biomarker of GBM. Finally, TGFBI is associated with tumor multi-drug resistance.