Peritumor Edema Serves as an Independent Predictive Factor of Recurrence Patterns and Recurrence-Free Survival for High-Grade Glioma.

Objective: This study is aimed at analyzing the factors affecting the recurrence patterns and recurrence-free survival (RFS) of high-grade gliomas (HGG). Methods: Eligible patients admitted to the Affiliated Hospital of Xuzhou Medical University were selected. Subsequently, the effects of some clinical data including age, gender, WHO pathological grades, tumor site, tumor size, clinical treatments, and peritumoral edema (PTE) area and molecular markers (Ki-67, MGMT, IDH-1, and p53) on HGG patients' recurrence patterns and RFS were analyzed. Results: A total number of 77 patients were enrolled into this study. After analyzing all the cases, it was determined that tumor size and tumor site had a significant influence on the recurrent patterns of HGG, and PTE was an independent predict factor of recurrence patterns. Specifically, when the PTE was mild (<1 cm), the recurrence pattern tended to be local; in contrast, HGG was more likely to progress to marginal recurrence and distant recurrence. Furthermore, age and PTE were significantly associated with RFS; the median RFS of the population with PTE < 1 cm (23.60 months) was obviously longer than the population with PTE ≥ 1 cm (5.00 months). Conclusions: PTE is an independent predictor of recurrence patterns and RFS for HGG. Therefore, preoperative identification of PTE in HGG patients is crucially important, which is helpful to accurately estimate the recurrence pattern and RFS.

Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes.

BACKGROUND: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and the prognostic value of autophagy genes in liver cancer. OBJECTIVE: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer for establishing a prognostic model of primary liver cancer based on autophagy genes. METHODS: We identified autophagy genes related to the prognosis of liver cancer through bioinformatics methods. RESULTS: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. For optimizing the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes, after which a prognostic model was established. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO) and performed a multi-factor cox regression analysis on the 9 selected genes for constructing a prognostic model. The risk score of each patient, who participated in the establishing of the model, was calculated based on 4 genes (BIRC5, HSP8, SQSTM1, and TMEM74). Then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was assessed by the independent prognostic factors (HR = 1.872, 95% CI = 1.544 - 2.196, p < 0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. By combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting the prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. CONCLUSION: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and reveal part of the molecular mechanism of liver cancer autophagy. At the same time, certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.