Background. Increasing evidence has shown that necroptosis has enormous significance in the generation and deterioration of cancer, and miRNA molecular markers involved in necroptosis in low-grade gliomas (LGGs) have not been thoroughly reported. Methods. Using the miRNA data of 512 samples from The Cancer Genome Atlas (TCGA), 689 miRNAs from LGG samples were split into high immunity score and low immunity score groups for analysis. The differential miRNAs related to necroptosis were analyzed by univariate Cox regression analysis. On the basis of the outcome of univariate Cox regression analysis, miRNAs with significant differences were selected to construct a multivariate Cox regression model and calculate the risk score. Then, we evaluated whether the risk score could be used as an unaided prognostic factor. Results. Overall, six differential miRNAs were identified (hsa-miR-148a-3p, hsa-miR-141-3p, hsa-miR-223-3p, hsa-miR-7-5p, hsa-miR-500a-3p, and hsa-miR-200a-5p). Univariate and multivariate Cox regression analyses were performed, and the c index was 0.71. Then, by mixing the risk score with clinicopathological factors, univariate Cox regression (HR: 2.7146, 95% CI: 1.8402−4.0044, P < 0.0001 ) and multivariate Cox regression analyses (HR: 2.3280, 95% CI: 1.5692−3.4536, P < 0.001 ) were performed. The data suggested that the risk score is an unaided prognostic indicator, which is markedly related with the overall survival time of LGG sufferers. Thus, a lower risk score is correlated with better prediction of LGG. Conclusion. In order to achieve the ultimate goal of improving the living conditions of patients, we established prognostic risk model using 6 miRNAs related to necroptosis, which has the ability to predict the prognosis of LGG. It is possible to further enrich the therapeutic targets for LGG and provide clinical guidance for the treatment of LGG in the future.

At present, there is no systematic study on the signature of long-chain noncoding RNAs (lncRNAs) involved in metabolism that can fully predict the prognosis in patients with low-grade gliomas (LGGs). Therefore, consistent metabolic-related lncRNA signatures need to be established. The Cancer Genome Atlas (TCGA) was used to identify the expression profile of lncRNAs containing 529LGGs samples. LncRNAs and genes related to metabolism are used to establish a network in the form of coexpression to screen lncRNAs related to metabolism. LncRNA was more clearly described by univariate Cox regression. Moreover, lncRNA signatures were explored by multivariate Cox regression and lasso regression. The risk score was established according to the signature and it was an unattached prognostic marker according to Cox regression analysis. Functional enrichment of lncRNAs was shown by employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Univariate Cox retrospective analysis showed that 543 metabolism-related lncRNAs were independent prognostic factors of LGG, and multivariate Cox regression analysis confirmed that 19 metabolism-related lncRNAs were prognostic genes of LGG. In the risk model, the low-risk group had a higher Overall survival (OS) than the high-risk group (P < .001). Univariate Cox regression analysis of risk score and clinical factors showed that risk score was an independent prognostic factor (P < .001, HR = 1.047, 95% CI: 1.038-1.056). Multivariate Cox results showed that risk score could predict the prognosis of LGG (P < .001, HR = 1.036, 95% CI: 1.026-1.045). ROC curve analysis showed that risk score could predict the prognosis of LGG. The areas of 1-year, 3-years, and 5 years are 0.891, 0.904 and 0.832. GO and KEGG analysis showed that metabolism-related lncRNAs was mainly concentrated in the pathways related to tumor metabolism. In order to find a more stable and reliable target for the treatment of LGG, we established 19 metabolicrelated lncRNAs prognostic model, and determined that it can predict the prognosis of LGG patients. This provides a new solution approach to the poor prognosis of patients with LGG and may reverse the trend of LGG's transformation to high-grade gliomas.

Objective: To discuss the prognostic factors affecting the prognosis of 1-stage surgical clipping in aneurysmal subarachnoid hemorrhage (aSAH) elderly patients with multiple intracranial aneurysms (MIAs). Materials and Methods: A total of 84 elderly patients with aSAH who had MIAs and underwent 1-stage surgical clipping were retrospectively analyzed. Follow-up was conducted with patients 30 days after discharge using the Glasgow Outcome Scale (GOS). A GOS score of 1 to 3 was defined as a poor outcome, and a GOS score of 4 to 5 was defined as a good outcome. General information (gender, age, size of aneurysm, location of rupture of the responsible aneurysm, H-H grade, CT characteristics of aSAH, number of subarachnoid hemorrhages, operation opportunity, postoperative complications, and intraoperative rupture) and complications（cerebral infarction, hydrocephalus, electrolyte disturbance, and encephaledema）were recorded. Univariate analysis and multivariate regression analysis were used to analyze factors that may affect outcomes. Results: Univariate analysis showed that the number of SAH events (P=0.005), intraoperative rupture (P=0.048) and postoperative complications (P=0.002) were associated with the prognosis of aSAH elderly patients with MIAs undergoing 1-stage surgery. Multivariate analysis showed that the number of SAH events (odds ratio [OR] 4.740, 95% confidence interval [CI] 1.056 to 21.282, P=0.042) and postoperative complications (OR 4.531, 95% CI 1.266 to 16.220, P=0.020) were independently associated with the prognosis of aSAH elderly patients with MIAs undergoing 1-stage surgery. Conclusions: The number of SAH events and postoperative complications are independent risk factors for the prognosis of aSAH elderly patients with MIAs undergoing 1-stage surgery. These factors contribute to the timely treatment of potentially related patients.