The importance of the neutrophil-to-lymphocyte ratio in the prognosis of glioma and its subtypes

Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER

AIMS

Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms.

METHODS

A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy-related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four-fold cross-validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score.

RESULTS

Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913-0.950), precision (92.4%, 95% CI: 0.897-0.951), F1 score (91.5%, 95% CI: 0.889-0.964), and recall score (93.6%, 95% CI: 0.909-0.964), and yielded higher area under the receiver operating characteristic curve (AU-ROC) (0.962, 95% CI: 0.942-0.982).

CONCLUSION

The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.

LINC01116 promotes tumor proliferation and neutrophil recruitment via DDX5-mediated regulation of IL-1β in glioma cell

Tumor-associated neutrophils (TANs) are important inflammatory infiltrating cells in the tumor microenvironment and are closely related to the development of human tumor. However, the underlying mechanism of TANs recruiting to glioma remains unknown. Herein, we identified that LINC01116 was significantly upregulated in glioma, and positively correlated with clinical malignancy and survival prognosis. LINC01116 regulated the progression of glioma in vitro and in vivo. RNA-seq analysis demonstrated that LINC01116 knockdown affected the expression of IL-1β, which promoted glioma proliferation and neutrophil recruitment. Furthermore, the co-culture of glioma cells and neutrophils showed that the accumulation of TANs promoted tumor proliferation via producing a host of cytokines. Mechanistically, LINC01116 activated IL-1β expression by recruiting the transcriptional regulator DDX5 to the IL-1β promoter. Our findings reveal that LINC01116 can promote glioma proliferation and neutrophil recruitment by regulating IL-1β, and may be served as a novel target for glioma therapy and prognosis.