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To the best of our knowledge, this is the largest clinical retrospective study in AN‐SAH patients, and is the first time to establish accurate predictive models paired with bleeding pattern. Background: Angiogram‐negative subarachnoid hemorrhage (AN‐SAH) has a definite incidence of delayed cerebral ischemia (DCI) and poor clinical outcomes. The purpose is to screen independent factors and establish a nomogram to guide the clinical therapy and assess post‐discharge prognosis. Methods: We identified 273 consecutive patients referred to our institute from 2013 to 2018 for AN‐SAH. A nomogram to predict poor outcomes was formulated based on the multivariable models of independent risk factors. The accuracy and discrimination of nomograms were determined in training and internal validation cohorts. Results: The overall poor outcome rates of AN‐SAH were 14.3% and 8.7% at 3 months and 12 months, respectively. In addition, perimesencephalic AN‐SAH (PAN‐SAH) presented with a more unfavorable prognosis compared with non‐perimesencephalic AN‐SAH (NPAN‐SAH). The clinical prognosis was associated with the World Federation of Neurosurgical Societies scale (WFNS) (odds ratio, 3.82 [95% CI, 1.15‐12.67] for 3‐month outcome; and odds ratio, 31.69 [95% CI, 3.65‐275.43] for 12‐month outcome), Subarachnoid hemorrhage Early Brain Edema Score (SEBES) (odds ratio, 10.39 [95% CI, 1.98‐54.64] for 3‐month outcome; odds ratio, 10.01 [95% CI, 1.87‐53.73] for 12‐month outcome), and symptomatic vasospasm (odds ratio, 3.16 [95% CI, 1.03‐9.70] for 3‐month outcome; odds ratio, 5.15 [95% CI, 1.34‐19.85] for 12‐month outcome). The nomogram was constructed based on the above features, which represented great predictive value in clinical outcomes. Conclusions: Symptomatic vasospasm, high WFNS, cerebral edema, and NPAN‐SAH after hemorrhage were associated with poor outcome of AN‐SAH. The nomogram with WFNS (3‐5), SEBES (3‐4), vasospasm, and NPAN‐SAH represented a practical approach to provide individualized risk assessment for AN‐SAH patients. [ABSTRACT FROM AUTHOR]

Aim: To demonstrate the clinical value of epithelial membrane protein 3 (EMP3) with bioinformatic analysis and clinical data, and then to establish a practical nomogram predictive model with bicenter validation. Methods: The data from CGGA and TCGA database were used to analyze the expression of EMP3 and its correlation with clinical prognosis. Then, we analyzed EMP3 expression in samples from 179 glioma patients from 2013 to 2017. Univariate and multivariate cox regression were used to predict the prognosis with multiple factors. Finally, a nomogram to predict poor outcomes was formulated. The accuracy and discrimination of nomograms were determined with ROC curve and calibration curve in training and validation cohorts. Results: EMP3 was significantly higher in higher‐grade glioma and predicted poor prognosis. In multivariate analysis, high expression of EMP3 (HR = 2.842, 95% CI 1.984–4.071), WHO grade (HR = 1.991, 95% CI 1.235–3.212), and IDH1 mutant (HR = 0.503, 95% CI 0.344–0.737) were included. The nomogram was constructed based on the above features, which represented great predictive value in clinical outcomes. Conclusion: This study demonstrated EMP3 as a novel predictor for clinical progression and clinical outcomes in glioma. Moreover, the nomogram with EMP3 expression represented a practical approach to provide individualized risk assessment for glioma patients. [ABSTRACT FROM AUTHOR]

Purpose: Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma. Methods: The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups. Results: FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI 1.04–1.52), grade (HR = 2.75, 95 % CI 2.06–3.68), tumor recurrence (HR = 2.17, 95 % CI 1.81–2.62), IDH mutant (HR = 2.46, 95 % CI 1.97–3.01) and chemotherapeutic status (HR = 1.4, 95 % CI 1.20–1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients. Conclusions: This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy. [ABSTRACT FROM AUTHOR]