A novel DNA repair‐related nomogram predicts survival in low‐grade gliomas. (16th October 2020)

• Record Type: Journal Article
• Authors: Li, Guanzhang; Wu, Fan; Zeng, Fan; Zhai, You; Feng, Yuemei; Chang, Yuanhao; Wang, Di; Jiang, Tao; Zhang, Wei
• Is Part Of: CNS neuroscience & therapeutics. Volume 27:Number 2(2021)
• Page Start: 186
• Page End: 195
• Publication Date: 2020-10-16
• Subjects: DNA repair functions -- low‐grade glioma -- prognosis prediction -- recurrence prediction -- tumor recurrence; Neuropharmacology -- Periodicals; Central nervous system -- Diseases -- Effect of drugs on -- Periodicals; 612.8
• DOI: 10.1111/cns.13464
• Languages: English
• ISSNs: 1755-5930

Abstract

Abstract: Aims: We aimed to create a tumor recurrent‐based prediction model to predict recurrence and survival in patients with low‐grade glioma. Methods: This study enrolled 291 patients (188 in the training group and 103 in the validation group) with clinicopathological information and transcriptome sequencing data. LASSO‐COX algorithm was applied to shrink predictive factor size and build a predictive recurrent signature. GO, KEGG, and GSVA analyses were performed for function annotations of the recurrent signature. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance. Results: This study found that DNA repair functions of tumor cells were significantly enriched in recurrent low‐grade gliomas. A predictive recurrent signature, built by the LASSO‐COX algorithm, was significantly associated with overall survival and progression‐free survival in low‐grade gliomas. Moreover, function annotations analysis of the predictive recurrent signature exhibited that the signature was associated with DNA repair functions. The nomogram, combining the predictive recurrent signature and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups. Conclusion: An individualized prediction model was created to predict 1‐, 2‐, 3‐, 5‐, and 10‐year survival and recurrent rate of patients with low‐grade glioma, which may serve as a potential tool to guide postoperative individualized care. Abstract : Based on large sample of clinical and sequencing data, we constructed an individualized prediction model for recurrence time predicting. The prediction model is accurate and simple enough to be widely applied for postoperative individualized care guidance of low‐grade gliomas.