A novel DNA repair‐related nomogram predicts survival in low‐grade gliomas

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.

Prediction of long-term mortality in patients with ischemic stroke based on clinical characteristics on the first day of ICU admission: An easy-to-use nomogram

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Machine learning unveils immune-related signature in multicenter glioma studies

“…( Figure 3 F) At the same time, the DCA curve shows that nomograms can better predict 1-year, 3-year, and 5-year OS. ( Figures S5 A–S5C) Compared to the C-index and restricted mean survival (RMS) of four published risk models, 40 , 41 , 42 , 43 the risk model we developed has great advantages. ( Figures S5 D and S5E).…”

A Novel Predictive Model Utilizing Retinal Microstructural Features for Estimating Survival Outcome in Patients with Glioblastoma

“…It has also been used to predict tumor segmentation and the location of tumor recurrence following standard treatment using multi-modal fusion and nonlinear correlation learning [20]. Another investigation focused on how DNA repair functions can be used to generate an individualized model of 1, 2, 3, 5, and 10 years survival and recurrence rates with the LASSO-COX algorithm for patients with low-grade glioma [21].…”