Development of a nomogram for prognostic prediction of lower-grade glioma based on alternative splicing signatures

Abstract

Background: The prognosis of lower-grade glioma (LGG) differs from that of other grades gliomas. Although lots of studies on the prognostic biomarkers of LGG have been reported, few have significant clinical impact. Alternative splicing (AS) events can affect cell function by splicing precursor mRNA. Therefore, a prognostic model for LGG based on AS events are important to establish.

Methods: RNA sequencing, clinical, and AS event data of 510 LGG patients from the TCGA database were downloaded. Univariate Cox regression analysis was used to screen out prognostic-related AS events and LASSO regression and multivariate Cox regression were used to establish prognostic risk scores for patients in the training set (n = 340). After validation, a nomogram model was established based on the AS signature and clinical information, which was able to predict 1-, 3-, and 5-year survival rates. Finally, considering the regulatory effect of splicing factors (SFs) on AS events, an AS-SF regulatory network was analyzed.

Results: The most common AS event was exon skipping and the least was mutually exclusive exons. All the seven AS events were related to the prognosis of LGG patients, regardless of whether they were separated or considered as a whole event (integrated AS event), and the integrated AS event had the most significant correlation. After further inclusion of clinical indicators, eight factors were screened out: age, new event, KPS, WHO grade, treatment, integrated AS signature, IDH1 and TP53 mutation status, and a nomogram model was established. The study also constructed an AS-SF regulatory network.

Conclusion: The AS events and clinical factors that can predict the prognosis of LGG patients were screened, and a prognostic prediction model was established. The results of this study can play an important role in clinical work to better evaluate the prognosis of patients and impact treatment options.

Keywords: alternative splicing; low-grade glioma; nomogram; prediction model.