A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma.

ABSTRACT:

Objective

This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets.

Method

A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms - LASSO and Random Forest (RF) - were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences b

SUBMITTER: Luo C 

PROVIDER: S-EPMC9234137 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature