Predicting the influence of homologous recombination repair deficiency genes on glioma heterogeneity and patient prognosis using multi-omics analysis and machine learning.

ABSTRACT:

Background

Glioma is the most common malignant tumor of the central nervous system, and homologous recombination deficiency (HRD) may play a crucial role in its progression. Our study aimed to predict the impact of HRD on glioma heterogeneity and patient prognosis from a multi-omics perspective.

Methods

We integrated HRD-related gene expression levels and survival information from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Using a combination of machine learning algorithms, we identified the optimal algorithm and constructed the HRD Index model. After validating the model's accuracy, we assessed the expression heterogeneity of HRD-related genes in vitro using quantitative polymerase chain reaction (qPCR). Multiple omics analyses, includi

SUBMITTER: Wu X 

PROVIDER: S-EPMC12716779 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature