GBMPurity: A machine learning tool for estimating glioblastoma tumor purity from bulk RNA-sequencing data.

ABSTRACT:

Background

Glioblastoma (GBM) presents a significant clinical challenge due to its aggressive nature and extensive heterogeneity. Tumor purity, the proportion of malignant cells within a tumor, is an important covariate for understanding the disease, having direct clinical relevance or obscuring signal of the malignant portion in molecular analyses of bulk samples. However, current methods for estimating tumor purity are nonspecific and technically demanding. Therefore, we aimed to build a reliable and accessible purity estimator for GBM.

Methods

We developed GBMPurity, a deep learning model specifically designed to estimate the purity of IDH-wild type primary GBM from bulk RNA-sequencing (RNA-seq) data. The model was trained using simulated pseudobulk tumors of known purity

SUBMITTER: Thomas MPH 

PROVIDER: S-EPMC12309721 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature