Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM.

ABSTRACT:

Background

Previous studies have described sex-specific patient subtyping in glioblastoma. The cluster labels associated with these "legacy data" were used to train a predictive model capable of recapitulating this clustering in contemporary contexts.

Methods

We used robust ensemble machine learning to train a model using gene microarray data to perform multi-platform predictions including RNA-seq and potentially scRNA-seq.

Results

The engineered feature set was composed of many previously reported genes that are associated with patient prognosis. Interestingly, these well-known genes formed a predictive signature only for female patients, and the application of the predictive signature to male patients produced unexpected results.

Conclusions

This work demonst

SUBMITTER: Gibbs DL 

PROVIDER: S-EPMC11815886 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature