Predicting GD2 expression across cancer types by the integration of pathway topology and transcriptome data.

ABSTRACT:

Background

The disialoganglioside GD2 is a key cancer therapy target due to its overexpression in several cancers and limited presence in normal tissues. However, experimental assessment is technically challenging and not routinely available. We developed a computational framework that integrates reaction activity derived from transcriptomic data with the glycosphingolipid biosynthesis pathway to predict GD2 expression.

Methods

We computed Reaction Activity Scores from transcriptomic data and weighted the reactions of a glycosphingolipid metabolic network, refining edge weights with topology-based transition probabilities to account for enzyme promiscuity. Cumulative activities of GD2-promoting and -mitigating reactions served as features in a Support Vector Machine (SVM) to

SUBMITTER: Ustjanzew A 

PROVIDER: S-EPMC12711791 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature