Inferring diagnostic and prognostic gene expression signatures across WHO glioma classifications: A network-based approach

TL;DR

This study introduces a network-based method to identify diagnostic and prognostic gene signatures across WHO glioma classifications, leveraging RNA-sequencing data and updated guidelines to improve biomarker discovery.

Contribution

The paper presents a novel two-step variable selection framework combining graphical lasso and Cox regression for biomarker discovery in glioma, incorporating recent WHO classification updates.

Findings

Identified potential biomarkers characteristic of each glioma type.

Better biomarker detection results with the 2021 WHO classification.

Supports inclusion of molecular data in glioma classification.

Abstract

Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official WHO classification CNS. These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on a TCGA glioma RNA-sequencing dataset updated according to the 2016 and 2021 WHO guidelines, we proposed a two-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularised Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.