  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.
