Refining Gene Selection and Outlier Detection in Glioblastoma Based on a Consensus Approach for Regularized Survival Models

Glioblastoma, the most malignant brain cancer in adults, exhibits vast heterogeneities in prognosis, clinicopathological features, immune landscapes, and immunotherapeutic responses, which calls the need to develop personalized therapeutic approaches. The identification of long/ short-term survivors, along with their associated gene expression markers, opens promising avenues for tailored treatments. However, modeling omics data is particularly challenging due to its high-dimensionality. Our study aimed to create survival models using gene expression data retrieved from tumour tissue, with the goal of detecting outlier observations. These observations correspond to glioblastoma patients whose survival time is much greater/smaller than predicted. To assist in dimensionality reduction and select relevant genes, elastic net and network-based regularization were applied. For each method, different outlier observations were obtained. The rank product test was used as a consensus method, enabling the identification of observations whose martingale residuals were consistently large across different models, thus producing a consensual list of outliers.