Protocol for tumor prognostic prediction, molecular stratification, and target discovery using a machine learning-driven approach

Accurate prediction of tumor prognosis, understanding molecular heterogeneity, and identifying druggable targets are critical for advancing precision oncology. Here, we present a protocol to analyze aging-associated prognosis and molecular heterogeneity in gastric tumors using a transcriptome-based, machine learning-driven approach. We describe procedures for developing a prognostic model using robust gene signature, identifying molecular subtypes, and building a machine learning classifier for subtype prediction. We further detail the inference of subtype-specific regulatory network and prioritization of druggable transcription factors through drug sensitivity analysis. For complete details on the use and execution of this protocol, please refer to Li et al.¹.