Work

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

Year: 2025

Type: article

Abstract: 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 • Steps for developing a prognostic model using robustly prioritized signature genes • Instructions for performing molecular subtyping and training a classifier • Steps for establishing regulatory network and performing master regulatory analysis • Steps for performing drug sensitivity analysis to repurpose therapeutic candidates Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. 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.

Source: STAR Protocols

DOI: 10.1016/j.xpro.2025.104130

Authors: Jiang Li, Xiaoning Hong, Zhongxu Zhu, Jiang Li

Institutions: Sun Yat-sen University, The Seventh Affiliated Hospital of Sun Yat-sen University, Chinese Academy of Sciences, Hangzhou Institute of Medicine, Chinese Academy of Sciences

Language: English

Topic: Gastric Cancer Management and Outcomes

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,550