Topologically inferring risk-active pathways toward precise cancer classification by directed random walk.

Area of Science:

• Bioinformatics
• Computational Biology
• Cancer Research

Background:

• Accurate prediction of cancer disease status is crucial for effective treatment.
• Gene biomarkers show promise but lack robustness and reproducibility across patient cohorts.
• Existing pathway-based methods often overlook critical topological information within biological pathways.

Purpose of the Study:

• To develop a novel method for inferring robust pathway activity by incorporating pathway topology.
• To improve the accuracy and reproducibility of cancer patient classification using pathway information.
• To provide a more reliable basis for guiding therapeutic selection and developing targeted strategies.

Main Methods:

• Proposed a directed random walk (DRW)-based approach to infer pathway activity.
• Weighted genes based on their topological importance within directed pathway networks.
• Evaluated the method's performance on 18 diverse cancer datasets.

Main Results:

• The DRW method significantly improved the reproducibility of pathway activity inference.
• Achieved more accurate and robust classification performance compared to existing gene-based and pathway-based methods.
• Identified risk-active pathways that are more reliable for clinical decision-making.

Conclusions:

• The DRW method offers a robust and accurate approach for pathway activity inference in cancer research.
• Integrating pathway topology enhances the reliability of biomarkers for predicting patient outcomes.
• The findings support the development of pathway-specific therapeutic strategies for improved cancer treatment.