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NetWalkRank: Cancer Driver Gene Prioritization in Multiplex Gene Regulatory Networks by a Random Walk Approach.

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    Identifying cancer driver genes (CDGs) is crucial for oncology. NetWalkRank prioritizes CDGs in multiplex gene regulatory networks (GRNs) using network propagation, showing significant effectiveness in predicting hepatocellular carcinoma driver genes.

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    Area of Science:

    • Oncology
    • Bioinformatics
    • Systems Biology

    Background:

    • Identifying cancer driver genes (CDGs) is essential for understanding cancer development.
    • Existing methods often struggle to integrate complex, multi-stage gene information.
    • Multiplex gene regulatory networks (GRNs) offer a promising approach to model gene interactions.

    Purpose of the Study:

    • To develop a novel network-based framework, NetWalkRank, for prioritizing CDGs.
    • To leverage multiplex GRNs and gene expression data for enhanced CDG identification.
    • To validate NetWalkRank's effectiveness in prioritizing hepatocellular carcinoma (HCC) driver genes.

    Main Methods:

    • Constructing multiplex gene regulatory networks (GRNs) integrating multi-stage gene information.
    • Applying network propagation within the multiplex GRNs to assess gene abnormality spread.
    • Utilizing gene expression profiling data as input for the network analysis.
    • Training a random forest model with NetWalkRank scores for CDG prediction.

    Main Results:

    • NetWalkRank effectively prioritized known CDGs for hepatocellular carcinoma (HCC).
    • The framework demonstrated superior performance compared to existing driver gene ranking methods.
    • A random forest model trained on NetWalkRank scores achieved accurate CDG prediction.
    • Numerical experiments confirmed the efficiency and effectiveness of the proposed method.

    Conclusions:

    • NetWalkRank provides a robust framework for prioritizing cancer driver genes.
    • Integrating information across multiplex GRNs significantly enhances CDG identification and prediction.
    • The method holds promise for advancing cancer research and therapeutic strategies.