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Botao Fa1, Chengwen Luo1, Zhou Tang2

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; SJTU-Yale Joint Centre for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.

Ebiomedicine
|May 19, 2019
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel pathway-based method accounting for gene overlap to predict hepatocellular carcinoma (HCC) prognosis, improving accuracy over single-gene approaches.

Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Single-gene prognostic models in cancer research often lack robustness and generalizability.
  • Pathway-based approaches offer improved reproducibility by integrating biological knowledge.

Purpose of the Study:

  • To develop a novel pathway-based prognostic model for hepatocellular carcinoma (HCC) that accounts for gene overlap between pathways (crosstalk).
  • To enhance the accuracy and robustness of prognostic predictions in HCC patients.

Main Methods:

  • Utilized the Pathifier methodology to estimate pathway deregulation scores (PDS).
  • Introduced a novel procedure to accommodate pathway crosstalk in PDS calculations.
  • Applied the method to a cohort of 355 HCC patients from The Cancer Genome Atlas (TCGA).
Keywords:
BiomarkerCrosstalkDeep learningHepatocellular carcinomaOverall survivalPathway-based

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Main Results:

  • Cross-validation demonstrated that pathway-based PDS features were more robust and accurate than single-gene (SG) features identified by deep learning.
  • External validation confirmed the superior performance of the proposed pathway features over SGs in predicting HCC prognosis.
  • Achieved an average improvement of 10.2% in prediction accuracy.

Conclusions:

  • The developed pathway-based approach with crosstalk consideration offers a more reliable method for HCC prognosis prediction.
  • Governing genes within the identified prognostic pathways provide insights into HCC cancer hallmarks.
  • An R package, PATHcrosstalk, was developed to facilitate the discovery of pathways with crosstalk effects considered.