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  9. Predictive And Prognostic Markers
  11. Enhancing Non-small Cell Lung Cancer Survival Prediction Through Multi-omics Integration Using Graph Attention Network.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Enhancing Non-small Cell Lung Cancer Survival Prediction Through Multi-omics Integration Using Graph Attention Network.

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Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network.

Murtada K Elbashir1, Abdullah Almotilag1, Mahmood A Mahmood1

  • 1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72441, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|October 16, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel graph attention network (GAT) model for predicting non-small cell lung cancer (NSCLC) survival using multi-omics data. The GAT model integrating mRNA and miRNA data achieved superior prediction accuracy, highlighting the power of multi-omics approaches in cancer research.

Keywords:
DNA methylationgraph attention networkmRNAmiRNA

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate cancer survival prediction is crucial for patient management and therapeutic decisions.
  • Integrating multi-omics data (mRNA, miRNA, DNA methylation) enhances understanding of cancer's molecular underpinnings.
  • Non-small cell lung cancer (NSCLC) survival prediction remains a critical challenge in oncology.

Purpose of the Study:

  • To develop and evaluate a novel graph attention network (GAT) model for predicting NSCLC survival.
  • To assess the efficacy of integrating multi-omics data for improved survival prediction.
  • To identify key molecular features and pathways associated with NSCLC survival.

Main Methods:

  • Acquired and preprocessed multi-omics data (mRNA, miRNA, DNA methylation) from The Cancer Genome Atlas (TCGA).
multi-omics data
  • Utilized chi-square tests for feature selection and SMOTE for dataset balancing.
  • Employed a GAT model and measured performance using the concordance index (C-index).

    • Main Results:

    • The GAT model integrating mRNA and miRNA data achieved the highest C-index, demonstrating superior predictive performance.
    • Pathway analysis (KEGG) revealed that high-weight features are linked to viral entry pathways (Epstein-Barr virus, Influenza A), implicated in lung cancer.
    • The proposed GAT model outperformed existing state-of-the-art methods for NSCLC prediction.

    Conclusions:

    • A novel GAT-based model effectively predicts NSCLC survival using multi-omics data.
    • The integration of mRNA and miRNA data significantly enhances predictive accuracy.
    • Identified biological pathways, including viral infections, are critically involved in NSCLC progression and survival.