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IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.

Zhanyu Xu1, Haibo Liao2, Liuliu Huang1

  • 1Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, China.

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Summary

A new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), predicts lung adenocarcinoma (LUAD) recurrence. This method identifies PSMC1 and PSMD11 as key genes influencing recurrence and therapeutic sensitivity.

Keywords:
PSMC1 and PSMD11lung adenocarcinomamulti-omics dataneural network interpretabilityrecurrence prediction

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Lung adenocarcinoma (LUAD) is a prevalent lung cancer subtype.
  • Early-stage LUAD patients face a significant risk (30-50%) of metastatic recurrence post-surgery.

Purpose of the Study:

  • To develop a novel computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), for predicting LUAD recurrence.
  • To elucidate the underlying regulatory mechanisms of LUAD progression and recurrence.
  • To integrate multi-omics data for enhanced interpretability in cancer research.

Main Methods:

  • Developed IBPGNET, a graph neural network model leveraging pathway hierarchy.
  • Integrated diverse omics data for comprehensive analysis.
  • Validated IBPGNET performance using 5-fold cross-validation against existing classification methods.

Main Results:

  • IBPGNET demonstrated superior performance compared to other classification methods.
  • Identified PSMC1 and PSMD11 as genes significantly associated with LUAD recurrence.
  • Observed elevated PSMC1 and PSMD11 expression in LUAD cells versus normal cells.
  • Knockdown of PSMC1/PSMD11 enhanced afatinib sensitivity and reduced LUAD cell migration, invasion, and proliferation.
  • Demonstrated that PSMC1/PSMD11 may regulate therapeutic sensitivity via EGFR expression.

Conclusions:

  • IBPGNET is an effective tool for predicting LUAD recurrence and understanding its mechanisms.
  • PSMC1 and PSMD11 are potential biomarkers for LUAD recurrence and therapeutic targets.
  • Targeting PSMC1 and PSMD11 could offer new therapeutic strategies for LUAD, potentially through modulating EGFR signaling.