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Related Experiment Video

Updated: Jun 27, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

PANA-Surv: A Pathway-Guided Adaptive Neighborhood Augmentation Framework Using KEGG Pathways for Multi-Omics Cancer

Xiaowen Cao^1,2, Yijin Zhou³, Yao Dong¹

¹School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.

|June 26, 2026

Summary

This summary is machine-generated.

This study introduces PANA-Surv, a novel pathway-guided framework that improves multi-omics cancer survival prediction by enhancing molecular data representations. The method significantly outperforms existing models, identifying key prognostic genes for better cancer outcome analysis.

Keywords:

KEGG pathways bioinformatics cancer prognosis graph neural network multi-omics prognostic biomarkers survival analysis

Related Experiment Videos

Last Updated: Jun 27, 2026

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools

Published on: August 19, 2025

Area of Science:

Bioinformatics
Computational Biology
Cancer Genomics

Background:

Integrating multi-omics data for cancer prognosis is challenging due to high dimensionality and incomplete biological networks.
Pathway databases offer prior biological knowledge but their sparsity limits graph-based survival models.

Purpose of the Study:

To develop a pathway-guided framework, PANA-Surv, for enhanced multi-omics survival prediction in cancer.
To identify biologically relevant prognostic signals using integrated pathway knowledge.

Main Methods:

Proposed PANA-Surv, a pathway-guided adaptive neighborhood augmentation framework.
Utilized KEGG pathways for gene graph construction and multi-omics profiles as node features.
Employed a conditional variational autoencoder (PANA-VAE) for feature enhancement and integrated into a graph convolutional survival model.

Main Results:

PANA-Surv achieved the highest mean concordance index (C-index) across 10 TCGA cancer cohorts, significantly outperforming other models (p < 0.01).
Ablation studies confirmed the benefits of neighborhood reconstruction and adaptive weighting, with KEGG-guided graphs showing superior performance.
Identified 18 prognostic genes in breast cancer, including 6 novel candidates.

Conclusions:

Pathway prior knowledge integration with adaptive feature enhancement improves multi-omics survival modeling.
PANA-Surv effectively identifies biologically relevant prognostic signals for cancer outcomes.