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MRGCN: cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics

Bo Yang^1,2, Yan Yang¹, Meng Wang¹

¹The Shaanxi Key Laboratory of Clothing Intelligence, School of Computer Science, Xi'an Polytechnic University, Xi'an 710048, China.

Bioinformatics (Oxford, England)

|May 31, 2023

Summary

This study introduces MRGCN, a novel graph convolutional network model for integrating multi-omics cancer data. MRGCN effectively identifies cancer subtypes, improving upon existing methods for diagnosis and therapy.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Cancer is a complex, heterogeneous disease requiring precise subtyping for effective diagnosis and treatment.
High-throughput sequencing generates vast multi-omics data, presenting challenges in data integration for cancer research.

Purpose of the Study:

To develop a novel computational model for integrating multi-omics data to improve cancer subtyping.
To address the challenge of missing data in multi-omics datasets within a unified framework.

Main Methods:

Propose MRGCN, a graph convolutional network model for multi-omics data integrative representation.
MRGCN encodes and reconstructs omics expression and similarity relationships into a shared latent space.
Utilize an indicator matrix to handle missing values in partial omics data.

Main Results:

MRGCN achieved superior cancer subtyping compared to typical integrative methods across 11 multi-omics datasets.
Subtypes identified by MRGCN showed significantly enriched clinical parameters.
MRGCN demonstrated improved P-values in survival analysis.

Conclusions:

MRGCN offers a robust framework for multi-omics data integration and cancer subtyping.
The model effectively handles missing data, enhancing its applicability.
MRGCN shows promise for advancing cancer diagnosis and personalized therapy through improved subtyping.