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Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration

Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based method...

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Autores principales: Yin, Chaoyi, Cao, Yangkun, Sun, Peishuo, Zhang, Hengyuan, Li, Zhi, Xu, Ying, Sun, Huiyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137453/
https://www.ncbi.nlm.nih.gov/pubmed/35646077
http://dx.doi.org/10.3389/fgene.2022.884028
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author Yin, Chaoyi
Cao, Yangkun
Sun, Peishuo
Zhang, Hengyuan
Li, Zhi
Xu, Ying
Sun, Huiyan
author_facet Yin, Chaoyi
Cao, Yangkun
Sun, Peishuo
Zhang, Hengyuan
Li, Zhi
Xu, Ying
Sun, Huiyan
author_sort Yin, Chaoyi
collection PubMed
description Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development.
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spelling pubmed-91374532022-05-28 Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration Yin, Chaoyi Cao, Yangkun Sun, Peishuo Zhang, Hengyuan Li, Zhi Xu, Ying Sun, Huiyan Front Genet Genetics Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning–based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) to select the molecular subtype-related transcriptomic features and then construct a sample–sample similarity graph with low noise by using these features. Next, we take the selected gene expression, single nucleotide variants (SNV), and copy number variation (CNV) data as input and learn the multi-view representations of samples. On this basis, a robust variant of graph convolutional network (GCN) model is finally developed to obtain samples’ new representations by aggregating their subgraphs. Experimental results of breast and stomach cancer demonstrate that the classification performance of M-GCN is superior to other existing methods. Moreover, the identified subtype-specific biomarkers are highly consistent with current clinical understanding and promising to assist accurate diagnosis and targeted drug development. Frontiers Media S.A. 2022-05-13 /pmc/articles/PMC9137453/ /pubmed/35646077 http://dx.doi.org/10.3389/fgene.2022.884028 Text en Copyright © 2022 Yin, Cao, Sun, Zhang, Li, Xu and Sun. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Yin, Chaoyi
Cao, Yangkun
Sun, Peishuo
Zhang, Hengyuan
Li, Zhi
Xu, Ying
Sun, Huiyan
Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
title Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
title_full Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
title_fullStr Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
title_full_unstemmed Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
title_short Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration
title_sort molecular subtyping of cancer based on robust graph neural network and multi-omics data integration
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137453/
https://www.ncbi.nlm.nih.gov/pubmed/35646077
http://dx.doi.org/10.3389/fgene.2022.884028
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