A densely connected framework for cancer subtype classification

Background Reliable identification of cancer subtypes is crucial for devising personalized treatment strategies. Integrating multi-omics data has proven to be an effective method for analyzing cancer subtypes. By combining molecular information across various layers, a more comprehensive understandi...

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Published in:BMC bioinformatics Vol. 26; no. 1; pp. 183 - 18
Main Authors: Li, Yu, Zheng, Denggao, Sun, Kaijie, Qin, Chi, Duan, Yuchen, Zhou, Qingqing, Yin, Yunxia, Kan, Hongxing, Hu, Jili
Format: Journal Article
Language:English
Published: London BioMed Central 18.07.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Accuracy
Algorithms
Artificial neural networks
Automatic classification
Bioinformatics
Biomedical and Life Sciences
Breast cancer
Cancer
Cancer research
Cancer subtypes
Classification
Computational Biology - methods
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data integration
Data mining
Datasets
Deep Learning
DenseNet
Diagnosis
Disease
DNA methylation
Gastric cancer
Health services
Humans
Kidney cancer
Kidney Neoplasms - classification
Kidney Neoplasms - genetics
Life Sciences
Machine Learning
Medical research
Methods
Microarrays
Multi-omics
Neoplasms - classification
Neoplasms - genetics
Neural networks
Patients
Variational autoencoder
China
Cancer subtypes
DenseNet
Variational autoencoder
Multi-omics
Kidney cancer
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Reliable identification of cancer subtypes is crucial for devising personalized treatment strategies. Integrating multi-omics data has proven to be an effective method for analyzing cancer subtypes. By combining molecular information across various layers, a more comprehensive understanding of biological characteristics and disease mechanisms can be achieved. Results We propose DEGCN, a novel deep learning model that integrates a three-channel Variational Autoencoder (VAE) for multi-omics dimensionality reduction and a densely connected Graph Convolutional Network (GCN) for effective subtype classification. DEGCN leverages the complementary strengths of non-linear feature extraction and graph-based relational learning, enabling accurate and robust classification of renal cancer subtypes. Experimental results demonstrate that DEGCN achieves a cross-validated classification accuracy of 97.06% ± 2.04% on renal cancer data, outperforming conventional machine learning algorithms and state-of-the-art deep learning models. Moreover, its generalization ability is validated on breast and gastric cancer datasets from TCGA, with cross-validated classification accuracies of 89.82% ± 2.29% and 88.64% ± 5.24%, respectively, indicating strong cross-cancer predictive performance. Conclusion The study highlights the outstanding performance of DEGCN in heterogeneous data integration and classification accuracy, demonstrating the model’s potential in cancer subtype prediction and its application in guiding clinical treatment.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-025-06230-0