A densely connected framework for cancer subtype classification
Abstract 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 un...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06230-0 |
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| author | Yu Li Denggao Zheng Kaijie Sun Chi Qin Yuchen Duan Qingqing Zhou Yunxia Yin Hongxing Kan Jili Hu |
| author_facet | Yu Li Denggao Zheng Kaijie Sun Chi Qin Yuchen Duan Qingqing Zhou Yunxia Yin Hongxing Kan Jili Hu |
| author_sort | Yu Li |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-8da5bd516b704b0bac71cb95d2d46a12 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-8da5bd516b704b0bac71cb95d2d46a122025-08-20T03:46:15ZengBMCBMC Bioinformatics1471-21052025-07-0126111810.1186/s12859-025-06230-0A densely connected framework for cancer subtype classificationYu Li0Denggao Zheng1Kaijie Sun2Chi Qin3Yuchen Duan4Qingqing Zhou5Yunxia Yin6Hongxing Kan7Jili Hu8School of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineSchool of Medical Information Engineering, Anhui University of Chinese MedicineAbstract 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.https://doi.org/10.1186/s12859-025-06230-0Cancer subtypesMulti-omicsKidney cancerVariational autoencoderDenseNet |
| spellingShingle | Yu Li Denggao Zheng Kaijie Sun Chi Qin Yuchen Duan Qingqing Zhou Yunxia Yin Hongxing Kan Jili Hu A densely connected framework for cancer subtype classification BMC Bioinformatics Cancer subtypes Multi-omics Kidney cancer Variational autoencoder DenseNet |
| title | A densely connected framework for cancer subtype classification |
| title_full | A densely connected framework for cancer subtype classification |
| title_fullStr | A densely connected framework for cancer subtype classification |
| title_full_unstemmed | A densely connected framework for cancer subtype classification |
| title_short | A densely connected framework for cancer subtype classification |
| title_sort | densely connected framework for cancer subtype classification |
| topic | Cancer subtypes Multi-omics Kidney cancer Variational autoencoder DenseNet |
| url | https://doi.org/10.1186/s12859-025-06230-0 |
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