Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data
IntroductionThe combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researche...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1466825/full |
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| author | Jian Liu Xinzheng Xue Pengbo Wen Qian Song Jun Yao Shuguang Ge |
| author_facet | Jian Liu Xinzheng Xue Pengbo Wen Qian Song Jun Yao Shuguang Ge |
| author_sort | Jian Liu |
| collection | DOAJ |
| description | IntroductionThe combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.MethodsIn this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data.ResultsWe conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences.DiscussionIn the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes. |
| format | Article |
| id | doaj-art-c838892bda594476aada3c9a637d5c05 |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-c838892bda594476aada3c9a637d5c052024-11-14T06:21:08ZengFrontiers Media S.A.Frontiers in Genetics1664-80212024-11-011510.3389/fgene.2024.14668251466825Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics dataJian Liu0Xinzheng Xue1Pengbo Wen2Qian Song3Jun Yao4Shuguang Ge5School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, ChinaDepartment of Gynecology and Obstetrics, Taizhou Cancer Hospital, Wenling, ChinaDepartment of Colorectal Surgery, Taizhou Cancer Hospital, Wenling, ChinaSchool of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, ChinaIntroductionThe combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.MethodsIn this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data.ResultsWe conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences.DiscussionIn the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.https://www.frontiersin.org/articles/10.3389/fgene.2024.1466825/fullcancer subtypes discoveringmulti-omics dataclusteringdeep learningfusion strategy |
| spellingShingle | Jian Liu Xinzheng Xue Pengbo Wen Qian Song Jun Yao Shuguang Ge Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data Frontiers in Genetics cancer subtypes discovering multi-omics data clustering deep learning fusion strategy |
| title | Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data |
| title_full | Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data |
| title_fullStr | Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data |
| title_full_unstemmed | Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data |
| title_short | Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data |
| title_sort | multi fusion strategy network guided cancer subtypes discovering based on multi omics data |
| topic | cancer subtypes discovering multi-omics data clustering deep learning fusion strategy |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2024.1466825/full |
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