Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules
Objective: Breast cancer is a common tumor and has a high mortality rate. Gene regulatory networks(GRNs) can genetically facilitate targeted therapies for this disease. Impact Statement: This study proposes a new method to infer GRNs. This new method combining genetic modules and convolutional neura...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | BME Frontiers |
| Online Access: | https://spj.science.org/doi/10.34133/bmef.0154 |
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| author | Yihao Chen Ling Guo Yue Pan Hui Cai Zhitong Bing |
| author_facet | Yihao Chen Ling Guo Yue Pan Hui Cai Zhitong Bing |
| author_sort | Yihao Chen |
| collection | DOAJ |
| description | Objective: Breast cancer is a common tumor and has a high mortality rate. Gene regulatory networks(GRNs) can genetically facilitate targeted therapies for this disease. Impact Statement: This study proposes a new method to infer GRNs. This new method combining genetic modules and convolutional neural networks is presented to infer GRNs from the RNA sequencing data of breast cancer. Introduction: GRNs play an essential role in many disease treatments. Previous studies showed that GRNs will accelerate tumor therapy. However, most of the existing network inference methods are based on large-scale gene collections, which ignore the characteristics of different tumors. Methods: In this work, weighted gene coexpression network analysis was deployed to screen key genes and gene modules. The gene regulatory associations in gene modules were then transformed into 2-dimensional histogram types. A convolutional neural network was chosen as the main framework to fit the gene regulatory types and infer the GRN. Results: The method integrates genetic data analysis and deep learning perspectives to screen key genes and predict GRNs among key genes. The key genes screened were validated by multiple methods, and the inferred gene regulatory associations were widely validated in real datasets. Conclusion: The method can be used as an auxiliary tool with the potential to predict key genes and the GRNs of key genes. It has the potential to facilitate the therapeutic process and targeted therapy for breast cancer. |
| format | Article |
| id | doaj-art-297decf48848445399ffb9a9017a2a34 |
| institution | DOAJ |
| issn | 2765-8031 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | BME Frontiers |
| spelling | doaj-art-297decf48848445399ffb9a9017a2a342025-08-20T03:02:37ZengAmerican Association for the Advancement of Science (AAAS)BME Frontiers2765-80312025-01-01610.34133/bmef.0154Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic ModulesYihao Chen0Ling Guo1Yue Pan2Hui Cai3Zhitong Bing4College of Electrical Engineering, Northwest Minzu University, Lanzhou, China.College of Electrical Engineering, Northwest Minzu University, Lanzhou, China.Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.Gansu Province People Hospital, Lanzhou, China.Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.Objective: Breast cancer is a common tumor and has a high mortality rate. Gene regulatory networks(GRNs) can genetically facilitate targeted therapies for this disease. Impact Statement: This study proposes a new method to infer GRNs. This new method combining genetic modules and convolutional neural networks is presented to infer GRNs from the RNA sequencing data of breast cancer. Introduction: GRNs play an essential role in many disease treatments. Previous studies showed that GRNs will accelerate tumor therapy. However, most of the existing network inference methods are based on large-scale gene collections, which ignore the characteristics of different tumors. Methods: In this work, weighted gene coexpression network analysis was deployed to screen key genes and gene modules. The gene regulatory associations in gene modules were then transformed into 2-dimensional histogram types. A convolutional neural network was chosen as the main framework to fit the gene regulatory types and infer the GRN. Results: The method integrates genetic data analysis and deep learning perspectives to screen key genes and predict GRNs among key genes. The key genes screened were validated by multiple methods, and the inferred gene regulatory associations were widely validated in real datasets. Conclusion: The method can be used as an auxiliary tool with the potential to predict key genes and the GRNs of key genes. It has the potential to facilitate the therapeutic process and targeted therapy for breast cancer.https://spj.science.org/doi/10.34133/bmef.0154 |
| spellingShingle | Yihao Chen Ling Guo Yue Pan Hui Cai Zhitong Bing Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules BME Frontiers |
| title | Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules |
| title_full | Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules |
| title_fullStr | Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules |
| title_full_unstemmed | Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules |
| title_short | Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules |
| title_sort | inference of gene regulatory networks for breast cancer based on genetic modules |
| url | https://spj.science.org/doi/10.34133/bmef.0154 |
| work_keys_str_mv | AT yihaochen inferenceofgeneregulatorynetworksforbreastcancerbasedongeneticmodules AT lingguo inferenceofgeneregulatorynetworksforbreastcancerbasedongeneticmodules AT yuepan inferenceofgeneregulatorynetworksforbreastcancerbasedongeneticmodules AT huicai inferenceofgeneregulatorynetworksforbreastcancerbasedongeneticmodules AT zhitongbing inferenceofgeneregulatorynetworksforbreastcancerbasedongeneticmodules |