A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy
العنوان: | A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy |
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المؤلفون: | Li-Xia Gao, Yi-Fan Tong, Zhou Ye, Yi-Zhi Zhang, Qi-En He, Ling Wang, Kai Song |
المصدر: | Technology in Cancer Research & Treatment Technology in Cancer Research & Treatment, Vol 19 (2020) |
بيانات النشر: | SAGE Publications, 2020. |
سنة النشر: | 2020 |
مصطلحات موضوعية: | Cancer Research, Computer science, Gene regulatory network, Inference, gene regulatory network, Computational biology, lcsh:RC254-282, Radiation Tolerance, 03 medical and health sciences, 0302 clinical medicine, Cell Line, Tumor, Neoplasms, Partial least squares regression, Humans, Gene Regulatory Networks, Precision Medicine, 030304 developmental biology, 0303 health sciences, Multiple genomic data, signature genes, Models, Statistical, Models, Genetic, Gene Expression Profiling, Computational Biology, Regression analysis, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Signature (logic), Regression, Gene Expression Regulation, Neoplastic, Identification (information), Oncology, integrated regression method, radiosensitivity, 030220 oncology & carcinogenesis, Original Article, Network analysis |
الوصف: | Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and uncovering regulatory relationships. A total of 113 signature genes were selected from more than 20,000 genes. The root mean square error of the model was only 0.0025, which was much lower than previously published results, suggesting that our method can predict radiosensitivity with the highest accuracy. Additionally, our regulatory network analysis identified 24 highly important ‘hub’ genes. The data analysis workflow we propose provides a unified and computational framework to harness the full potential of large-scale integrated cancer genomic data for integrative signature discovery. Furthermore, the regression model, signature genes, and their regulatory network should provide a reliable quantitative reference for optimizing personalized treatment options, and may aid our understanding of cancer progress mechanisms. |
اللغة: | English |
تدمد: | 1533-0338 1533-0346 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7835317322b7d404ee0a282dde8e3fd6 http://europepmc.org/articles/PMC7225787 |
حقوق: | OPEN |
رقم الأكسشن: | edsair.doi.dedup.....7835317322b7d404ee0a282dde8e3fd6 |
قاعدة البيانات: | OpenAIRE |
تدمد: | 15330338 15330346 |
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