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Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning
Cell‐free methylated DNA immunoprecipitation and high‐throughput sequencing (cfMeDIP‐seq) is a new bisulfite‐free technique, which can detect the whole‐genome methylation of blood cell‐free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
John Wiley and Sons Inc.
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409309/ https://www.ncbi.nlm.nih.gov/pubmed/34251068 http://dx.doi.org/10.1111/cas.15052 |
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| author | Qi, Jian Hong, Bo Tao, Rui Sun, Ruifang Zhang, Huanhu Zhang, Xiaopeng Ji, Jie Wang, Shujie Liu, Yanzhe Deng, Qingmei Wang, Hongzhi Zhao, Dahai Nie, Jinfu |
| author_facet | Qi, Jian Hong, Bo Tao, Rui Sun, Ruifang Zhang, Huanhu Zhang, Xiaopeng Ji, Jie Wang, Shujie Liu, Yanzhe Deng, Qingmei Wang, Hongzhi Zhao, Dahai Nie, Jinfu |
| author_sort | Qi, Jian |
| collection | PubMed |
| description | Cell‐free methylated DNA immunoprecipitation and high‐throughput sequencing (cfMeDIP‐seq) is a new bisulfite‐free technique, which can detect the whole‐genome methylation of blood cell‐free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung tumors and normal controls. Based on the top 300 DMR, we built a random forest prediction model, which was able to distinguish malignant lung tumors from normal controls with high sensitivity and specificity of 91.0% and 93.3% (AUROC curve of 0.963). In summary, we reported a non–invasive prediction model that had good ability to distinguish malignant pulmonary nodules. |
| format | Online Article Text |
| id | pubmed-8409309 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | John Wiley and Sons Inc. |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-84093092021-09-03 Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning Qi, Jian Hong, Bo Tao, Rui Sun, Ruifang Zhang, Huanhu Zhang, Xiaopeng Ji, Jie Wang, Shujie Liu, Yanzhe Deng, Qingmei Wang, Hongzhi Zhao, Dahai Nie, Jinfu Cancer Sci Reports Cell‐free methylated DNA immunoprecipitation and high‐throughput sequencing (cfMeDIP‐seq) is a new bisulfite‐free technique, which can detect the whole‐genome methylation of blood cell‐free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung tumors and normal controls. Based on the top 300 DMR, we built a random forest prediction model, which was able to distinguish malignant lung tumors from normal controls with high sensitivity and specificity of 91.0% and 93.3% (AUROC curve of 0.963). In summary, we reported a non–invasive prediction model that had good ability to distinguish malignant pulmonary nodules. John Wiley and Sons Inc. 2021-07-21 2021-09 /pmc/articles/PMC8409309/ /pubmed/34251068 http://dx.doi.org/10.1111/cas.15052 Text en © 2021 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
| spellingShingle | Reports Qi, Jian Hong, Bo Tao, Rui Sun, Ruifang Zhang, Huanhu Zhang, Xiaopeng Ji, Jie Wang, Shujie Liu, Yanzhe Deng, Qingmei Wang, Hongzhi Zhao, Dahai Nie, Jinfu Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning |
| title | Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning |
| title_full | Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning |
| title_fullStr | Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning |
| title_full_unstemmed | Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning |
| title_short | Prediction model for malignant pulmonary nodules based on cfMeDIP‐seq and machine learning |
| title_sort | prediction model for malignant pulmonary nodules based on cfmedip‐seq and machine learning |
| topic | Reports |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409309/ https://www.ncbi.nlm.nih.gov/pubmed/34251068 http://dx.doi.org/10.1111/cas.15052 |
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