Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study
Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic...
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MDPI AG
2021-11-01
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| Online Access: | https://www.mdpi.com/2073-4425/12/12/1872 |
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| author | Yingxia Li Ulrich Mansmann Shangming Du Roman Hornung |
| author_facet | Yingxia Li Ulrich Mansmann Shangming Du Roman Hornung |
| author_sort | Yingxia Li |
| collection | DOAJ |
| description | Lung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic data information or a small number of different omics data types concurrently for generating predictive models. A few of them have considered multi-omics data from genome to proteome. We used a publicly available dataset to illustrate the potential of multi-omics data for stage prediction in LUAD. In particular, we investigated the roles of the specific omics data types in the prediction process. We used a self-developed method, Omics-MKL, for stage prediction that combines an existing feature ranking technique Minimum Redundancy and Maximum Relevance (mRMR), which avoids redundancy among the selected features, and multiple kernel learning (MKL), applying different kernels for different omics data types. Each of the considered omics data types individually provided useful prediction results. Moreover, using multi-omics data delivered notably better results than using single-omics data. Gene expression and methylation information seem to play vital roles in the staging of LUAD. The Omics-MKL method retained 70 features after the selection process. Of these, 21 (30%) were methylation features and 34 (48.57%) were gene expression features. Moreover, 18 (25.71%) of the selected features are known to be related to LUAD, and 29 (41.43%) to lung cancer in general. Using multi-omics data from genome to proteome for predicting the stage of LUAD seems promising because each omics data type may improve the accuracy of the predictions. Here, methylation and gene expression data may play particularly important roles. |
| first_indexed | 2024-03-10T04:02:43Z |
| format | Article |
| id | doaj.art-ba66b56547674b6fab8b6e37dafb4f59 |
| institution | Directory Open Access Journal |
| issn | 2073-4425 |
| language | English |
| last_indexed | 2024-03-10T04:02:43Z |
| publishDate | 2021-11-01 |
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| record_format | Article |
| series | Genes |
| spelling | doaj.art-ba66b56547674b6fab8b6e37dafb4f592023-11-23T08:29:41ZengMDPI AGGenes2073-44252021-11-011212187210.3390/genes12121872Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative StudyYingxia Li0Ulrich Mansmann1Shangming Du2Roman Hornung3Institute of Medical Informatics, Biometry and Epidemiology, University of Munich, 81377 Munich, GermanyInstitute of Medical Informatics, Biometry and Epidemiology, University of Munich, 81377 Munich, GermanyInstitute of Medical Informatics, Biometry and Epidemiology, University of Munich, 81377 Munich, GermanyInstitute of Medical Informatics, Biometry and Epidemiology, University of Munich, 81377 Munich, GermanyLung adenocarcinoma (LUAD) is a common and very lethal cancer. Accurate staging is a prerequisite for its effective diagnosis and treatment. Therefore, improving the accuracy of the stage prediction of LUAD patients is of great clinical relevance. Previous works have mainly focused on single genomic data information or a small number of different omics data types concurrently for generating predictive models. A few of them have considered multi-omics data from genome to proteome. We used a publicly available dataset to illustrate the potential of multi-omics data for stage prediction in LUAD. In particular, we investigated the roles of the specific omics data types in the prediction process. We used a self-developed method, Omics-MKL, for stage prediction that combines an existing feature ranking technique Minimum Redundancy and Maximum Relevance (mRMR), which avoids redundancy among the selected features, and multiple kernel learning (MKL), applying different kernels for different omics data types. Each of the considered omics data types individually provided useful prediction results. Moreover, using multi-omics data delivered notably better results than using single-omics data. Gene expression and methylation information seem to play vital roles in the staging of LUAD. The Omics-MKL method retained 70 features after the selection process. Of these, 21 (30%) were methylation features and 34 (48.57%) were gene expression features. Moreover, 18 (25.71%) of the selected features are known to be related to LUAD, and 29 (41.43%) to lung cancer in general. Using multi-omics data from genome to proteome for predicting the stage of LUAD seems promising because each omics data type may improve the accuracy of the predictions. Here, methylation and gene expression data may play particularly important roles.https://www.mdpi.com/2073-4425/12/12/1872multi-omics datalung adenocarcinomaMKLmRMR |
| spellingShingle | Yingxia Li Ulrich Mansmann Shangming Du Roman Hornung Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study Genes multi-omics data lung adenocarcinoma MKL mRMR |
| title | Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study |
| title_full | Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study |
| title_fullStr | Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study |
| title_full_unstemmed | Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study |
| title_short | Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative Study |
| title_sort | synergistic effects of different levels of genomic data for the staging of lung adenocarcinoma an illustrative study |
| topic | multi-omics data lung adenocarcinoma MKL mRMR |
| url | https://www.mdpi.com/2073-4425/12/12/1872 |
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