Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm
Polyunsaturated fatty acid (PUFA) metabolism is currently a focus in cancer research due to PUFAs functioning as structural components of the membrane matrix, as fuel sources for energy production, and as sources of secondary messengers, so called oxylipins, important players of inflammatory process...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/19/4663 |
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| author | Mariia V. Guryleva Dmitry D. Penzar Dmitry V. Chistyakov Andrey A. Mironov Alexander V. Favorov Marina G. Sergeeva |
| author_facet | Mariia V. Guryleva Dmitry D. Penzar Dmitry V. Chistyakov Andrey A. Mironov Alexander V. Favorov Marina G. Sergeeva |
| author_sort | Mariia V. Guryleva |
| collection | DOAJ |
| description | Polyunsaturated fatty acid (PUFA) metabolism is currently a focus in cancer research due to PUFAs functioning as structural components of the membrane matrix, as fuel sources for energy production, and as sources of secondary messengers, so called oxylipins, important players of inflammatory processes. Although breast cancer (BC) is the leading cause of cancer death among women worldwide, no systematic study of PUFA metabolism as a system of interrelated processes in this disease has been carried out. Here, we implemented a Boruta-based feature selection algorithm to determine the list of most important PUFA metabolism genes altered in breast cancer tissues compared with in normal tissues. A rank-based Random Forest (RF) model was built on the selected gene list (33 genes) and applied to predict the cancer phenotype to ascertain the PUFA genes involved in cancerogenesis. It showed high-performance of dichotomic classification (balanced accuracy of 0.94, ROC AUC 0.99) We also retrieved a list of the important PUFA genes (46 genes) that differed between molecular subtypes at the level of breast cancer molecular subtypes. The balanced accuracy of the classification model built on the specified genes was 0.82, while the ROC AUC for the sensitivity analysis was 0.85. Specific patterns of PUFA metabolic changes were obtained for each molecular subtype of breast cancer. These results show evidence that (1) PUFA metabolism genes are critical for the pathogenesis of breast cancer; (2) BC subtypes differ in PUFA metabolism genes expression; and (3) the lists of genes selected in the models are enriched with genes involved in the metabolism of signaling lipids. |
| first_indexed | 2024-03-09T21:57:56Z |
| format | Article |
| id | doaj.art-ab338b6ff873410499443a8507d36c2f |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T21:57:56Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-ab338b6ff873410499443a8507d36c2f2023-11-23T19:54:32ZengMDPI AGCancers2072-66942022-09-011419466310.3390/cancers14194663Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest AlgorithmMariia V. Guryleva0Dmitry D. Penzar1Dmitry V. Chistyakov2Andrey A. Mironov3Alexander V. Favorov4Marina G. Sergeeva5Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234 Moscow, RussiaFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234 Moscow, RussiaBelozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119992 Moscow, RussiaFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, 119234 Moscow, RussiaVavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, RussiaBelozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119992 Moscow, RussiaPolyunsaturated fatty acid (PUFA) metabolism is currently a focus in cancer research due to PUFAs functioning as structural components of the membrane matrix, as fuel sources for energy production, and as sources of secondary messengers, so called oxylipins, important players of inflammatory processes. Although breast cancer (BC) is the leading cause of cancer death among women worldwide, no systematic study of PUFA metabolism as a system of interrelated processes in this disease has been carried out. Here, we implemented a Boruta-based feature selection algorithm to determine the list of most important PUFA metabolism genes altered in breast cancer tissues compared with in normal tissues. A rank-based Random Forest (RF) model was built on the selected gene list (33 genes) and applied to predict the cancer phenotype to ascertain the PUFA genes involved in cancerogenesis. It showed high-performance of dichotomic classification (balanced accuracy of 0.94, ROC AUC 0.99) We also retrieved a list of the important PUFA genes (46 genes) that differed between molecular subtypes at the level of breast cancer molecular subtypes. The balanced accuracy of the classification model built on the specified genes was 0.82, while the ROC AUC for the sensitivity analysis was 0.85. Specific patterns of PUFA metabolic changes were obtained for each molecular subtype of breast cancer. These results show evidence that (1) PUFA metabolism genes are critical for the pathogenesis of breast cancer; (2) BC subtypes differ in PUFA metabolism genes expression; and (3) the lists of genes selected in the models are enriched with genes involved in the metabolism of signaling lipids.https://www.mdpi.com/2072-6694/14/19/4663breast cancermachine learningPUFAstranscriptomicsrandom forest |
| spellingShingle | Mariia V. Guryleva Dmitry D. Penzar Dmitry V. Chistyakov Andrey A. Mironov Alexander V. Favorov Marina G. Sergeeva Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm Cancers breast cancer machine learning PUFAs transcriptomics random forest |
| title | Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm |
| title_full | Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm |
| title_fullStr | Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm |
| title_full_unstemmed | Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm |
| title_short | Investigation of the Role of PUFA Metabolism in Breast Cancer Using a Rank-Based Random Forest Algorithm |
| title_sort | investigation of the role of pufa metabolism in breast cancer using a rank based random forest algorithm |
| topic | breast cancer machine learning PUFAs transcriptomics random forest |
| url | https://www.mdpi.com/2072-6694/14/19/4663 |
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