3Mont: A multi-omics integrative tool for breast cancer subtype stratification.
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, espec...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326154 |
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| author | Miray Unlu Yazici J S Marron Burcu Bakir-Gungor Fei Zou Malik Yousef |
| author_facet | Miray Unlu Yazici J S Marron Burcu Bakir-Gungor Fei Zou Malik Yousef |
| author_sort | Miray Unlu Yazici |
| collection | DOAJ |
| description | Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git. |
| format | Article |
| id | doaj-art-1f0abcd626244116bf3768f04c1d3f8a |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1f0abcd626244116bf3768f04c1d3f8a2025-08-20T02:36:15ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032615410.1371/journal.pone.03261543Mont: A multi-omics integrative tool for breast cancer subtype stratification.Miray Unlu YaziciJ S MarronBurcu Bakir-GungorFei ZouMalik YousefBreast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git.https://doi.org/10.1371/journal.pone.0326154 |
| spellingShingle | Miray Unlu Yazici J S Marron Burcu Bakir-Gungor Fei Zou Malik Yousef 3Mont: A multi-omics integrative tool for breast cancer subtype stratification. PLoS ONE |
| title | 3Mont: A multi-omics integrative tool for breast cancer subtype stratification. |
| title_full | 3Mont: A multi-omics integrative tool for breast cancer subtype stratification. |
| title_fullStr | 3Mont: A multi-omics integrative tool for breast cancer subtype stratification. |
| title_full_unstemmed | 3Mont: A multi-omics integrative tool for breast cancer subtype stratification. |
| title_short | 3Mont: A multi-omics integrative tool for breast cancer subtype stratification. |
| title_sort | 3mont a multi omics integrative tool for breast cancer subtype stratification |
| url | https://doi.org/10.1371/journal.pone.0326154 |
| work_keys_str_mv | AT mirayunluyazici 3montamultiomicsintegrativetoolforbreastcancersubtypestratification AT jsmarron 3montamultiomicsintegrativetoolforbreastcancersubtypestratification AT burcubakirgungor 3montamultiomicsintegrativetoolforbreastcancersubtypestratification AT feizou 3montamultiomicsintegrativetoolforbreastcancersubtypestratification AT malikyousef 3montamultiomicsintegrativetoolforbreastcancersubtypestratification |