Evidence map›Paper›PMID 40577268›Full record

ArticlePloS one2025

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

Miray Unlu Yazici, J S Marron, Burcu Bakir-Gungor, Fei Zou, Malik Yousef

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Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Miray Unlu YaziciDepartment of Bioengineering, Abdullah Gül University, Kayseri, Turkey.
J S MarronDepartment of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina, United States of America.
Burcu Bakir-GungorDepartment of Bioengineering, Abdullah Gül University, Kayseri, Turkey.
Fei ZouDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Malik YousefDepartment of Information Systems, Zefat Academic College, Zefat, Israel.ORCID 0000-0001-8780-6303

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Breast NeoplasmsGenomicsAlgorithmsBiomarkers, TumorFemaleHumansMachine LearningMultiomicsPrognosisBiomarkers, Tumor

Identifiers

PMID40577268
PMCPMC12204537

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.