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Related Experiment Video

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection.

Joseph Pateras1, Musaddiq Lodi2, Pratip Rana3

  • 1Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.

International Journal of Molecular Sciences
|February 26, 2025
PubMed
Summary

This study integrates multiomics data for cancer research, developing a novel framework for breast cancer subtype identification and biomarker discovery. The approach optimizes patient clustering and enhances survival analysis, paving the way for personalized cancer treatments.

Keywords:
cancer subtypinggene expression profilingmachine learning methodsmultiomics data integrationsurvival analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Multiomics data integration is essential for advancing cancer research due to the increasing volume and diversity of '-omics' datasets.
  • Understanding cancer subtypes and identifying reliable biomarkers are critical for effective diagnosis and personalized treatment strategies.

Purpose of the Study:

  • To develop and validate a computational framework for joint latent variable modeling of multiomics patient profiles.
  • To enable efficient and computationally inexpensive patient clustering for cancer subtype discovery.
  • To identify potential biomarkers and gain insights into the molecular mechanisms underlying breast cancer subtypes.

Main Methods:

  • Adaptation of the expectation-maximization algorithm for joint latent variable modeling of multiomics data.
  • Integration of biological feature selection with latent distribution optimization for patient clustering.
  • Differential gene expression analysis, pathway enrichment, and Gene Ontology (GO) Semantic Similarity analysis for biomarker identification.

Main Results:

  • The framework successfully clusters patients, optimizing latent distributions and improving survival analysis and runtime performance.
  • Identification of key genes in breast cancer (BRCA) patients: COL10A1 (upregulated, poor prognosis) and CDG300LG (downregulated, brain metastasis link).
  • Pathway enrichment revealed conserved cellular matrix organization pathways and subtype-specific differences in proliferation and endocytosis; MATN2 identified as a potential biomarker.

Conclusions:

  • The proposed framework provides an efficient method for distinguishing cancer subtypes and discovering biomarkers from multiomics data.
  • The identified genes and pathways offer insights into breast cancer progression and metastasis, supporting personalized treatment approaches.
  • The study highlights the potential of multiomics integration for uncovering complex relationships within cancer subtypes and guiding therapeutic strategies.