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Spheroid-Based 3D Models to Decode Cell Function and Matrix Effectors in Breast Cancer.

Sylvia Mangani1, Christos Koutsakis1, Nikolaos E Koletsis1

  • 1Biochemistry, Biochemical Analysis & Matrix Pathobiology Research Group, Laboratory of Biochemistry, Department of Chemistry, University of Patras, 26504 Patras, Greece.

Cancers
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a 3D breast cancer model using spheroids to better mimic the tumor microenvironment. The 3D model revealed phenotypic changes and distinct receptor and matrix molecule expression compared to 2D cultures, improving in vitro cancer cell behavior prediction.

Keywords:
3D breast cancer cell modelsextracellular matrixfunctional cell propertiesmatrix metalloproteinasesspheroidssyndecans

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Area of Science:

  • * Cancer Biology
  • * Tumor Microenvironment Research
  • * 3D Cell Culture Models

Background:

  • * Conventional 2D cell cultures inadequately replicate complex tumor microenvironments (TME).
  • * Three-dimensional (3D) cell models are crucial for studying dynamic cell-cell and cell-matrix interactions in tumors.
  • * Breast cancer necessitates advanced models due to its high incidence and mortality.

Purpose of the Study:

  • * Develop and characterize breast cancer cell-derived spheroids as a 3D model.
  • * Investigate phenotypic transitions and marker expression in 3D versus 2D cultures.
  • * Analyze key receptor and matrix molecule expression in breast cancer spheroids.

Main Methods:

  • * Utilized ERα-positive MCF-7 and ERβ-positive MDA-MB-231 breast cancer cell lines.
  • * Developed 3D spheroids and characterized their morphology and function.
  • * Assessed expression of epithelial-to-mesenchymal transition (EMT) markers and matrix molecules.

Main Results:

  • * Significant phenotypic transitions observed between 2D and 3D cultures, correlating with EMT markers.
  • * Spheroids displayed distinct expression profiles for estrogen receptors (ERs), EGFR, IGF1R, syndecans, and MMPs.
  • * Bioinformatic analysis highlighted the prognostic relevance of matrix regulators in breast cancer.

Conclusions:

  • * A simple yet informative 3D breast cancer model was successfully developed.
  • * The 3D model effectively captures key TME features.
  • * This model enhances the prediction of in vitro cancer cell behavior.