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Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

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Identifying Gene Predictors of Chemicals Linked With Breast Cancer: A Machine Learning Analysis of MCF7 Cellular

Lauren E Koval1,2, Richard Judson3, Julia E Rager1,2,4

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Environmental and Molecular Mutagenesis
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This study identifies potentially harmful environmental chemicals linked to breast cancer risk using machine learning on cell data. It prioritizes chemicals for further research, aiding in understanding breast cancer causes.

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Area of Science:

  • Environmental Health
  • Genomics
  • Computational Biology

Background:

  • Breast cancer is the most common cancer in women, with environmental chemical exposure as a suspected factor.
  • Many chemicals lack evaluation for their potential role in breast cancer development.
  • Understanding chemical-environment-cancer links is crucial for public health.

Purpose of the Study:

  • To identify and prioritize understudied chemicals for breast cancer risk assessment.
  • To develop a predictive model for chemical-breast cancer associations using transcriptomic data.
  • To investigate the biological pathways and genes involved in chemical-induced breast cancer risk.

Main Methods:

  • RNA sequencing of MCF7 breast cancer cells exposed to hundreds of chemicals.
  • Machine learning models trained on transcriptomic and physicochemical data to classify chemicals.
  • Analysis of gene expression data in The Cancer Genome Atlas (TCGA) for validation.

Main Results:

  • A machine learning model achieved 80% balanced accuracy in distinguishing known breast cancer chemicals from non-associated ones.
  • 170 genes, including CLSPN, RUNX2, and UBN2, were identified as key contributors to the model.
  • 97 understudied chemicals, including biocides and dyes, were predicted to have breast cancer-associated profiles.
  • In vitro findings showed overlap with alterations in human breast cancer samples from TCGA.

Conclusions:

  • This study provides a framework for prioritizing chemicals for breast cancer risk evaluation.
  • Identified genes and pathways (inflammation, ferroptosis, proliferation) offer insights into mechanisms of chemical carcinogenesis.
  • The findings highlight specific chemicals and biological alterations relevant to human breast cancer.