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Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes

Author

Listed:
  • Linder Henry

    (Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA)

  • Zhang Yuping

    (Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA)

  • Wang Yunqi

    (Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA)

  • Ouyang Zhengqing

    (Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, 01003, USA)

Abstract

Developments in biotechnologies enable multi-platform data collection for functional genomic units apart from the gene. Profiling of non-coding microRNAs (miRNAs) is a valuable tool for understanding the molecular profile of the cell, both for canonical functions and malignant behavior due to complex diseases. We propose a graphical mixed-effects statistical model incorporating miRNA-gene target relationships. We implement an integrative pathway analysis that leverages measurements of miRNA activity for joint analysis with multimodal observations of gene activity including gene expression, methylation, and copy number variation. We apply our analysis to a breast cancer dataset, and consider differential activity in signaling pathways across breast tumor subtypes. We offer discussion of specific signaling pathways and the effect of miRNA integration, as well as publish an interactive data visualization to give public access to the results of our analysis.

Suggested Citation

  • Linder Henry & Zhang Yuping & Wang Yunqi & Ouyang Zhengqing, 2024. "Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 23(1), pages 1-12.
    • Handle: RePEc:bpj:sagmbi:v:23:y:2024:i:1:p:12:n:1001
    DOI: 10.1515/sagmb-2019-0050
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