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

Linder Henry, Zhang Yuping (), Wang Yunqi and Ouyang Zhengqing
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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

Statistical Applications in Genetics and Molecular Biology, 2024, vol. 23, issue 1, 12

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.

Keywords: pathway analysis; data integration; network topology; genomics; visualization (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/sagmb-2019-0050

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