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

  • Henry Linder , Yuping Zhang ORCID logo EMAIL logo , Yunqi Wang and Zhengqing Ouyang ORCID logo
Published/Copyright: February 19, 2024
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 23 Issue 1

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
Corresponding author: Yuping Zhang, Department of Statistics, University of Connecticut, Storrs, CT, 06269, USA, E-mail:

Funding source: Yuping Zhang acknowledges Faculty Research Excellence Program Award from University of Connecticut

Acknowledgments

The authors acknowledge the suggestions from anonymous reviewers.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for theentire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: Yuping Zhang acknowledges Faculty ResearchExcellence Program Award from University of Connecticut.

  5. Data availability: Not applicable.

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Received: 2019-10-06
Accepted: 2024-01-27
Published Online: 2024-02-19

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Empirically adjusted fixed-effects meta-analysis methods in genomic studies
  4. A CNN-CBAM-BIGRU model for protein function prediction
  5. A heavy-tailed model for analyzing miRNA-seq raw read counts
  6. Flexible model-based non-negative matrix factorization with application to mutational signatures
  7. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data
  8. Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets
  9. A global test of hybrid ancestry from genome-scale data
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https://doi.org/10.1515/sagmb-2019-0050
Keywords for this article
pathway analysis; data integration; network topology; genomics; visualization

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Empirically adjusted fixed-effects meta-analysis methods in genomic studies
  4. A CNN-CBAM-BIGRU model for protein function prediction
  5. A heavy-tailed model for analyzing miRNA-seq raw read counts
  6. Flexible model-based non-negative matrix factorization with application to mutational signatures
  7. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data
  8. Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets
  9. A global test of hybrid ancestry from genome-scale data
  10. Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes
  11. Bayesian LASSO for population stratification correction in rare haplotype association studies
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