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Biomarker Categorization in Transcriptomic Meta-Analysis by Concordant Patterns With Application to Pan-Cancer
Zhenyao Ye1, Hongjie Ke1, Shuo Chen2
1Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States.
Frontiers in Genetics
|July 19, 2021
Summary
This study introduces a new meta-analysis approach to categorize biomarkers from multiple transcriptomic studies. It enhances biomarker discovery and functional understanding in Pan-cancer research by integrating diverse transcript types.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- High-throughput omics technologies generate vast public datasets.
- Meta-analysis of transcriptomic studies boosts statistical power and reproducibility.
- Biomarker categorization by differential expression (DE) patterns is crucial for understanding heterogeneity.
Purpose of the Study:
- To propose a novel meta-analysis method for categorizing biomarkers based on DE patterns across studies.
- To integrate multi-omics data (mRNA, miRNA, lncRNA) for comprehensive analysis.
- To identify biomarker categories with related functionality and regulatory relationships.
Main Methods:
- Developed a meta-analysis method considering concordant DE patterns, biological, and statistical significance.
- Enabled joint analysis of different transcript types (mRNA, miRNA, lncRNA).
- Performed integrative analysis including target enrichment and causal regulatory network construction.
Main Results:
- Applied the method to two Pan-cancer transcriptomic datasets.
- Successfully categorized biomarkers based on their DE patterns.
- Identified distinct biomarker categories with unique functions and regulatory links.
Conclusions:
- The proposed method effectively categorizes biomarkers by integrating multi-omics data.
- This approach facilitates downstream pathway enrichment and network analysis.
- It generates novel hypotheses for Pan-cancer research by revealing biomarker functionality and regulation.
