LAceModule: Identification of Competing Endogenous RNA Modules by Integrating Dynamic Correlation
Xiao Wen1, Lin Gao1, Yuxuan Hu1
1School of Computer Science and Technology, Xidian University, Xi'an, China.
Frontiers in Genetics
|April 8, 2020
Summary
Competing endogenous RNAs (ceRNAs) are regulated by shared microRNAs. A new method, liquid association (LA), improves ceRNA detection and identifies cancer-related modules, offering potential therapeutic targets.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Competing endogenous RNAs (ceRNAs) are crucial for post-transcriptional regulation in physiological and pathological processes.
- Existing computational methods for ceRNA identification overlook the sensitivity of correlations to microRNA expression levels.
Purpose of the Study:
- To introduce a dynamic correlation measure, liquid association (LA), to enhance ceRNA pair detection.
- To develop an LA-based framework (LAceModule) for identifying ceRNA modules.
Main Methods:
- Analysis of LA's effect on ceRNA pair detection.
- Integration of Pearson correlation and LA with multi-view non-negative matrix factorization in the LAceModule framework.
- Application to breast and liver cancer datasets.
Main Results:
- Liquid association (LA) proves effective for detecting ceRNA pairs and modules.
- Identified ceRNA modules are implicated in cell adhesion, migration, and communication.
- LAceModule successfully identified biologically relevant ceRNA modules.
Conclusions:
- ceRNAs play significant roles in cancer development and progression.
- LA is a valuable metric for improving ceRNA identification.
- ceRNAs represent promising targets for cancer diagnostics and therapeutics.
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