Cancerin: A computational pipeline to infer cancer-associated ceRNA interaction networks
1Department of Mathematics, Statistics, and Computer Science, Marquette University, Milwaukee, Wisconsin, United States of America.
Abstract:
MicroRNAs (miRNAs) inhibit expression of target genes by binding to their RNA transcripts. It has been recently shown that RNA transcripts targeted by the same miRNA could "compete" for the miRNA molecules and thereby indirectly regulate each other. Experimental evidence has suggested that the aberration of such miRNA-mediated interaction between RNAs-called competing endogenous RNA (ceRNA) interaction-can play important roles in tumorigenesis. Given the difficulty of deciphering context-specific miRNA binding, and the existence of various gene regulatory factors such as DNA methylation and copy number alteration, inferring context-specific ceRNA interactions accurately is a computationally challenging task. Here we propose a computational method called Cancerin to identify cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation, copy number alteration, gene and miRNA expression datasets to construct cancer-specific ceRNA networks. We applied Cancerin to three cancer datasets from the Cancer Genome Atlas (TCGA) project. Our results indicated that ceRNAs were enriched with cancer-related genes, and ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. Using LINCS-L1000 shRNA-mediated gene knockdown experiment in breast cancer cell line to assess accuracy, Cancerin was able to predict expression outcome of ceRNA genes with high accuracy.
Insights
Researchers developed Cancerin, a computational method to identify cancer-associated competing endogenous RNA (ceRNA) interactions. This approach integrates multiple genomic data types to build cancer-specific ceRNA networks, aiding cancer research.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- MicroRNAs (miRNAs) regulate gene expression by binding to RNA transcripts.
- Competing endogenous RNA (ceRNA) interactions, where RNAs compete for miRNAs, are implicated in tumorigenesis.
- Accurate inference of context-specific ceRNA interactions is computationally challenging due to regulatory complexity.
Purpose of the Study:
- To develop a computational method, Cancerin, for identifying cancer-associated ceRNA interactions.
- To construct cancer-specific ceRNA networks by integrating diverse genomic data.
- To assess the biological relevance and predictive accuracy of inferred ceRNA networks.
Main Methods:
- Developed Cancerin, a computational tool for ceRNA network inference.
- Integrated DNA methylation, copy number alteration, and gene/miRNA expression data.
- Applied Cancerin to Cancer Genome Atlas (TCGA) datasets and validated using LINCS-L1000 experiments.
Main Results:
- Inferred ceRNA networks revealed enrichment of cancer-related genes within ceRNAs.
- Identified ceRNA modules associated with cancer-related biological processes.
- Cancerin accurately predicted expression outcomes of ceRNA genes in a breast cancer cell line.
Conclusions:
- Canercin provides a robust method for identifying cancer-specific ceRNA networks.
- The inferred networks highlight the role of ceRNA interactions in cancer biology.
- This approach has potential for advancing cancer diagnostics and therapeutics.
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