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Published on: February 14, 2025
Hierarchical structural component modeling of microRNA-mRNA integration analysis
Yongkang Kim1, Sungyoung Lee2, Sungkyoung Choi2
1Department of Statistics, Seoul National University, Seoul, Korea.
Background:
Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification.
Results:
It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration ("HisCoM-mimi") model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods.
Conclusion:
As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation.
Insights
This study introduces HisCoM-mimi, a novel model for identifying integrated microRNA-mRNA markers. The model successfully identified diagnostic markers for pancreatic cancer, advancing personalized medicine.
Area of Science:
- Bioinformatics
- Genomics
- Molecular Biology
Background:
- Identifying multi-markers is crucial for personalized medicine, but current methods struggle with integrating diverse omics data.
- MicroRNAs regulate phenotypes indirectly by controlling mRNA expression and protein translation.
Purpose of the Study:
- To develop a model for identifying integrated microRNA-mRNA markers.
- To address the challenge of identifying multi-markers from different omics data types.
Main Methods:
- Proposed a hierarchical structured component analysis of microRNA-mRNA integration (HisCoM-mimi) model.
- Accounted for the biological relationship between microRNAs and mRNA expression.
- Evaluated performance using simulation studies and real-world pancreatic ductal adenocarcinoma (PDAC) data.
Main Results:
- HisCoM-mimi demonstrated superior performance compared to three other methods in simulation studies.
- The model successfully identified informative microRNA-mRNA integration sets for PDAC diagnosis using real data.
- Identified integrated miRNA/target mRNA pairs as effective markers for early diagnosis.
Conclusions:
- The HisCoM-mimi model effectively identifies integrated miRNA/target mRNA pairs as diagnostic markers.
- The model provides a broader biological interpretation for early cancer diagnosis.
- This approach enhances marker identification in the era of personalized medicine.
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