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MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to
Xianbin Li1, Hannan Ai1,2,3, Bizhou Li1
1State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Frontiers in Genetics
|February 14, 2022
Summary
A new method, MIMRDA, integrates miRNA and mRNA expression profiles to identify key cancer-related microRNAs (miRNAs) for improved diagnosis and treatment. This approach successfully identified potential miRNA biomarkers from TCGA datasets, showing promise for cancer research.
Area of Science:
- Bioinformatics
- Genomics
- Cancer Research
Background:
- Accurate identification of cancer-related microRNAs (miRNAs) targeting mRNAs is crucial for cancer diagnosis and treatment.
- Integrating diverse databases presents a significant challenge in discovering novel candidate miRNAs.
Purpose of the Study:
- To introduce MIMRDA, a novel computational method for predicting miRNA-disease associations by integrating miRNA and mRNA expression profiles.
- To identify key miRNAs with potential biomarker utility in various cancer types.
Main Methods:
- Developed and applied the MIMRDA method to analyze The Cancer Genome Atlas (TCGA) datasets across 20 cancer types.
- Validated identified miRNAs using public databases (TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR, mTD) and literature evidence.
- Assessed biomarker potential through function similarity, overall survival, and anti-cancer drug sensitivity analyses.
Main Results:
- Identified hundreds of top-ranked candidate miRNAs, categorized by database endorsement or literature support.
- miR-21 and miR-1258 demonstrated excellent biomarker characteristics in multi-dimensional assessments.
- MIMRDA outperformed Limma and SPIA packages and showed high accuracy in Random Forest simulations.
Conclusions:
- The MIMRDA method is superior and effective for identifying key miRNAs and predicting miRNA-disease associations.
- Top-ranked miRNAs identified by MIMRDA show potential as biomarkers warranting further experimental validation.
- This approach facilitates the integration of multi-omics data for advancing cancer diagnostics and therapeutics.
