einstein (São Paulo). 07/Apr/2025;23:eAO1372.

Comparative analysis of miRNA-mRNA interaction prediction tools based on experimental head and neck cancer data

Bárbara dos Santos

Dias

, Larissa Figueiredo Alves

Diniz

, Lucca D’Arco

Corrêa

, Rafael Pereira de

Souza

, Leticia Torres

Ferreira

, Denise da Cunha

Pasqualin

, Rafael de

Cicco

, Eloiza Helena Tajara da

Silva

, Patricia

Severino

DOI: 10.31744/einstein_journal/2025AO1372

Highlights

■ miRWalk had the highest predicted interactions and validated miRNA networks in HNSCC.
■ Around 3.3% of interactions overlapped across tools, emphasizing the need for multitool approaches.
■ Dysregulated genes and miRNAs were tied to cancerdriving PI3K-Akt and Wnt pathways.
■ The validated approach highlights the importance of integrating computational and molecular data.

ABSTRACT

Objective:

Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis largely due to late diagnosis and a lack of reliable biomarkers. MicroRNAs (miRNAs), small non-coding RNAs that regulate gene expression, are promising biomarkers for HNSCC. This study evaluated miRNA-mRNA interactions in HNSCC using conventional computational tools and validated the results using molecular data.

Methods:

We compared three miRNA-mRNA interaction prediction tools, TargetScan, miRDB, and miRWalk, using differentially expressed miRNAs and mRNAs from HNSCC and cancer-free tissues. NanoString nCounter was used to measure miRNA and
mRNA expression and the miRTarBase database was used to validate the predicted miRNA-mRNA interactions.

Results:

TargetScan and miRWalk provide a comprehensive overview of potential interactions, whereas miRDB provides functional insights. Our results identified 77 and 154 differentially expressed miRNAs and mRNAs in HNSCC, respectively. miRWalk predicted the highest number of miRNA-mRNA interactions, followed by miRDB and TargetScan. Only 3.3% of interactions were common among the tools. The MiRTarBase analysis confirmed a small subset of the predictions. Biological pathway analysis highlighted the dysregulation of PI3K-Akt and Wnt signaling; miRWalk was the best for elucidating how miRNAs modulate target mRNAs in these key pathways during HNSCC progression.

Conclusion:

miRWalk emerged as the most robust tool for predicting miRNA-mRNA interactions. Our findings highlight the importance of integrating bioinformatics predictions with experimental data to better understand the regulatory networks in HNSCC and identify potential biomarkers for diagnosis and therapy.

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