Literature-based validation of miRNA targeting in lung cancer: a computational study

Computational study with secondary data validation

Abstract

Background: Lung cancer is a leading cause of cancer-related mortality globally. MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression and represent promising therapeutic targets. Computational prediction of miRNA targets is common, but these predictions require rigorous validation.

Objectives: To identify miRNAs targeting frequently mutated genes in lung cancer (EGFR, ERBB2, KRAS, TP53) using computational tools and to validate these interactions through a comprehensive review of existing experimental literature.

Methods: This study is an in silico bioinformatics analysis combined with literature-based validation of predicted miRNA–gene interactions in lung cancer. miRNA targets were predicted using three algorithms: miRanda, TargetScan and RNAhybrid. The resulting candidates were then validated by mining experimental evidence from published literature (PubMed) and curated databases (miRTarBase, TarBase, DIANA Tools). Validation criteria included direct evidence from experiments such as luciferase reporter assays, qPCR, and Western blotting.

Results: Six candidate miRNAs were identified from the computational prediction. Literature validation confirmed strong experimental evidence for the role of miR-93 and miR-939 in regulating their respective target genes (EGFR, TP53, ERBB2) in lung cancer. The remaining miRNAs (miR-765, miR-1273, miR-887, miR-1285) lacked sufficient direct experimental support.

Conclusion: The integration of computational prediction with literature-based validation efficiently prioritizes high- confidence miRNA targets. This study identifies miR-93 and miR-939 as robustly validated miRNAs for key lung cancer genes, highlighting their potential for further translational investigation. The approach underscores the necessity of experimental validation to complement in silico findings.