Integration of Omics Data to Identify Cancer-Related MicroRNA.

MicroRNAs (miRNAs) are small noncoding RNAs that play critical regulatory roles in gene expression by targeting messenger RNAs (mRNAs) for degradation or translational inhibition. Their ability to modulate multiple genes simultaneously places them at the center of many biological processes, including those involved in tumorigenesis. Depending on their targets, miRNAs can act as oncogenes or tumor suppressors, contributing to cancer development and progression. Advances in high-throughput sequencing now allow simultaneous profiling of miRNA and mRNA expression in the same biological samples. When properly integrated, these datasets can uncover functional miRNA-mRNA regulatory networks, reveal cancer-specific signatures, and identify novel biomarkers or therapeutic targets. However, the integrative analysis of miRNA and mRNA expression data remains computationally challenging and requires well-structured pipelines and rigorous statistical approaches. In this chapter, we present updated computational strategies to identify biologically relevant miRNA-mRNA interactions through expression correlation, target prediction, and functional enrichment. The chapter includes step-by-step protocols, example R code, and recommendations for integrating additional data layers such as copy number variation and DNA methylation, with the goal of improving the robustness and interpretability of miRNA-based analyses in cancer.