Integrative machine learning-driven prioritization of ceRNA networks in adrenocortical carcinomaOA

Adrenocortical carcinoma(ACC)is a rare and highly aggressive endocrine malignancy in urgent need of robust biomarkers and novel therapeutic targets.In this study,a machine learning(ML)-driven framework is introduced to systematically model microRNA-messenger RNAmiRNA-mRNA regulatory associations and reconstruct context-specific ceRNA networks in ACC,leveraging harmonized RNA-Seq and miRNA-Seq data from The Cancer Genome Atlas-ACC and Genotype-Tissue Expression(2025).Multiple regression models were benchmarked,with random forest achieving superior predictive performance(R^(2)=0.9467 for normal and 0.9044 for the tumor datasets),enabling accurate identification of high-confidence miRNA-mRNA interactions.Integration of ML predictions with established reference datasets(TargetScan and miRTarBase)was demonstrated to have strong biological validity,while subsequent network analyses revealed extensive topological rewiring and a loss of regulatory hub connectivity in tumor tissue versus normal adrenal tissue.Moreover,a set of new,highconfidence miRNA-mRNA interactions in ACC was identified,implicating new candidates in oncogenic signaling and extracellular matrix remodeling.Together,a robust resource is provided for prioritizing functional interactions and regulatory hubs for future experimental validation,and the power of integrative ML approaches is underscored in advancing systems-level understanding and biomarker discovery in rare cancers such as ACC.