An integrative multi-omics modeling framework for chemotherapy response prediction through microRNA-target regulatory signatures.

Chemotherapy remains a cornerstone of cancer treatment, yet therapeutic resistance continues to limit durable clinical responses. MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression that orchestrate diverse cellular processes implicated in drug sensitivity and resistance. However, the contribution of miRNA regulatory networks to chemotherapy response has not been systematically characterized using clinical patient cohorts. Here we present DrugMiR, an integrative multi-omics modeling framework that integrates drug response-associated miRNAs with experimentally validated target genes to identify regulatory signatures predictive of chemotherapy response. Using 1015 patient samples from The Cancer Genome Atlas, we identified differentially expressed miRNAs (DEmiRs) associated with response to five widely used chemotherapeutic agents (cisplatin, carboplatin, gemcitabine, paclitaxel, and fluorouracil) and constructed predictive models using multiple machine learning algorithms. DrugMiR achieved robust predictive performance, particularly for platinum-based therapies, with area under the receiver operating characteristic curves of 0.81 for cisplatin and 0.79 for carboplatin. Integration of validated miRNA targets consistently improved predictive performance while substantially enhancing biological interpretability. Integrative network and pathway analyses identified recurrent miRNA-target regulatory programs shared across chemotherapy cohorts, particularly involving the miR-200 family and target genes such as BMP2, ITGA3, EGFR, FGFR1, MET, and IL6, implicating growth factor signaling, focal adhesion, extracellular matrix remodeling, and adaptive survival pathways in chemotherapy response. Together, these findings establish patient-derived miRNA-target regulatory signatures as robust predictors of chemotherapy response and provide a multi-omics framework for elucidating molecular mechanisms underlying therapeutic sensitivity and resistance across diverse cancer types.