Regulatory QTLs affecting miRNA-mRNA interactions in cancer: mechanisms, methods, and clinical implications - Kumar, Vivek; Shukla, Rohit; Chaudhary, Amit; Gautam, Mansi (Frontiers in Molecular Biosciences, 2026)
MicroRNAs (miRNAs) are post-transcriptional regulators that play essential roles in cancer initiation, progression, and therapy response. Single nucleotide polymorphisms (SNPs) that affect miRNA-mRNA interactions, termed regulatory quantitative trait loci (regQTLs), have emerged as critical modulators of gene expression landscapes in tumors. These regQTLs can disrupt or enhance miRNA binding to target sites, modulate transcript stability, and influence oncogenic or tumor-suppressive pathways, thus shaping individual cancer susceptibility and clinical outcomes. In this review, we comprehensively examine the biological, computational, and translational aspects of regQTLs in cancer. We summarize key computational approaches used to investigate germline influences on miRNA-mediated regulation, including interaction-based regQTL models, miR- and isomiR-eQTL analyses, and sequence-based prediction tools. We further discuss emerging miRNA-TWAS methods, which do not directly detect regQTLs but provide a valuable upstream strategy by identifying genetically regulated miRNAs that may participate in downstream regQTL interactions. We also summarize publicly available datasets and annotation platforms supporting large-scale discovery efforts. Through critical evaluation of recent experimental validations and clinical association studies, we highlight regQTLs that serve as biomarkers for prognosis and therapy response in diverse cancers such as breast, lung, prostate, and colorectal. Furthermore, we explore the therapeutic potential of targeting miRNA–SNP interactions, including emerging strategies in miRNA-tailored immunotherapies and mRNA vaccines. We propose a strategic roadmap for future research, emphasizing the need for population-specific analyses, single-cell regQTL mapping, and mechanistic dissection using multi-omic models. By connecting genetic variation, regulatory biology, and clinical translation, this review provides a foundational framework to harness miRNA-regulatory QTLs for precision oncology.