BACKGROUND
Cyclin-dependent kinase 9 (CDK9) is a central regulator of RNA polymerase II elongation and has emerged as a therapeutic target in tumors characterized by transcriptional addiction. Growing interest in selective inhibitors and targeted degraders has renewed attention to the translational potential of CDK9-directed therapy.
AREAS COVERED
This review summarizes the molecular functions of the CDK9/positive transcription elongation factor b (P-TEFb) axis, its role in super-enhancer-driven oncogenic programs, and the mechanisms by which CDK9 inhibition promotes apoptosis, epigenetic derepression, and tumor microenvironment remodeling. We also discuss representative small-molecule inhibitors and proteolysis-targeting chimera (PROTAC) degraders, emerging biomarkers for patient stratification, rational combination strategies, and the current landscape of resistance mechanisms.
EXPERT OPINION
Selective targeting of CDK9 offers a promising route for treating refractory malignancies, particularly when guided by transcriptional dependency, biomarker-informed dosing, and rational combination design. Future progress will likely depend on improving therapeutic index, refining translational biomarkers, and anticipating adaptive resistance during clinical development.
Tiyao Liu, Zhongran Liu, Wen-Jing Wei et al.· Expert opinion on therapeuti...· 0 citations
Noncoding RNAs (ncRNAs) are critical regulators of drug response and disease progression, making accurate prediction of ncRNA-drug resistance associations a key task in pharmacogenomics and precision medicine. However, current methods largely rely on global neighborhood aggregation, which treats node contexts as homogeneous and overlooks fine-grained structural and semantic heterogeneity. Moreover, they often model ncRNAs and drugs as interchangeable nodes, disregarding their biological distinctions and asymmetric interactions, and failing to effectively integrate modality-specific and cross-modal features. To overcome these limitations, we propose HDBI, a higher-order dynamic disentangled framework for predicting ncRNA-drug resistance associations. HDBI integrates multiview hypergraph learning, disentangled representation modeling, and bidirectional cross-modal updating to capture heterogeneous topological and semantic patterns within ncRNA and drug spaces while preserving modality-specific characteristics and enabling cross-modal information exchange. Extensive experiments on two benchmark data sets demonstrate that HDBI consistently outperforms state-of-the-art methods. Case studies on 5-FU and Docetaxel further support the biological relevance of the predictions, with 22/30 and 21/30 top-ranked ncRNAs supported by PubMed evidence, respectively. Functional enrichment and molecular docking analyses further linked these predictions to drug-relevant pathways and structurally plausible regulatory interactions. These findings suggest that HDBI provides an effective and interpretable framework for prioritizing ncRNA-mediated drug resistance associations and guiding downstream mechanistic investigation.
Tiyao Liu, Shudong Wang, Baoming Feng et al.· Journal of Chemical Informat...· 0 citations
