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Yuanyuan Zhang

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Review Aug 2026

Molecular targeting of CDK9 in oncology therapeutics: avenues for translational impact.

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. · 0 citations
Open access Jul 2026

PM-DUnet: Fusing long-range dependencies and attention in a dual-U architecture for thyroid nodule segmentation

For medical image segmentation, accurately balancing local details and global long-range dependencies is critical to tackling thyroid nodule challenges (variable sizes, ambiguous boundaries, complex context). Traditional CNNs excel at local feature extraction but are constrained by local receptive fields, hindering efficient global dependency modeling. To address this, we propose a Parallel Mamba Dual-U Network (PM-DUNet). It adopts a cascaded dual U-Net encoder-decoder for two-stage “coarse-to-fine” segmentation refinement. We design a Multi-Path Parallel Mamba (MPM) module—using State Space Models (SSMs)—to efficiently model global context with linear complexity. Additionally, Squeeze-Excitation Downsampling (SED) and Spatial Attention Upsampling (SAU) modules are integrated to adaptively enhance key features in encoding/decoding. Results show PM-DUNet achieves highly competitive performance and outperforms state-of-the-art methods on most core metrics, verifying its effectiveness and robustness for complex medical image segmentation. Our code is available on https://github.com/Andrevict/MPDUNet.

Shaoqiang Wang, Linhao Zhang, Guiling Shi et al. · 0 citations