ZLZichuan Liu

Zichuan Liu

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article

Previous Organizations / Institutes

The organizations / institutes from the scholar's other articles in the past ten years


Zichuan Liu has a total of 35 co-authors. Here are the top co-authors by number of shared publications:

  • Budong Chen

    Department of Neurosurgery, Tianjin Huanhu Hospital

    1 shared publication
  • Ce Yang

    Tianjin University and Health‐Biotech United Group Joint Laboratory of Innovative Drug Development and Translational Medicine, School of Pharmaceutical Science and Technology, Faculty of Medicine Tianjin University Tianjin China

    1 shared publication
  • Chao Jia

    Department of Pathology Beijing Children's Hospital Capital Medical University National Center for Children's Health Beijing China

    1 shared publication
  • Chenyu Wei

    Central South University, Changsha, China

    1 shared publication
  • Chong Liu

    Tianjin University and Health‐Biotech United Group Joint Laboratory of Innovative Drug Development and Translational Medicine, School of Pharmaceutical Science and Technology, Faculty of Medicine Tianjin University Tianjin China

    1 shared publication
  • Dongpeng Zheng

    School of Pharmaceutical Science and Technology, Faculty of Medicine, Tianjin University

    1 shared publication

Latest Publications

  • Open Access

    Chromatin Modifications Orchestrate DNA Repair Pathway Choice: Epigenetics Strategies for Enhancing CRISPR –Cas9‐Mediated Knock‐In Efficiency

    by Xin Chen, Shan Qin, Ce Yang, Zhiyao Xing, Chong Liu, Xingchen Zhang, Xi Wu, Xueren Li, Yanhua Gong, Zichuan Liu

    CRISPR–Cas9‐mediated precise gene knock‐in relies on homology‐directed repair (HDR) at low frequencies in most mammalian cells, limiting precise gene editing in both research and therapeutic applications. Although multiple strategies have been developed to increase knock‐in efficiency, their application can be restricted by technical complexity, limited applicability, or potential safety concerns. In this review, we propose a shift in focus from conventional strategies toward epigenetic methods to increase knock‐in efficiency. It has been proved that histone modifications such as H3K36me3, H3K9me3, H4K20me2, and H2AK15ub are involved in DNA damage signallings and the recruitment of key HDR and non‐homologous end joining (NHEJ) factors. We further discuss conceptual strategies for transient chromatin modulation to bias repair pathway choice and highlight associated technical challenges and safety considerations. Together, these findings and insights highlight the essential role of histone modification in shaping chromatin states and orchestrating DNA repair dynamics. Accordingly, epigenetic regulation emerges as a compelling strategy to facilitate high‐fidelity gene knock‐in, offering valuable mechanistic clues and actionable epigenetic targets to gene editing for basic research and therapeutic translation.

  • Mixed Chimerism in Recurrent Nasal Polyps After Pediatric Allogeneic Hematopoietic Stem Cell Transplantation

    by Jinhao Zhao, Wenjing Liu, Jing Wei, Chao Jia, Pengpeng Wang, Zichuan Liu, Wentong Ge, Yang Han

  • Open Access

    TFPI2 promotes NK cell–mediated glioblastoma killing through adhesion and checkpoint control

    by Dongpeng Zheng, Fengqi Li, Zhuang Zhang, Yunyi Dong, Xuebin Zhang, Xueren Li, Jingyue Yang, Shouchun Peng, Budong Chen, Shupeng Sun, et al.

    Immune-mediated killing triggers dynamic transcriptional adaptations in tumor cells that can reciprocally regulate the cytolytic process. Unraveling such feedback mechanisms is crucial for advancing cancer immunotherapy. Here, we identified tissue factor pathway inhibitor 2 (TFPI2) as a central node in natural killer (NK)–glioblastoma cross talk. Using transcriptomic and functional approaches, we demonstrated that NK cell attack induces TFPI2 expression in glioblastoma cells via IL1β- and TNFα-driven activation of NFκB signaling. TFPI2 not only restrains tumor proliferation by suppressing the POU2F2–CCND1 axis but also enhances NK cytotoxicity through two complementary mechanisms: It supports optimal ICAM1 expression to promote NK–tumor adhesion, and it selectively represses the immune checkpoint molecule SIGLEC15, restoring NK cell effector function. In vivo, loss of TFPI2 accelerates glioblastoma progression and abrogates the efficacy of adoptive NK cell therapy in a context-dependent manner; the functionality is likely restricted to tumors retaining the capacity for TFPI2 induction upon inflammatory stimuli. Our findings identified the TFPI2–ICAM1 and TFPI2–SIGLEC15 axes as conditional regulators of immune–tumor adhesion and checkpoint control, supporting TFPI2 as a candidate therapeutic target for a subset of glioblastomas amenable to inflammatory reprogramming.

  • Open Access

    Learning Markov Clustering Networks for Scene Text Detection

    by Zichuan Liu, Guosheng Lin, Sheng Yang, Jiashi Feng, Weisi Lin, Wang Ling Goh

    A novel framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. MCN predicts instance-level bounding boxes by firstly converting an image into a Stochastic Flow Graph (SFG) and then performing Markov Clustering on this graph. Our method can detect text objects with arbitrary size and orientation without prior knowledge of object size. The stochastic flow graph encode objects' local correlation and semantic information. An object is modeled as strongly connected nodes, which allows flexible bottom-up detection for scale-varying and rotated objects. MCN generates bounding boxes without using Non-Maximum Suppression, and it can be fully parallelized on GPUs. The evaluation on public benchmarks shows that our method outperforms the existing methods by a large margin in detecting multioriented text objects. MCN achieves new state-of-art performance on challenging MSRA-TD500 dataset with precision of 0.88, recall of 0.79 and F-score of 0.83. Also, MCN achieves realtime inference with frame rate of 34 FPS, which is $1.5\times$ speedup when compared with the fastest scene text detection algorithm.