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211 results on '"Dang, Qin"'

11. Transcription factor EB reprograms branched‐chain amino acid metabolism and promotes pancreatic cancer progression via transcriptional regulation of BCAT1.

Author

Wang, Ting, Hu, Qiangsheng, Li, Borui, Fan, Guixiong, Jing, Desheng, Xu, Junfeng, Hu, Yuheng, Dang, Qin, Ji, Shunrong, Zhou, Chenjie, Zhuo, Qifeng, Xu, Xiaowu, Qin, Yi, Yu, Xianjun, and Li, Zheng

Subjects
TRANSCRIPTION factors, METABOLIC reprogramming, PANCREATIC cancer, ENZYME metabolism, GENETIC transcription regulation
Abstract

Pancreatic cancer cells have a much higher metabolic demand than that of normal cells. However, the abundant interstitium and lack of blood supply determine the lack of nutrients in the tumour microenvironment. Although pancreatic cancer has been reported to supply extra metabolic demand for proliferation through autophagy and other means, the specific regulatory mechanisms have not yet been elucidated. In this study, we focused on transcription factor EB (TFEB), a key factor in the regulation of autophagy, to explore its effect on the phenotype and role in the unique amino acid utilisation pattern of pancreatic cancer cells (PCCs). The results showed that TFEB, which is generally highly expressed in pancreatic cancer, promoted the proliferation and metastasis of PCCs. TFEB knockdown inhibited the proliferation and metastasis of PCCs by blocking the catabolism of branched‐chain amino acids (BCAAs). Concerning the mechanism, we found that TFEB regulates the catabolism of BCAAs by regulating BCAT1, a key enzyme in BCAA metabolism. BCAA deprivation alone did not effectively inhibit PCC proliferation. However, BCAA deprivation combined with eltrombopag, a drug targeting TFEB, can play a two‐pronged role in exogenous supply deprivation and endogenous utilisation blockade to inhibit the proliferation of pancreatic cancer to the greatest extent, providing a new therapeutic direction, such as targeted metabolic reprogramming of pancreatic cancer. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.

Author

Li, Shengfa, Cao, Peihua, Li, Jia, Chen, Tianyu, Luo, Ping, Ruan, Guangfeng, Zhang, Yan, Wang, Xiaoshuai, Han, Weiyu, Zhu, Zhaohua, Dang, Qin, Wang, Qianyi, Zhang, Mengdi, Bai, Qiushun, Chai, Zhiyi, Yang, Hao, Chen, Haowei, Tang, Mingze, Akbar, Arafat, and Tack, Alexander

Subjects
KNEE osteoarthritis, PREDICTIVE tests, ARTICULAR cartilage, STRUCTURAL models, RECEIVER operating characteristic curves, PREDICTION models, MENISCUS (Anatomy), RESEARCH funding, RADIOMICS, SCIENTIFIC observation, MAGNETIC resonance imaging, DESCRIPTIVE statistics, KNEE joint, LONGITUDINAL method, ARTIFICIAL neural networks, COMPUTER-aided diagnosis, RESEARCH, CONFIDENCE intervals, SENSITIVITY & specificity (Statistics), DISEASE incidence
Abstract

Objective: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)‐based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features. Methods: In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three‐dimensional double‐echo steady‐state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS‐RM aid. Results: Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS‐RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876–0.963), a sensitivity of 84.4% (95% CI 83.9%–84.9%), and a specificity of 85.6% (95% CI 85.2%–86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333–0.614) and 0.586 (95% CI 0.429–0.743) without the assistance of JS‐RM to 0.874 (95% CI 0.847–0.901) and 0.812 (95% CI 0.742–0.881) with JS‐RM assistance, respectively (P < 0.001). Conclusion: JS‐RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges.

Author

Dang, Qin, Zuo, Lulu, Hu, Xinru, Zhou, Zhaokai, Chen, Shuang, Liu, Shutong, Ba, Yuhao, Zuo, Anning, Xu, Hui, Weng, Siyuan, Zhang, Yuyuan, Luo, Peng, Cheng, Quan, Liu, Zaoqu, and Han, Xinwei

Subjects
*COLORECTAL cancer, *GUT microbiome, *CLINICAL medicine, *TUMOR classification, *CELL lines
Abstract

Background: Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker‐drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter‐ or intra‐tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians. Review: In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations. Conclusion: In recent years, exploration of subtypes through cell lines, animal models, patient‐derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype‐specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR‐associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Artificial intelligence‐driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi‐center integration analysis

Author

Xu, Hui, primary, Liu, Zaoqu, additional, Weng, Siyuan, additional, Dang, Qin, additional, Ge, Xiaoyong, additional, Zhang, Yuyuan, additional, Ren, Yuqing, additional, Xing, Zhe, additional, Chen, Shuang, additional, Zhou, Yifang, additional, Ren, Jianzhuang, additional, and Han, Xinwei, additional

Published
2022
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28. Machine learning algorithm-generated and multi-center validated melanoma prognostic signature with inspiration for treatment management

Author

Liu, Zaoqu, primary, Xu, Hui, additional, Weng, Siyuan, additional, Guo, Chunguang, additional, Dang, Qin, additional, Zhang, Yuyuan, additional, Ren, Yuqing, additional, Liu, Long, additional, Wang, Libo, additional, Ge, Xiaoyong, additional, Xing, Zhe, additional, Zhang, Jian, additional, Luo, Peng, additional, and Han, Xinwei, additional

Published
2022
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33. Nobiletin Inhibits Hypoxia-Induced Placental Damage via Modulating P53 Signaling Pathway

Author

Zhang, Meng-Ling, primary, Yang, Qian, additional, Zhu, Yan-Di, additional, Zhang, Ya-Di, additional, Zhang, Rui, additional, Liu, Jian, additional, Zhao, Xiao-Yan, additional, Dang, Qin-Yu, additional, Huang, Dong-Xu, additional, Zhang, Ming-Yuan, additional, Wei, Yu-Chen, additional, Hu, Zhuo, additional, Cai, Xia-Xia, additional, Gao, Li-Fang, additional, Shan, Yang, additional, and Yu, Huan-Ling, additional

Published
2022
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42. Immune Landscape Refines the Classification of Colorectal Cancer With Heterogeneous Prognosis, Tumor Microenvironment and Distinct Sensitivity to Frontline Therapies

Author

Liu, Zaoqu, primary, Guo, Yaxin, additional, Yang, Xiuxiu, additional, Chen, Chen, additional, Fan, Dandan, additional, Wu, Xiaoke, additional, Si, Chaohua, additional, Xu, Yanxin, additional, shao, Bo, additional, Chen, Zhuang, additional, Dang, Qin, additional, Cui, Wenming, additional, Han, Xinwei, additional, Ji, Zhenyu, additional, and Sun, Zhenqiang, additional

Published
2022
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48. Computational Recognition and Clinical Verification of TGF-β-Derived miRNA Signature With Potential Implications in Prognosis and Immunotherapy of Intrahepatic Cholangiocarcinoma

Author

Liu, Zaoqu, primary, Weng, Siyuan, additional, Xu, Hui, additional, Wang, Libo, additional, Liu, Long, additional, Zhang, Yuyuan, additional, Guo, ChunGuang, additional, Dang, Qin, additional, Xing, Zhe, additional, Lu, Taoyuan, additional, and Han, Xinwei, additional

Published
2021
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