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292 results on '"Liang, Chaozhao"'

15. Prognostic Significance of Grade Discrepancy Between Primary Tumor and Venous Thrombus in Nonmetastatic Clear-cell Renal Cell Carcinoma: Analysis of the REMEMBER Registry and Implications for Adjuvant Therapy

Author

Wu, Zhenjie, Chen, Hui, Chen, Qi, Ge, Silun, Yu, Nengwang, Campi, Riccardo, Gómez Rivas, Juan, Autorino, Riccardo, Rouprêt, Morgan, Psutka, Sarah P., Mehrazin, Reza, Porpiglia, Francesco, Bensalah, Karim, Black, Peter C., Mir, Maria C., Minervini, Andrea, Djaladat, Hooman, Margulis, Vitaly, Bertolo, Riccardo, Caliò, Anna, Carbonara, Umberto, Amparore, Daniele, Borregales, Leonardo D., Ciccarese, Chiara, Diana, Pietro, Erdem, Selcuk, Marandino, Laura, Marchioni, Michele, Muselaers, Constantijn H.J., Palumbo, Carlotta, Pavan, Nicola, Pecoraro, Angela, Roussel, Eduard, Warren, Hannah, Pandolfo, Savio Domenico, Chen, Rui, Zhou, Wenquan, Zhai, Wei, He, Miaoxia, Li, Yaoming, Han, Bo, Wan, Jie, Zeng, Xing, Yan, Junan, Fu, Yao, Ji, Changwei, Fan, Xiang, Zhang, Guangyuan, Zhao, Cheng, Jing, Taile, Wang, Anbang, Feng, Chenchen, Zhao, Hongwei, Sun, Di, Wang, Liang, Tai, Sheng, Zhang, Cheng, Chen, Shaohao, Liu, Yixun, Xu, Zhipeng, Wang, Haifeng, Gao, Jinli, Wang, Fubo, Cheng, Jiwen, Miao, He, Rao, Qiu, Wang, Jianning, Xu, Ning, Wang, Gongxian, Liang, Chaozhao, Liu, Zhiyu, Xia, Dan, Jiang, Jun, Zu, Xiongbing, Chen, Ming, Guo, Hongqian, Qin, Weijun, Wang, Zhe, Xue, Wei, Shi, Benkang, Zhou, Xiaojun, Wang, Shaogang, Zheng, Junhua, Ge, Jingping, Feng, Xiang, Li, Minming, Chen, Cheng, Qu, Le, and Wang, Linhui

Published
2024
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21. Rezvilutamide versus bicalutamide in combination with androgen-deprivation therapy in patients with high-volume, metastatic, hormone-sensitive prostate cancer (CHART): a randomised, open-label, phase 3 trial

Author

Gu, Weijie, Han, Weiqing, Luo, Hong, Zhou, Fangjian, He, Dalin, Ma, Lulin, Guo, Hongqian, Liang, Chaozhao, Chong, Tie, Jiang, Jun, Chen, Zhiwen, Wang, Yong, Zou, Qing, Tian, Ye, Xiao, Jun, Huang, Jian, Zhu, Shaoxing, Dong, Qiang, Zhang, Xiaoping, Li, Hanzhong, Yang, Xinfeng, Chen, Chunxia, Li, Junliang, Jin, Chunlei, Zhang, Xiaojing, and Ye, Dingwei

Published
2022
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35. A genomic and epigenomic atlas of prostate cancer in Asian populations

Author

Li, Jing, Xu, Chuanliang, Lee, Hyung Joo, Ren, Shancheng, Zi, Xiaoyuan, Zhang, Zhiming, Wang, Haifeng, Yu, Yongwei, Yang, Chenghua, Gao, Xiaofeng, Hou, Jianguo, Wang, Linhui, Yang, Bo, Yang, Qing, Ye, Huamao, Zhou, Tie, Lu, Xin, Wang, Yan, Qu, Min, Yang, Qingsong, Zhang, Wenhui, Shah, Nakul M., Pehrsson, Erica C., Wang, Shuo, Wang, Zengjun, Jiang, Jun, Zhu, Yan, Chen, Rui, Chen, Huan, Zhu, Feng, Lian, Bijun, Li, Xiaoyun, Zhang, Yun, Wang, Chao, Wang, Yue, Xiao, Guangan, Jiang, Junfeng, Yang, Yue, Liang, Chaozhao, Hou, Jianquan, Han, Conghui, Chen, Ming, Jiang, Ning, Zhang, Dahong, Wu, Song, Yang, Jinjian, Wang, Tao, Chen, Yongliang, Cai, Jiantong, Yang, Wenzeng, Xu, Jun, Wang, Shaogang, Gao, Xu, Wang, Ting, and Sun, Yinghao

Published
2020
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43. Deciphering the suppressive immune microenvironment of prostate cancer based on CD4+ regulatory T cells: Implications for prognosis and therapy prediction.

Author

Ge, Qintao, Zhao, Zhijie, Li, Xiao, Yang, Feixiang, Zhang, Meng, Hao, Zongyao, Liang, Chaozhao, and Meng, Jialin

Subjects
REGULATORY T cells, PROSTATE cancer, CD4 antigen, TUMOR microenvironment, PROGNOSIS, CELL transformation
Abstract

This article explores the role of regulatory T cells (Tregs) in prostate cancer (PCa) and their impact on prognosis and treatment response. The researchers found that Tregs can be divided into two subtypes: TregR, which is associated with a poor prognosis, immunosuppression, and resistance to therapy, and TregP, which has the opposite characteristics. TregR showed higher expression of TGF-β and mutations in the TP53 and PIK3CA genes, which may contribute to therapy resistance. The study suggests that targeting TGF-β and the PI3K-AKT-mTOR pathway may improve treatment outcomes for TregR PCa. [Extracted from the article]

Published
2024
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45. Low dose of emetine as potential anti-SARS-CoV-2 virus therapy: preclinical in vitro inhibition and in vivo pharmacokinetic evidences

Author

Wang, Aoli, Sun, Yong, Liu, Qingwang, Wu, Hong, Liu, Juan, He, Jun, Yu, Junling, Chen, Qing Qing, Ge, Yinglu, Zhang, Zhuhui, Hu, Chen, Chen, Cheng, Qi, Ziping, Zou, Fengming, Liu, Feiyang, Hu, Jie, Zhao, Ming, Huang, Tao, Wang, Beilei, Wang, Li, Wang, Wei, Wang, Wenchao, Ren, Tao, Liu, Jing, Sun, Yehuan, Fan, Song, Wu, Qibing, Liang, Chaozhao, Sun, Liangdan, Su, Bin, Wei, Wei, and Liu, Qingsong

Published
2020
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48. Molecular classifications of prostate cancer: basis for individualized risk stratification and precision therapy.

Author

Ge, Qintao, Li, Jiawei, Yang, Feixiang, Tian, Xuefeng, Zhang, Meng, Hao, Zongyao, Liang, Chaozhao, and Meng, Jialin

Subjects
PROSTATE cancer, SELF-organizing maps, GLEASON grading system, TUMOR microenvironment, DISEASE exacerbation, CLUSTER analysis (Statistics), TUMOR classification, PROGNOSIS
Abstract

Tumour classifications play a pivotal role in prostate cancer (PCa) management. It can predict the clinical outcomes of PCa as early as the disease is diagnosed and then guide therapeutic schemes, such as active monitoring, standalone surgical intervention, or surgery supplemented with postoperative adjunctive therapy, thereby circumventing disease exacerbation and excessive treatment. Classifications based on clinicopathological features, such as prostate cancer-specific antigen, Gleason score, and TNM stage, are still the main risk stratification strategies and have played an essential role in standardized clinical decision-making. However, mounting evidence indicates that clinicopathological parameters in isolation fail to adequately capture the heterogeneity exhibited among distinct PCa patients, such as those sharing identical Gleason scores yet experiencing divergent prognoses. As a remedy, molecular classifications have been introduced. Currently, molecular studies have revealed the characteristic genomic alterations, epigenetic modulations, and tumour microenvironment associated with different types of PCa, which provide a chance for urologists to refine the PCa classification. In this context, numerous invaluable molecular classifications have been devised, employing disparate statistical methodologies and algorithmic approaches, encompassing self-organizing map clustering, unsupervised cluster analysis, and multifarious algorithms. Interestingly, the classifier PAM50 was used in a phase-2 multicentre open-label trial, NRG-GU-006, for further validation, which hints at the promise of molecular classification for clinical use. Consequently, this review examines the extant molecular classifications, delineates the prevailing panorama of clinically pertinent molecular signatures, and delves into eight emblematic molecular classifications, dissecting their methodological underpinnings and clinical utility. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Multiomics characterization and verification of clear cell renal cell carcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.

Author

Meng, Jialin, Jiang, Aimin, Lu, Xiaofan, Gu, Di, Ge, Qintao, Bai, Suwen, Zhou, Yundong, Zhou, Jun, Hao, Zongyao, Yan, Fangrong, Wang, Linhui, Wang, Haitao, Du, Juan, and Liang, Chaozhao

Subjects
RENAL cell carcinoma, MULTIOMICS, SOMATIC mutation, IMMUNOTHERAPY, SUNITINIB, COHORT analysis
Abstract

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor with different genetic and molecular alterations. Schemes for ccRCC classification system based on multiomics are urgent, to promote further biological insights. Two hundred and fifty‐five ccRCC patients with paired data of clinical information, transcriptome expression profiles, copy number alterations, DNA methylation, and somatic mutations were collected for identification. Bioinformatic analyses were performed based on our team's recently developed R package "MOVICS." With 10 state‐of‐the‐art algorithms, we identified the multiomics subtypes (MoSs) for ccRCC patients. MoS1 is an immune exhausted subtype, presented the poorest prognosis, and might be caused by an exhausted immune microenvironment, activated hypoxia features, but can benefit from PI3K/AKT inhibitors. MoS2 is an immune "cold" subtype, which represented more mutation of VHL and PBRM1, favorable prognosis, and is more suitable for sunitinib therapy. MoS3 is the immune "hot" subtype, and can benefit from the anti‐PD‐1 immunotherapy. We successfully verified the different molecular features of the three MoSs in external cohorts GSE22541, GSE40435, and GSE53573. Patients that received Nivolumab therapy helped us to confirm that MoS3 is suitable for anti‐PD‐1 therapy. E‐MTAB‐3267 cohort also supported the fact that MoS2 patients can respond more to sunitinib treatment. We also confirm that SETD2 is a tumor suppressor in ccRCC, along with the decreased SETD2 protein level in advanced tumor stage, and knock‐down of SETD2 leads to the promotion of cell proliferation, migration, and invasion. In summary, we provide novel insights into ccRCC molecular subtypes based on robust clustering algorithms via multiomics data, and encourage future precise treatment of ccRCC patients. Highlights: Three molecular subtypes of ccRCC based on multiomics data were identified, with diverse overall survival time, and validated in external cohorts.MoS1 is an immune exhausted subtype, which can benefit from PI3K/AKT inhibitors; MoS2 is an immune "cold" subtype, but is more suitable for sunitinib therapy; MoS3 is an immune "hot" subtype, which can benefit from anti‐PD‐1 immunotherapy.SETD2 is a tumor suppressor in ccRCC, and knock‐down of SETD2 leads to the promotion of cell proliferation, migration, and invasion. [ABSTRACT FROM AUTHOR]

Published
2023
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