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题名

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma

作者
通讯作者Wang, Ji-gang; Dai, Ling-yun
发表日期
2023-10-01
DOI
发表期刊
ISSN
1671-4083
EISSN
1745-7254
卷号45期号:2页码:391-404
摘要
Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.
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英语
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通讯
资助项目
This work was supported by the National Natural Science Foundation of China (32070748, 81902787); Excellent Scientific and Technological Innovation Training Program of Shenzhen (RCYX20210706092040048); Natural Science Foundation of Top Talent of SZTU (Gran["32070748","81902787"] ; National Natural Science Foundation of China[RCYX20210706092040048] ; Excellent Scientific and Technological Innovation Training Program of Shenzhen[GDRC202125] ; Natural Science Foundation of Top Talent of SZTU[NRF-CRP22-2019-0003]
WOS研究方向
Chemistry ; Pharmacology & Pharmacy
WOS类目
Chemistry, Multidisciplinary ; Pharmacology & Pharmacy
WOS记录号
WOS:001081779500003
出版者
ESI学科分类
PHARMACOLOGY & TOXICOLOGY
Scopus记录号
2-s2.0-85173726461
来源库
Web of Science
引用统计
被引频次[WOS]:3
成果类型期刊论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/582952
专题南方科技大学第一附属医院
南方科技大学医学院
作者单位
1.Jinan Univ, Dept Geriatr, Shenzhen Peoples Hosp, Clin Med Coll 2, Shenzhen 518020, Peoples R China
2.Jinan Univ, Shenzhen Peoples Hosp, Clin Med Coll 2, Shenzhen Clin Res Ctr Geriatr, Shenzhen 518020, Peoples R China
3.Southern Univ Sci & Technol, Affiliated Hosp 1, Shenzhen 518020, Peoples R China
4.Jinan Univ, Integrated Chinese & Western Med Postdoctoral Res, Guangzhou 510632, Peoples R China
5.Shenzhen Technol Univ, Coll Pharm, Shenzhen 518118, Peoples R China
6.ASTAR, Inst Mol & Cell Biol, Singapore, Singapore
7.Southern Univ Sci & Technol, Affiliated Hosp 1, Dept Clin Med Res Ctr, Sch Med, Shenzhen 518020, Peoples R China
8.Karolinska Inst, Dept Oncol & Pathol, S-17177 Stockholm, Sweden
9.China Acad Chinese Med Sci, Artemisinin Res Ctr, Beijing 100700, Peoples R China
10.China Acad Chinese Med Sci, Inst Chinese Mat Med, Beijing 100700, Peoples R China
第一作者单位南方科技大学第一附属医院
通讯作者单位南方科技大学第一附属医院
推荐引用方式
GB/T 7714
Yang, Fan,Jia, Lin,Zhou, Hong-chao,et al. Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma[J]. ACTA PHARMACOLOGICA SINICA,2023,45(2):391-404.
APA
Yang, Fan.,Jia, Lin.,Zhou, Hong-chao.,Huang, Jing-nan.,Hou, Meng-yun.,...&Dai, Ling-yun.(2023).Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma.ACTA PHARMACOLOGICA SINICA,45(2),391-404.
MLA
Yang, Fan,et al."Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma".ACTA PHARMACOLOGICA SINICA 45.2(2023):391-404.
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