Identification of two-dimensional copper signatures in human blood for bladder cancer with machine learning
文献类型：期刊论文
| 作者 | Wang, Weichao; Liu, Xian ; Zhang, Changwen; Sheng, Fei; Song, Shanjun; Li, Penghui; Dai, Shaoqing; Wang, Bin; Lu, Dawei; Zhang, Luyao
|
| 刊名 | CHEMICAL SCIENCE
 |
| 出版日期 | 2022-02-09 |
| 卷号 | 13期号:6页码:1648-1656 |
| 关键词 | ICP-MASS SPECTROMETRY ISOTOPE FRACTIONATION HEPATOCELLULAR-CARCINOMA MEDICAL APPLICATIONS OXIDATIVE STRESS TRACE-ELEMENTS CELL CARCINOMA SERUM CU ZN |
| ISSN号 | 2041-6520 |
| 英文摘要 | Currently, almost all available cancer biomarkers are based on concentrations of compounds, often suffering from low sensitivity, poor specificity, and false positive or negative results. The stable isotopic composition of elements provides a different dimension from the concentration and has been widely used as a tracer in geochemistry. In health research, stable isotopic analysis has also shown potential as a new diagnostic/prognostic tool, which is still in the nascent stage. Here we discovered that bladder cancer (BCa) could induce a significant variation in the ratio of natural copper isotopes (Cu-65/Cu-63) in the blood of patients relative to benign and healthy controls. Such inherent copper isotopic signatures permitted new insights into molecular mechanisms of copper imbalance underlying the carcinogenic process. More importantly, to enhance the diagnostic capability, a machine learning model was developed to classify BCa and non-BCa subjects based on two-dimensional copper signatures (copper isotopic composition and concentration in plasma and red blood cells) with a high sensitivity, high true negative rate, and low false positive rate. Our results demonstrated the promise of blood copper signatures combined with machine learning as a versatile tool for cancer research and potential clinical application. |
| 源URL | [https://ir.rcees.ac.cn/handle/311016/47550]  |
| 专题 | 生态环境研究中心_环境化学与生态毒理学国家重点实验室 |
| 通讯作者 | Liu, Qian |
| 作者单位 | 1.Tianjin Univ Technol, Tianjin 300384, Peoples R China 2.Univ Twente, Fac Geoinformat Sci & Earth Observat ITC, POB 217, NL-7500 AE Enschede, Netherland 3.Natl Inst Metrol, Beijing 100029, Peoples R China 4.Tianjin Med Univ, Tianjin Inst Urol, Dept Urol, Hosp 2, Tianjin 300211, Peoples R China 5.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Environm Chem & Ecotoxicol, Beijing 100085, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Weichao,Liu, Xian,Zhang, Changwen,et al. Identification of two-dimensional copper signatures in human blood for bladder cancer with machine learning[J]. CHEMICAL SCIENCE,2022,13(6):1648-1656. |
| APA | Wang, Weichao.,Liu, Xian.,Zhang, Changwen.,Sheng, Fei.,Song, Shanjun.,...&Jiang, Guibin.(2022).Identification of two-dimensional copper signatures in human blood for bladder cancer with machine learning.CHEMICAL SCIENCE,13(6),1648-1656. |
| MLA | Wang, Weichao,et al."Identification of two-dimensional copper signatures in human blood for bladder cancer with machine learning".CHEMICAL SCIENCE 13.6(2022):1648-1656. |
入库方式： OAI收割
来源：生态环境研究中心
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