A novel PDX modeling strategy and its application in metabolomics study for malignant pleural mesothelioma
Chen, Zhongjian1,2; Yang, Chenxi1,2; Guo, Zhenying1,2; Song, Siyu1,2; Gao, Yun1,2; Wang, Ding1,2; Mao, Weimin1,2; Liu, Junping1,2
刊名BMC CANCER
2021-11-17
卷号21
关键词Patient-derived xenograft Malignant pleural mesothelioma GC-MS Metabolomics Ultrasound-guided biopsy
DOI10.1186/s12885-021-08980-5
通讯作者Mao, Weimin(maowm@zjcc.org.cn) ; Liu, Junping(liujunpingzjcc@163.com)
英文摘要Background Malignant pleural mesothelioma (MPM) is a rare and aggressive carcinoma located in pleural cavity. Due to lack of effective diagnostic biomarkers and therapeutic targets in MPM, the prognosis is extremely poor. Because of difficulties in sample extraction, and the high rate of misdiagnosis, MPM is rarely studied. Therefore, novel modeling methodology is crucially needed to facilitate MPM research. Methods A novel patient-derived xenograft (PDX) modeling strategy was designed, which included preliminary screening of patients with pleural thickening using computerized tomography (CT) scan, further reviewing history of disease and imaging by a senior sonographer as well as histopathological analysis by a senior pathologist, and PDX model construction using ultrasound-guided pleural biopsy from MPM patients. Gas chromatography-mass spectrometry-based metabolomics was further utilized for investigating circulating metabolic features of the PDX models. Univariate and multivariate analysis, and pathway analysis were performed to explore the differential metabolites, enriched metabolism pathways and potential metabolic targets. Results After screening using our strategy, 5 out of 116 patients were confirmed to be MPM, and their specimens were used for modeling. Two PDX models were established successfully. Metabolomics analysis revealed significant metabolic shifts in PDX models, such as dysregulations in amino acid metabolism, TCA cycle and glycolysis, and nucleotide metabolism. Conclusions To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed.
资助项目Key R&D Program Projects in Zhejiang Province[2018C04009] ; National Natural Science Foundation of China[81672315] ; National Natural Science Foundation of China[82072577] ; International Cooperation Project of Zhejiang Basic Public Technology Research Program[LGJ20H010001] ; Projects of Zhejiang Province Medical and Health Science and Technology Plan[2017KY256] ; Clinical Research Fund of Zhejiang Medical Association[2019ZYC-A77]
WOS关键词URIC-ACID ; PERIPHERAL LUNG ; NEEDLE-BIOPSY ; XENOGRAFTS ; GENERATION ; EXPOSURE ; ACCURACY ; CANCERS ; US
WOS研究方向Oncology
语种英语
出版者BMC
WOS记录号WOS:000719925800005
资助机构Key R&D Program Projects in Zhejiang Province ; National Natural Science Foundation of China ; International Cooperation Project of Zhejiang Basic Public Technology Research Program ; Projects of Zhejiang Province Medical and Health Science and Technology Plan ; Clinical Research Fund of Zhejiang Medical Association
内容类型期刊论文
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/126548]  
专题中国科学院合肥物质科学研究院
通讯作者Mao, Weimin; Liu, Junping
作者单位1.Key Lab Diag & Treatment Technol Thorac Oncol, Hangzhou 310022, Zhejiang, Peoples R China
2.Chinese Acad Sci, Canc Res Inst, Canc Hosp, Univ Chinese Acad Sci,Zhejiang Canc Hosp,Inst Can, Hangzhou 310022, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714
Chen, Zhongjian,Yang, Chenxi,Guo, Zhenying,et al. A novel PDX modeling strategy and its application in metabolomics study for malignant pleural mesothelioma[J]. BMC CANCER,2021,21.
APA Chen, Zhongjian.,Yang, Chenxi.,Guo, Zhenying.,Song, Siyu.,Gao, Yun.,...&Liu, Junping.(2021).A novel PDX modeling strategy and its application in metabolomics study for malignant pleural mesothelioma.BMC CANCER,21.
MLA Chen, Zhongjian,et al."A novel PDX modeling strategy and its application in metabolomics study for malignant pleural mesothelioma".BMC CANCER 21(2021).
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