Plasma metabolomics profiling of EGFR-mutant NSCLC patients treated with third-generation EGFR-TKI.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Ning Lou, Ruyun Gao, Yuankai Shi, Xiaohong Han
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引用次数: 0

Abstract

Third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the latest and a vital treatment option for non-small cell lung cancer (NSCLC) patients. Although EGFR-sensitive mutations are an indication for third-generation EGFR-TKI therapy, 30% of NSCLC patients lack response and all patients inevitably progress. There is a lack of biomarkers to predict the efficacy of EGFR-TKI therapy. In this report, we performed comprehensive plasma metabolomic profiling on 186 baseline and 20 post-treatment samples, analyzing 1,019 metabolites using four ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. The dataset contains detailed clinical and metabolic information for 186 patients. Rigorous quality control measures were implemented. No significant differences in body mass index and biochemical metabolic parameters were observed between responders and non-responders. The datasets were utilized to characterize the responsive metabolic traits of third-generation EGFR-TKI therapy. All datasets are available for download on the OMIX website. We anticipate that these datasets will serve as valuable resources for future studies investigating NSCLC metabolism and for the development of personalized therapeutic strategies.

第三代EGFR-TKI治疗egfr突变NSCLC患者的血浆代谢组学分析
第三代表皮生长因子受体酪氨酸激酶抑制剂(EGFR-TKIs)是非小细胞肺癌(NSCLC)患者最新和重要的治疗选择。虽然egfr敏感突变是第三代EGFR-TKI治疗的指征,但30%的NSCLC患者缺乏反应,所有患者不可避免地进展。目前缺乏生物标志物来预测EGFR-TKI治疗的疗效。在本报告中,我们对186份基线样品和20份处理后样品进行了全面的血浆代谢组学分析,使用四种超高高效液相色谱-串联质谱(UPLC-MS/MS)方法分析了1,019种代谢物。该数据集包含186名患者的详细临床和代谢信息。实施严格的质量控制措施。反应者和无反应者的体重指数和生化代谢参数无显著差异。这些数据集被用来表征第三代EGFR-TKI治疗的反应性代谢特征。所有数据集都可以在OMIX网站上下载。我们预计这些数据集将为未来研究非小细胞肺癌代谢和开发个性化治疗策略提供宝贵的资源。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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