Yuyao Yuan1, Zitong Zhao2, Liyan Xue3, Guangxi Wang1, Huajie Song1, Ruifang Pang1,4, Juntuo Zhou1, Jianyuan Luo5, Yongmei Song6, Yuxin Yin7,8. 1. Department of Pathology, School of Basic Medical Sciences, Institute of Systems Biomedicine, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China. 2. State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 3. Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 4. Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen, China. 5. Department of Medical Genetics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China. 6. State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. songym@cicams.ac.cn. 7. Department of Pathology, School of Basic Medical Sciences, Institute of Systems Biomedicine, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing, China. yinyuxin@bjmu.edu.cn. 8. Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen, China. yinyuxin@bjmu.edu.cn.
Abstract
BACKGROUND: Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients. METHODS: A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation. RESULTS: Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism. CONCLUSIONS: We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.
BACKGROUND: Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients. METHODS: A total of 525 serum samples were subjected to lipidomic analysis. We combined serum lipidomics and machine-learning algorithms to select important metabolite features for the detection of oesophageal squamous cell carcinoma (ESCC), the major subtype of EC in developing countries. A diagnostic model using a panel of selected features was developed and evaluated. Integrative analyses of tissue transcriptome and serum lipidome were conducted to reveal the underlying mechanism of lipid dysregulation. RESULTS: Our optimised diagnostic model with a panel of 12 lipid biomarkers together with age and gender reaches a sensitivity of 90.7%, 91.3% and 90.7% and an area under receiver-operating characteristic curve of 0.958, 0.966 and 0.818 in detecting ESCC for the training cohort, validation cohort and independent validation cohort, respectively. Integrative analysis revealed matched variation trend of genes encoding key enzymes in lipid metabolism. CONCLUSIONS: We have identified a panel of 12 lipid biomarkers for diagnostic modelling and potential mechanisms of lipid dysregulation in the serum of ESCC patients. This is a reliable, rapid and non-invasive tumour-diagnostic approach for clinical application.
Authors: Jason D Merker; Geoffrey R Oxnard; Carolyn Compton; Maximilian Diehn; Patricia Hurley; Alexander J Lazar; Neal Lindeman; Christina M Lockwood; Alex J Rai; Richard L Schilsky; Apostolia M Tsimberidou; Patricia Vasalos; Brooke L Billman; Thomas K Oliver; Suanna S Bruinooge; Daniel F Hayes; Nicholas C Turner Journal: J Clin Oncol Date: 2018-03-05 Impact factor: 44.544
Authors: Julia Mayerle; Holger Kalthoff; Regina Reszka; Beate Kamlage; Erik Peter; Bodo Schniewind; Sandra González Maldonado; Christian Pilarsky; Claus-Dieter Heidecke; Philipp Schatz; Marius Distler; Jonas A Scheiber; Ujjwal M Mahajan; F Ulrich Weiss; Robert Grützmann; Markus M Lerch Journal: Gut Date: 2017-01-20 Impact factor: 23.059