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© 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Dear Editor, Endoscopy with iodine staining was widely used for esophageal cancer (EC) screening in high-incidence area.1,2 Most endoscopy screening-positive population was found to develop esophageal epithelium lesion, and therefore endured higher risk for developing esophageal squamous cell carcinoma (ESCC) than normal population.3,4 However, endoscopic screening may be too costly and invasive for large-scale population, and non-invasive biomarkers may be more applicable and cost effective for population-based screening.5,6 In this population-based screening study, we aim to identify potential metabolic biomarkers for early screening of ESCC, and establish the optimal early ESCC screening model. TABLE 2 Random forest model composed 14 metabolic biomarkers to predict ESCC screening-positive subjects Model N AUC Sensitivity Specificity PPV NPV Risk factors† 0.643 (0.541, 0.734) 0.756 (0.533, 0.933) 0.557 (0.341, 0.761) 0.466 (0.396, 0.575) 0.817 (0.738, 0.931) Metabolites 0.806 (0.728, 0.878) 0.873 (0.745, 0.964) 0.705 (0.590, 0.821) 0.676 (0.602, 0.774) 0.887 (0.803, 0.962) Metabolites and risk factors† 0.828 (0.755, 0.893) 0.782 (0.582, 0.927) 0.782 (0.615, 0.936) 0.719 (0.607, 0.878) 0.838 (0.753, 0.931) Metabolites (by stages) Esophagitis 56 0.711 (0.596, 0.819) 0.800 (0.550, 1.000) 0.671 (0.316, 0.835) 0.365 (0.266, 0.519) 0.927 (0.867, 1.000) Dysplasia 106 0.771 (0.665, 0.863) 0.839 (0.677, 0.968) 0.723 (0.554, 0.831) 0.532 (0.429, 0.651) 0.922 (0.859, 0.981) TIS and Invasive cancer 8 0.939 (0.841, 1.000) 1.000 (1.000, 1.000) 0.902 (0.829, 1.000) 0.200 (0.125, 1.000) 1.000 (1.000, 1.000) Abbreviations: AUC, area under curve; NPV, negative predictive value; PPV, positive predictive value; TIS, tumor in situ. The presence of 56 metabolites would be challenging for ESCC screening. [...]we performed a stepwise logistic regression analysis in the validation dataset to determine the best subset of potential biomarkers among 56 metabolites (Figure 2D). [...]we mapped 22 potential biomarkers based on KEGG database and MetaboAnalyst (Figure 3A).7 Four metabolic pathways were associated with ESCC at the FDR threshold of 0.05 and the pathway impact value threshold of 0.001.

Details

Title
A serum metabolomics analysis reveals a panel of screening metabolic biomarkers for esophageal squamous cell carcinoma
Author
Lv, Jiali 1 ; Wang, Jialin 2 ; Shen, Xiaotao 3 ; Liu, Jia 4 ; Zhao, Deli 5 ; Wei, Mengke 1 ; Li, Xia 1 ; Fan, Bingbing 1 ; Sun, Yawen 2 ; Xue, Fuzhong 1 ; Zheng-Jiang, Zhu 3 ; Zhang, Tao 1   VIAFID ORCID Logo 

 Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China 
 The Shandong Cancer Hospital Affiliated to Shandong University, Jinan, China 
 Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China 
 Yanjing Medical College, Capital Medical University, Beijing, China 
 Tumor Preventative and Therapeutic Base of Shandong Province, Feicheng People's Hospital, Feicheng, China 
Section
LETTER TO EDITOR
Publication year
2021
Publication date
May 2021
Publisher
John Wiley & Sons, Inc.
e-ISSN
20011326
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2760819007
Copyright
© 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.