Summary of Study ST003014

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001876. The data can be accessed directly via it's Project DOI: 10.21228/M87426 This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST003014
Study TitleNMR- and MS-based omics reveal characteristic metabolome atlas and optimize biofluid earlydiagnostic biomarkers for esophageal squamous cell carcinoma (part-Ⅲ)
Study SummaryMetabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. We conducted NMR- and MS-based metabolomics on 1,153 matched ESCC tissues, normal mucosae, pre- and one-week post-operative sera and urines from 560 participants across three hospitals, with machine learning, logistic regression and WGCNA. Aberrations in 'alanine, aspartate and glutamate metabolism' proved to be prevalent throughout the ESCC evolution, and were reflected in 16 serum and 10 urine metabolic signatures that were consistently identified by NMR and MS in both discovery and validation sets. NMR-based simplified panels of any five serum or urine metabolites outperformed clinical serological tumor markers (AUC = 0.984 and 0.930, respectively), and were effective in distinguishing early-stage ESCC in test set (serum accuracy = 0.994, urine accuracy = 0.879). Collectively, NMR-based biofluid screening can reveal characteristic metabolic events of ESCC and be feasible for early detection (ChiCTR2300073613).
Radiology Department, Second Affiliated Hospital, Shantou University Medical College
Last NameLin
First NameYan
AddressNo. 69, Dongxia North Road, Shantou, Guangdong, China, Shantou, Guangdong, China, 515041, China
Phone+86 18823992148
Submit Date2023-12-15
Raw Data AvailableYes
Raw Data File Type(s)fid
Analysis Type DetailNMR
Release Date2024-02-08
Release Version1
Yan Lin Yan Lin application/zip

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Analysis ID:AN004947
Analysis Type:NMR
Results File:ST003014_AN004947_Results.txt