Summary of Study ST003025

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, 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.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

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.

Study ID	ST003025
Study Title	NMR- and MS-based omics reveal characteristic metabolome atlas and optimize biofluid earlydiagnostic biomarkers for esophageal squamous cell carcinoma (part-Ⅴ)
Study Summary	Metabolic 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).
Institute	Radiology Department, Second Affiliated Hospital, Shantou University Medical College
Last Name	Lin
First Name	Yan
Address	No. 69, Dongxia North Road, Shantou, Guangdong, China
Email	994809889@qq.com
Phone	+86 18823992148
Submit Date	2023-12-18
Raw Data Available	Yes
Raw Data File Type(s)	cdf
Analysis Type Detail	GC-MS
Release Date	2024-02-08
Release Version	1

Select appropriate tab below to view additional metadata details:

Project:

Project ID:	PR001876
Project DOI:	doi: 10.21228/M87426
Project Title:	NMR- and MS-based omics reveal characteristic metabolome atlas and optimize biofluid earlydiagnostic biomarkers for esophageal squamous cell carcinoma
Project Summary:	Metabolic 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).
Institute:	Radiology Department, Second Affiliated Hospital, Shantou University Medical College
Last Name:	Lin
First Name:	Yan
Address:	No. 69, Dongxia North Road, Shantou, Guangdong, China
Email:	994809889@qq.com
Phone:	+86 18823992148

Subject:

Subject ID:	SU003139
Subject Type:	Human
Subject Species:	Homo sapiens
Taxonomy ID:	9606

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id	local_sample_id	Fator
SA327859	13	Early stage ESCC
SA327860	11	Early stage ESCC
SA327861	14	Early stage ESCC
SA327862	16	Early stage ESCC
SA327863	1	Early stage ESCC
SA327864	10	Early stage ESCC
SA327865	15	Early stage ESCC
SA327866	12	Early stage ESCC
SA327867	4	Early stage ESCC
SA327868	9	Early stage ESCC
SA327869	2	Early stage ESCC
SA327870	5	Early stage ESCC
SA327871	3	Early stage ESCC
SA327872	8	Early stage ESCC
SA327873	7	Early stage ESCC
SA327874	6	Early stage ESCC
SA327875	28	Normal tissue
SA327876	27	Normal tissue
SA327877	29	Normal tissue
SA327878	32	Normal tissue
SA327879	26	Normal tissue
SA327880	31	Normal tissue
SA327881	30	Normal tissue
SA327882	17	Normal tissue
SA327883	20	Normal tissue
SA327884	19	Normal tissue
SA327885	18	Normal tissue
SA327886	21	Normal tissue
SA327887	22	Normal tissue
SA327888	24	Normal tissue
SA327889	23	Normal tissue
SA327890	25	Normal tissue

Showing results 1 to 32 of 32

Showing results 1 to 32 of 32

Collection:

Collection ID:	CO003132
Collection Summary:	Tissue samples, including tumor and normal areas 5 cm away, were obtained under the guidance of experienced pathologists without compromising the patients' pathology examinations. The collected tissue was rinsed with PBS to avoid contamination, excess moisture was removed, and it was rapidly frozen in liquid nitrogen to arrest enzymatic or chemical reactions. Samples were stored at −80°C until metabolite extraction.
Sample Type:	Tissue

Treatment:

Treatment ID:	TR003148
Treatment Summary:	None

Sample Preparation:

Sampleprep ID:	SP003145
Sampleprep Summary:	GC-MS Analysis mainly detects seven short-chain and four medium-chain fatty acids. Qualitative and quantitative analysis was also performed using internal standard method. Metabolic extracts were analyzed using the SHIMADZU GC2030-QP2020 NX gas chromatography-mass spectrometer. The system employed an HP-FFAP capillary column, and a 1 μL aliquot of the analyte was injected in split mode (5:1). Helium was used as the carrier gas with a front inlet purge flow of 3 mL/min and a gas flow rate of 1 mL/min through the column. The initial temperature was maintained at 50 °C for 1 min, then increased to 150 °C at a rate of 50 °C/min for 1 min. Subsequently, it was raised to 170 °C at a rate of 10 °C/min for 1 min, further increased to 210 °C at a rate of 20 °C/min for 1 min, and finally raised to 240 °C at a rate of 40 °C/min for 1 min. The injection, transfer line, quad, and ion source temperatures were set at 220 °C, 240 °C, 150 °C, and 200 °C, respectively. The energy used was -70 eV in electron impact mode. Mass spectrometry data were acquired in Scan/SIM mode within the m/z range of 33-150 after a solvent delay of 3 min. Metabolite identification was performed using an in-house MS database. The pre-processing of MS raw data involved filtering individual metabolites to retain those with no more than 50% missing values. Missing values in the original data were simulated by multiplying the minimum value by a random number between 0.1 and 0.5.

Sampleprep ID:

SP003145

Sampleprep Summary:

GC-MS Analysis mainly detects seven short-chain and four medium-chain fatty acids. Qualitative and quantitative analysis was also performed using internal standard method. Metabolic extracts were analyzed using the SHIMADZU GC2030-QP2020 NX gas chromatography-mass spectrometer. The system employed an HP-FFAP capillary column, and a 1 μL aliquot of the analyte was injected in split mode (5:1). Helium was used as the carrier gas with a front inlet purge flow of 3 mL/min and a gas flow rate of 1 mL/min through the column. The initial temperature was maintained at 50 °C for 1 min, then increased to 150 °C at a rate of 50 °C/min for 1 min. Subsequently, it was raised to 170 °C at a rate of 10 °C/min for 1 min, further increased to 210 °C at a rate of 20 °C/min for 1 min, and finally raised to 240 °C at a rate of 40 °C/min for 1 min. The injection, transfer line, quad, and ion source temperatures were set at 220 °C, 240 °C, 150 °C, and 200 °C, respectively. The energy used was -70 eV in electron impact mode. Mass spectrometry data were acquired in Scan/SIM mode within the m/z range of 33-150 after a solvent delay of 3 min. Metabolite identification was performed using an in-house MS database. The pre-processing of MS raw data involved filtering individual metabolites to retain those with no more than 50% missing values. Missing values in the original data were simulated by multiplying the minimum value by a random number between 0.1 and 0.5.

Combined analysis:

Analysis ID	AN004960
Analysis type	MS
Chromatography type	GC
Chromatography system	SHIMADZU GC2030
Column	Agilent HP5-MS (30m x 0.25mm, 0.25 um)
MS Type	EI
MS instrument type	GC2030-QP2020
MS instrument name	SHIMADZU
Ion Mode	POSITIVE
Units	m/z

Chromatography:

Chromatography ID:	CH003743
Instrument Name:	SHIMADZU GC2030
Column Name:	Agilent HP5-MS (30m x 0.25mm, 0.25 um)
Column Temperature:	475°C
Flow Gradient:	-
Flow Rate:	350-400
Solvent A:	-
Solvent B:	-
Chromatography Type:	GC

MS:

MS ID:	MS004700
Analysis ID:	AN004960
Instrument Name:	SHIMADZU
Instrument Type:	GC2030-QP2020
MS Type:	EI
MS Comments:	The quantitative results are calculated from the following formula: Calculation formula: C（con）= (Cs∗V1∗V3)/(M∗V2) *1000 C (con) : content of the target compound in the sample, μg/g; Cs: target compound concentration in extract, mg/l; V1: Volume of extract solution added, ML; V2: Take Out pure water supernatant volume, ML; V3: Volume of pure water added, ML; M: weighing sample, MG.
Ion Mode:	POSITIVE