Summary of Study ST003048

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 PR001898. The data can be accessed directly via it's Project DOI: 10.21228/M8CQ76 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.

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Study IDST003048
Study TitleIdentification and validation of serum metabolite biomarkers for endometrial cancer diagnosis
Study TypeBiomarker, Endometrial cancer, Machine learning, Mass spectrometry, Metabolite
Study SummaryEndometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, P < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.
Institute
Shanghai Jiao Tong University
DepartmentSchool of Biomedical Engineering
Last NameLiu
First NameWanshan
Address1954 Huashan Road, Shanghai, China
Emailliuwanshan@sjtu.edu.cn
Phone+86-13262629289
Submit Date2024-01-20
Analysis Type DetailMALDI
Release Date2024-01-23
Release Version1
Wanshan Liu Wanshan Liu
https://dx.doi.org/10.21228/M8CQ76
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:

Project:

Project ID:PR001898
Project DOI:doi: 10.21228/M8CQ76
Project Title:Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis
Project Type:Biomarker, Endometrial cancer, Machine learning, Mass spectrometry, Metabolite
Project Summary:Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, P < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.
Institute:Shanghai Jiao Tong University
Department:School of Biomedical Engineering
Last Name:Liu
First Name:Wanshan
Address:1954 Huashan Road, Shanghai, China
Email:liuwanshan@sjtu.edu.cn
Phone:+86-13262629289

Subject:

Subject ID:SU003163
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Gender:Female

Factors:

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

mb_sample_id local_sample_id Group
SA330841SMFs-268EC
SA330842SMFs-267EC
SA330843SMFs-265EC
SA330844SMFs-269EC
SA330845SMFs-266EC
SA330846SMFs-271EC
SA330847SMFs-274EC
SA330848SMFs-273EC
SA330849SMFs-272EC
SA330850SMFs-264EC
SA330851SMFs-270EC
SA330852SMFs-262EC
SA330853SMFs-256EC
SA330854SMFs-255EC
SA330855SMFs-254EC
SA330856SMFs-253EC
SA330857SMFs-257EC
SA330858SMFs-258EC
SA330859SMFs-275EC
SA330860SMFs-261EC
SA330861SMFs-260EC
SA330862SMFs-259EC
SA330863SMFs-263EC
SA330864SMFs-277EC
SA330865SMFs-292EC
SA330866SMFs-291EC
SA330867SMFs-290EC
SA330868SMFs-289EC
SA330869SMFs-293EC
SA330870SMFs-294EC
SA330871SMFs-298EC
SA330872SMFs-297EC
SA330873SMFs-296EC
SA330874SMFs-295EC
SA330875SMFs-288EC
SA330876SMFs-287EC
SA330877SMFs-280EC
SA330878SMFs-279EC
SA330879SMFs-278EC
SA330880SMFs-252EC
SA330881SMFs-281EC
SA330882SMFs-282EC
SA330883SMFs-286EC
SA330884SMFs-285EC
SA330885SMFs-284EC
SA330886SMFs-283EC
SA330887SMFs-276EC
SA330888SMFs-250EC
SA330889SMFs-220EC
SA330890SMFs-219EC
SA330891SMFs-218EC
SA330892SMFs-217EC
SA330893SMFs-221EC
SA330894SMFs-222EC
SA330895SMFs-226EC
SA330896SMFs-225EC
SA330897SMFs-224EC
SA330898SMFs-223EC
SA330899SMFs-216EC
SA330900SMFs-215EC
SA330901SMFs-208EC
SA330902SMFs-207EC
SA330903SMFs-206EC
SA330904SMFs-205EC
SA330905SMFs-209EC
SA330906SMFs-210EC
SA330907SMFs-214EC
SA330908SMFs-213EC
SA330909SMFs-212EC
SA330910SMFs-211EC
SA330911SMFs-227EC
SA330912SMFs-228EC
SA330913SMFs-244EC
SA330914SMFs-243EC
SA330915SMFs-242EC
SA330916SMFs-241EC
SA330917SMFs-245EC
SA330918SMFs-246EC
SA330919SMFs-300EC
SA330920SMFs-249EC
SA330921SMFs-248EC
SA330922SMFs-247EC
SA330923SMFs-240EC
SA330924SMFs-239EC
SA330925SMFs-232EC
SA330926SMFs-231EC
SA330927SMFs-230EC
SA330928SMFs-229EC
SA330929SMFs-233EC
SA330930SMFs-234EC
SA330931SMFs-238EC
SA330932SMFs-237EC
SA330933SMFs-236EC
SA330934SMFs-235EC
SA330935SMFs-251EC
SA330936SMFs-299EC
SA330937SMFs-364EC
SA330938SMFs-365EC
SA330939SMFs-363EC
SA330940SMFs-362EC
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Collection:

Collection ID:CO003156
Collection Summary:Blood was collected through venipuncture and then centrifuged at 2000 g for 10 minutes. The serum was transferred to a microtube immediately and stored at -80°C. The samples of the EC and Non-EC groups were collected during a similar period from Dec. 2018 to Sep. 2021, and the serum metabolic fingerprints (SMFs) database was recorded in Jul. 2022 using the serum samples that underwent one freeze-thaw cycle. The pathologists were blinded to any information about SMFs analysis.
Sample Type:Blood (serum)
Storage Conditions:-80℃

Treatment:

Treatment ID:TR003172
Treatment Summary:NA

Sample Preparation:

Sampleprep ID:SP003169
Sampleprep Summary:1.5 μL of serum samples (10-fold dilution) were spotted on a 384 polished steel plate and dried.

Combined analysis:

Analysis ID AN004999
Analysis type MS
Chromatography type None (Direct infusion)
Chromatography system NA
Column NA
MS Type MALDI
MS instrument type TOF
MS instrument name Bruker Autoflex speed TOF/TOF
Ion Mode POSITIVE
Units Peak intensity

Chromatography:

Chromatography ID:CH003777
Instrument Name:NA
Column Name:NA
Column Temperature:NA
Flow Gradient:NA
Flow Rate:NA
Solvent A:NA
Solvent B:NA
Chromatography Type:None (Direct infusion)

MS:

MS ID:MS004739
Analysis ID:AN004999
Instrument Name:Bruker Autoflex speed TOF/TOF
Instrument Type:TOF
MS Type:MALDI
MS Comments:10 mg of organic matrices were dissolved in 1 mL of TA30 solution (acetonitrile:0.1% TFA solution, 3:7, v/v), and the particle powder was dispersed in DIW with 1.0 mg/mL. 1.5 μL of either standard metabolite solutions or serum samples (10-fold dilution) were spotted on a 384 polished steel plate and dried. The serum samples were prepared randomly to minimize subjective bias for the SMFs database construction. Subsequently, matrix solution was added and dried before detection. Mass-to-charge ratio (m/z) calibration was performed using alanine, proline, glutamic acid, glucose, lactose, maltotriose, and thyroxine. The pulse frequency and laser shots per analysis were set to 1000 Hz and 2000, respectively.
Ion Mode:POSITIVE
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