| Accession |
PRJCA016944 |
| Title |
Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicenter study |
| Relevance |
Medical |
| Data types |
Metabolic Fingerprints
|
| Organisms |
Homo sapiens
|
| Description |
We conducted a large-scale, multicenter study comprising 1944 participants from 7 centers in retrospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionization-mass spectrometry (NPELDI-MS). |
| Sample scope |
Multiisolate |
| Release date |
2023-06-22 |
| Grants |
| Agency |
program |
Grant ID |
Grant title |
| Ministry of Science and Technology of the People's Republic of China (MOST)
|
National Key Technologies R&D Program
|
2021YFA0910100
|
|
|
|
| Submitter |
Xiangdong
Cheng (chengxd@zjcc.org.cn)
|
| Organization |
Zhejiang Cancer Hospital |
| Submission date |
2023-05-12 |
