| Accession |
PRJCA017614 |
| Title |
Metabolic Signature Subtypes in Gastric Cancer |
| Relevance |
Medical |
| Data types |
Transcriptome or Gene expression
|
| Organisms |
Homo sapiens
|
| Description |
Recent studies highlighted the clinicopathologic importance of tumor metabolic reprogramming in delineating molecular attributes and therapeutic potentials. However, the overall metabolic features in gastric cancer (GC) have not been comprehensively recognized. Here, consensus NMF clustering algorithm is employed to determine the tumor metabolism patterns in GC. This comprehensive metabolic signature-based clustering analysis identifies three distinct cluster (termed as MSC1, MSC2, MSC3) with substantial differences in metabolic pathways and oncology signaling. The constructed metabolic subtype-related prognosis genes (MSPG) scoring model divides GC patients into high- and low-score subgroups, where a low score is associated with glycosaminoglycan biosynthesis, and worse prognosis. The SDPH in-house dataset with paired transcriptomic and metabolomic also validated these findings. |
| Sample scope |
Multiisolate |
| Release date |
2024-06-29 |
| Publication |
| PubMed ID |
Article title |
Journal name |
DOI |
Year |
| 38959111
|
Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer
|
Cell Reports
|
10.1016/j.celrep.2024.114424
|
2024
|
|
|
| Grants |
| Agency |
program |
Grant ID |
Grant title |
| National Natural Science Foundation of China (NSFC)
|
|
82102702
|
|
| National Natural Science Foundation of China (NSFC)
|
|
82103322
|
|
|
|
| Submitter |
Wei
Chog (chongwei@sdfmu.edu.cn)
|
| Organization |
Shandong Provinical Hospital |
| Submission date |
2023-06-10 |
