PXD034233

PXD034233 is an original dataset announced via ProteomeXchange.

Title	Significant alterations of sphingolipid metabolism in GC discovered by lipidome pseudotargeted metabolomics
Description	Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting outcomes and therapeutic efficacy. However, no studies have reported a systematic analysis of lipid metabolism characterizations in gastric cancer (GC). Experimental design: In this study, we comprehensively estimated the lipid metabolism characteristics of 367 GC patients and systematically correlated lipid metabolism with genomic characteristics, clinicopathologic features, and clinical outcomes of GC using two proposed computational algorithms (TGGA-STAD and GSE84437). Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore as a biomarker for GC. Finally, a total of 90 serum as well as 30 tumor tissues and adjacent tissues were for pseudotargeted metabolomics analysis via UHPLC-Q-TOF/MS to determine the lipid metabolism characteristics of the GC patients. Results: A total of 3104 DEGs were determined, among which five lipid metabolism signature genes were identified. The high LMscore subgroup had a positive correlation with the progression of GC, which indicated that the LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic agents. Additionally, significant alterations of sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue by pseudotargeted metabolomics. Conclusions: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the lipid metabolism characteristics of GC may, therefore, help to provide insights into the pathogenesis of GC and interpret the responses of gastric tumors to therapies, which might be helpful to achieve better outcomes in the treatment of GC.
HostingRepository	iProX
AnnounceDate	2022-06-01
AnnouncementXML	Submission_2022-05-31_20:21:47.909.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Lin Yu
SpeciesList	scientific name: Homo sapiens; NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	TripleTOF 5600

Revision	Datetime	Status	ChangeLog Entry
0	2022-05-31 20:21:32	ID requested
⏵ 1	2022-05-31 20:21:48	announced

Dataset with its publication pending

submitter keyword: gastric cancer, lipid metabolism score, therapeutic response, sphingolipid metabolism, pseudotargeted metabolomics

Jiawei Zeng
contact affiliation	Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China
contact email	jiaweizeng@myzxyy.com
lab head
Lin Yu
contact affiliation	Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China
contact email	xiaoyulin918@163.com
dataset submitter

iProX dataset URI