AUTHOR=Zeng Jiawei , Tan Honglin , Huang Bin , Zhou Qian , Ke Qi , Dai Yan , Tang Jie , Xu Bei , Feng Jiafu , Yu Lin 

TITLE=Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.959170

DOI=10.3389/fgene.2022.959170

ISSN=1664-8021

ABSTRACT=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 characterization 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 SCIEX triple quad 5500 LC-MS/MS system 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.