Development and Validation of a Prognostic Classifier Based on Lipid Metabolism–Related Genes in Gastric Cancer

作者：Xiaoli Wei, Tianqi Luo, Jianing Li, Zhicheng Xue, Yun Wang, You Zhang, Ying-Bo Chen, Chuan Peng · 发表于：Frontiers in Molecular Biosciences · 年份：2021 · DOI：10.3389/fmolb.2021.691143 · 被引用次数：32 · 研究领域：Cancer, Lipids, and Metabolism、Ferroptosis and cancer prognosis、Cancer-related molecular mechanisms research

Background: Dysregulation of lipid metabolism plays important roles in the tumorigenesis and progression of gastric cancer (GC). The present study aimed to establish a prognostic model based on the lipid metabolism–related genes in GC patients. Materials and Methods: Two GC datasets from the Gene Expression Atlas, GSE62254 ( n = 300) and GSE26942 ( n = 217), were used as training and validation cohorts to establish a risk predictive scoring model. The efficacy of this model was assessed by ROC analysis. The association of the risk predictive scores with patient characteristics and immune cell subtypes was evaluated. A nomogram was constructed based on the risk predictive score model and other prognostic factors. Results: A risk predictive score model was established based on the expression of 19 lipid metabolism–related genes (LPL, IPMK, PLCB3, CDIPT, PIK3CA, DPM2, PIGZ, GPD2, GPX3, LTC4S, CYP1A2, GALC, SGMS1, SMPD2, SMPD3, FUT6, ST3GAL1, B4GALNT1, and ACADS). The time-dependent ROC analysis revealed that the risk predictive score model was stable and robust. Patients with high risk scores had significantly unfavorable overall survival compared with those with low risk scores in both the training and validation cohorts. A higher risk score was associated with more aggressive features, including a higher tumor grade, a more advanced TNM stage, and diffuse type of Lauren classification of GC. Moreover, distinct immune cell subtypes and signaling pathways were found between the ...