Gastric cancer (GC) is a highly heterogeneous malignancy, characterized by high mortality and poor prognosis. Ferroptosis is a newly defined nonapoptotic programmed cell death mechanism that has been implicated in the development of various pathological conditions. We aimed to identify ferroptosis-related long noncoding RNA (lncRNAs) that might be used to predict GC prognosis. The data were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database. Two subtypes, C1 and C2, were identified, which had significant variations in prognosis and immune cell infiltrations. Differentially expressed genes between the subtypes were found to be involved in multiple tumor-associated pathways. Subsequently, a training dataset and a testing dataset were created from the TCGA dataset. A predictive model for GC patients based on six ferroptosis-related lncRNAs (including STX18-AS1, MIR99AHG, LINC01197, LINC00968, LINC00865, and LEF1-AS1) was developed. The model could stratify patients into a high- and low-risk group, showing good predictive performance. The testing dataset, entire TCGA dataset, and GSE62254 cohort both confirmed the predictive value of the model. Compared to the clinical parameters (including gender, age, and grade), the risk model was an independent risk factor for GC patients. Moreover, a nomogram (containing our risk score model and clinical parameters) was constructed, which might provide great potential to improve prediction accuracy. Moreover, the single-sample gene set enrichment analysis revealed that the high-risk group was linked to various signaling pathways involved in the regulation of GC progression. Conclusively, a novel classification and risk model based on ferroptosis-related lncRNAs that can predict oncologic outcomes for GC patients has been developed.
Keywords: ferroptosis; gastric cancer; lncRNA; prognosis.
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