The potential of glutamine metabolism-related long non-coding RNAs (lncRNAs) as prognostic biomarkers in multiple myeloma patients

BACKGROUND: Glutamine (Gln) metabolism has been confirmed as an important fuel in cancer metabolism. This study aimed to uncover potential links of Gln with long non-coding RNAs (lncRNAs) and the prognostic value of Gln-associated lncRNAs in multiple myeloma (MM) patients. METHODS: The RNA-seq expression profile and corresponding clinical data of gastric cancer obtained from Gene Expression Omnibus (GEO) database. Unsupervised consensus clustering was used to cluster MM samples based on Gln-associated lncRNAs. The overall survival (OS), biological pathways, and immune microenvironment were compared in different subtypes. Differential analysis was utilized to identify differentially expressed lncRNAs (DElncRNAs) in different subtypes. A risk model was constructed based on DElncRNAs by using Cox regression, least absolute shrinkage and selection operator (LASSO), and the stepAIC algorithm. RESULTS: We screened 50 Gln-associated lncRNAs and identified 3 molecular subtypes (clust1, clust2, and clust3) based on lncRNA expression profiles. Clust3 subtype showed the worst prognosis and highest enrichment of Gln metabolism pathway. Angiogenesis, epithelial-mesenchymal transition (EMT), and cell cycle-related pathways were relatively activated in clust3. Then, we identified 11 prognostic DElncRNAs for constructing the risk model. The MM samples were divided into high- and low-risk groups with distinct prognosis according to the risk score. The risk score was significantly associated with cell cycle and infiltration of many immune cells. CONCLUSIONS: This study characterized the role of Gln-associated lncRNAs in Gln metabolism contributing for tumor-related pathways and immune microenvironment in MM patients. The 11 lncRNAs in the risk model may serve as potential targets for exploring the mechanism of Gln metabolism or serve as potential biomarkers for MM prognosis.