A novel sphingolipid metabolism-related long noncoding RNA signature predicts the prognosis, immune landscape and therapeutic response in pancreatic adenocarcinoma

• Published in: Heliyon Vol. 10; no. 1; p. e23659
• Main Authors: He, Xiaolan, Xu, Zhengyang, Ren, Ruiping, Wan, Peng, Zhang, Yu, Wang, Liangliang, Han, Ying
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.01.2024; Elsevier
• Subjects: adenocarcinoma; algorithms; biomarkers; cancer therapy; drugs; gene ontology; genes; immunity; immunotherapy; lncRNA; metabolism; mutation; non-coding RNA; Pancreatic adenocarcinoma; Prognosis; Sphingolipid metabolism; sphingolipids; Tumor biomarker; Tumor immune microenvironment; Pancreatic adenocarcinoma; Tumor biomarker; Tumor immune microenvironment; lncRNA; Prognosis; Sphingolipid metabolism
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2023.e23659

Sphingolipid metabolism affects prognosis and resistance to immunotherapy in patients with cancer and is an emerging target in cancer therapy with promising diagnostic and prognostic value. Long noncoding ribonucleic acids (lncRNAs) broadly regulate tumour-associated metabolic reprogramming. However, the potential of sphingolipid metabolism-related lncRNAs in pancreatic adenocarcinoma (PAAD) is poorly understood. In this study, coexpression algorithms were employed to identify sphingolipid metabolism-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a sphingolipid metabolism-related lncRNA signature (SMLs). The prognostic predictive stability of the SMLs was validated using Kaplan–Meier. Univariate and multivariate Cox, receiver operating characteristic (ROC) and clinical stratification analyses were used to comprehensively assess the SMLs. Gene set variation analysis (GSVE), gene ontology (GO) and tumor mutation burden (TMB) analysis explored the potential mechanisms. Additionally, single sample gene set enrichment analysis (ssGSEA), ESTIMATE, immune checkpoints and drug sensitivity analysis were used to investigate the potential predictive function of the SMLs. Finally, an SMLs-based consensus clustering algorithm was utilized to differentiate patients and determine the suitable population for immunotherapy. The results showed that the SMLs consists of seven sphingolipid metabolism-related lncRNAs, which can well determine the clinical outcome of individuals with PAAD, with high stability and general applicability. In addition, the SMLs-based consensus clustering algorithm divided the TCGA-PAAD cohort into two clusters, with Cluster 1 showing better survival than Cluster 2. Additionally, Cluster 1 had a higher level of immune cell infiltration than Cluster 2, which combined with the higher levels of immune checkpoints in Cluster 1 suggests that Cluster 1 is more consistent with an immune ‘hot tumor’ profile and may respond better to immune checkpoint inhibitors (ICIs). This study offers new insights regarding the potential role of sphingolipid metabolism-related lncRNAs as biomarkers in PAAD. The constructed SMLs and the SMLs-based clustering are valuable tools for predicting clinical outcomes in PAAD and provide a basis for clinical selection of individualized treatments.