ArticleBMC cancer2024
Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
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- Tumour-Derived sEVs Promote Triple-Negative Breast Cancer Progression Associated with HAVCR2 Upregulation in Macrophages.Oncology research · 2026Article
- Integrated bioinformatic and machine learning analysis identifies MCM7 and ADAM17 as potential biomarkers for early stage gastric cancer.Journal of gastrointestinal oncology · 2025Article
- PTK6 mediated immune signatures revealed by single cell transcriptomic and multi omics big data analysis in cervical cancer.Discover oncology · 2025Article
- Mendelian randomization analysis reveals potential association between allergic rhinitis and nasopharyngeal carcinoma.Discover oncology · 2025Article
- Development and functional validation of a disulfidoptosis-related gene prognostic model for lung adenocarcinoma based on bioinformatics and experimental validation.Frontiers in immunology · 2025Article
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6 authors.
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Abstract
backgroundAbnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.
methodsSingle-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.
resultsA total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.
conclusionThis study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.
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