ArticleFrontiers in endocrinology2022
Sphingolipid metabolism is associated with osteosarcoma metastasis and prognosis: Evidence from interaction analysis.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Decoding the Sphingolipid Landscape of Clear Cell Renal Cell Carcinoma: A Single-Cell-Guided Prognostic Model Built With 101 Machine Learning.Human mutation · 2026Article
- Harnessing multi‑omics to revolutionize understanding and management of osteosarcoma: A pathway to precision medicine (Review).International journal of molecular medicine · 2025Review
- Regulation of sphingolipid metabolism in the immune microenvironment of gastric cancer: current insights and future directions.Frontiers in oncology · 2025Review
- Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer.BMC cancer · 2024Article
- A Model to Predict Prognosis of Renal Cell Clear Cell Carcinoma Based on 3 Angiogenesis-related Long Non-coding RNAs.Journal of Cancer · 2024Article
- Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy.Journal of Cancer · 2024Article
- Targeted Regulation of Osteoblasts and Osteoclasts in Osteosarcoma Patients by CSF3R Receptor Inhibition of Osteolysis Caused by Tumor Inflammation Based on Transcriptional Spectrum Analysis and Drug Library Screening.Recent patents on anti-cancer drug discovery · 2024Article
- Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.Frontiers in endocrinology · 2022Article
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Authors and funding
4 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Metabolism is widely involved in the occurrence and development of cancer. However, its role in osteosarcoma (OS) has not been elucidated. Methods: The open-accessed data included in this study were downloaded from The Cancer Genome Atlas (TCGA) database (TARGET-OS project). All the analysis was performed in R environments. Results: Based on the single sample gene set enrichment analysis algorithm, we quantified 21 metabolism terms in OS patients. Among these, sphingolipid metabolism was upregulated in the metastatic OS tissue and associated with a worse prognosis, therefore aroused our interest and selected for further analysis. Our result showed that sphingolipid metabolism could activate the Notch signaling and angiogenesis pathway, which might be responsible for the metastasis ability and poor prognosis. A protein-protein interaction network was constructed to illustrate the interaction of the differentially expressed genes between high and low sphingolipid metabolism. Immune analysis showed that multiple immune terms were upregulated in patients with high sphingolipid metabolism activity. Then, a prognosis model was established based on the identified DEGs between patients with high and low sphingolipid metabolism, which showed great prediction efficiency. Pathway enrichment showed the pathway of myogenesis, spermatogenesis, peroxisome, KRAS signaling, pancreas beta cells, apical surface, MYC target, WNT beta-catenin signaling, late estrogen response and apical junction was significantly enriched in high risk patients. Moreover, we found that the model genes MAGEB1, NPIPA2, PLA2G4B and MAGEA3 could effectively indicate sphingolipid metabolism and risk group. Conclusions: In summary, our result showed that sphingolipid metabolism is associated with osteosarcoma metastasis and prognosis, which has the potential to be a therapeutic target for OS.
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