ArticleFrontiers in immunology2024
Exploring the heterogeneity of osteosarcoma cell characteristics and metabolic states and their association with clinical prognosis.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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Authors and funding
4 authors.
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Abstract
Background: Osteosarcoma is a malignant tumor originating from mesenchymal bone tissue, characterized by high malignancy and poor prognosis. Despite progress in comprehensive treatment approaches, the five-year survival rate remains largely unchanged, highlighting the need to clarify its underlying mechanisms and discover new therapeutic targets. Methods: This study utilized RNA sequencing data from multiple public databases, encompassing osteosarcoma samples and healthy controls, along with single-cell RNA sequencing data. Various methods were utilized, such as differential expression analysis of genes, analysis of metabolic pathways, and weighted gene co-expression network analysis (WGCNA), to pinpoint crucial genes. Using this list of genes, we developed and validated a prognostic model that incorporated risk signatures, and we evaluated the effectiveness of the model through survival analysis, immune cell infiltration examination, and drug sensitivity evaluation. Results: We analyzed gene expression and metabolic pathways in nine samples using single-cell sequencing data. Initially, we performed quality control and clustering, identifying 21 statistically significant cell subpopulations. Metabolic analyses of these subpopulations revealed heterogeneous activation of metabolic pathways. Focusing on the osteoblastic cell subpopulation, we further subdivided it into six groups and examined their gene expression and differentiation capabilities. Differential expression and enrichment analyses indicated that tumor tissues were enriched in cytoskeletal and structural pathways. Through WGCNA, we identified core genes negatively correlated with four highly activated metabolic pathways. Using osteosarcoma patient data, we developed a risk signature model that demonstrated robust prognostic predictions across three independent cohorts. Ultimately, we performed a thorough examination of the model, which encompassed clinical and pathological characteristics, enrichment analysis, pathways associated with cancer markers, and scores of immune infiltration, highlighting notable and complex disparities between high-risk and low-risk populations. Conclusion: This research clarifies the molecular mechanisms and metabolic features associated with osteosarcoma and how they relate to patient outcomes, offering novel perspectives and approaches for targeted therapy and prognostic assessment in osteosarcoma.
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