Conclusion
Our study demonstrated the role of ANRGs in osteosarcoma progression, providing insights into clinical decision making in osteosarcoma.
Methods
The RNA sequencing and clinical data of patients with osteosarcoma were extracted from the TARGET and GEO databases, and ANRGs were identified from the GeneCards database. Unsupervised clustering analysis was employed to identify anoikis-related patterns. The ESTIMATE, TIMER and ssGSEA algorithms were used to assess the immune microenvironment of different subtypes. A prognostic signature based on the identified ANRGs was constructed via univariate, LASSO and multivariate Cox regression analyses. KEGG, GO and GSEA were used for functional enrichment of genes associated with different risk subtypes. qPCR, WB and IHC were used to validate the expression of candidate genes.
Results
Two anoikis-related patterns with distinct clinical features and immune statuses were identified based on prognosis-related ANRGs. Cluster 2 had more active immunogenicity and a better prognosis than Cluster 1. Subsequently, we developed and validated an anoikis prognostic signature demonstrating excellent predictive ability for the prognosis of osteosarcoma. Anoikis risk score was positively associated with osteosarcoma metastasis and was identified as an independent prognostic marker. Additionally, a nomogram was established to predict the 3- and 5-year survival probability of patients with osteosarcoma. Functional enrichment analysis revealed that immune dysregulation was correlated with poor prognosis. Besides, patients in the low-risk group had higher infiltration levels of immune cells and more active immune function than patients in the high-risk group. Drug sensitivity analysis revealed several chemotherapeutic agents for the treatment of different subtypes of osteosarcoma.