Objective To analyze and verify the role of senescent genes in the treatment, prognosis, and tumor microenvironment (TME) characteristics of osteoblastic osteosarcoma, bioinformatic methods were employed. Methods Senescent genes were obtained from the China National Genome Science database (https://ngdc.cncb.ac.cn/aging/index). The gene expression profile and clinical information of osteosarcoma patients were sourced from the TARGET database (https://ocg.cancer.gov/programs/target), while single-cell RNA-sequencing (scRNA-seq) data was collected from GSE162454 on the Gene Expression Omnibus (GEO) for downstream analysis. Osteosarcoma cells were classified based on scRNA-seq, and differential expression analysis between osteoblasts/chondroblasts and other cell types was conducted to identify differently expressed genes (DEGs). After matching with the senescent genes, prognostic senescent DEGs were identified through univariable and multivariable Cox regression analysis. Subsequently, the osteosarcoma senescent-related model (OSRM) was constructed, and the risk score was calculated. The role of OSRM in treatment, prognosis, and TME of osteosarcoma was further investigated. Results The analysis revealed that GSE162454 contained 6 osteosarcoma samples, with 19 933 cells identified after filtering, quality control, and normalization. Seventeen cellular subtypes were identified using uniform manifold approximation and projection (UMAP) methods. A total of 4821 DEGs were found between osteoblasts/chondroblasts and other subtypes, with 132 senescent DEGs obtained after matching with the senescent gene set. In the TARGET database, 4 prognostic senescent DEGs [ADH5 (alcohol dehydrogenase 5), ARHGAP1 (Rho GTPase activating protein 1), APOE (apolipoprotein E), and ATF4 (activating transcription factor 4)] were identified through univariable and multivariable Cox analyses to construct OSRM. Based on risk score, patients were stratified into high- and low-risk groups, with the latter showing better prognosis (HR=0.13, 95%CI 0.06-0.28, P<0.001) and higher sensitivity to immune checkpoint inhibitors. qRT-PCR and Western blotting confirmed the high expression of senescent genes ADH5 (P<0.01), APOE (P<0.01), and ATF4 (P<0.05) in the K7M2 osteosarcoma cell line, suggesting the potential for predicting the response to anti-PD-1 immunotherapy for osteosarcoma. Conclusions scRNA-seq facilitated the division of osteosarcoma into 17 cell subtypes. ADH5, ARHGAP1, APOE, and ATF4 emerged as potential cancer-promoting or suppressing senescent genes in osteosarcoma. OSRM was found to be associated with treatment response, prognosis, and TME characteristics, thereby promoting the molecular pathological diagnosis of osteoblastic osteosarcoma and prediction for anti-PD-1 immunotherapy.