Osteosarcoma (OS), known for its high recurrence and metastasis rates, poses a significant challenge in oncology. Our research investigates the role of programmed cell death (PCD) genes in OS and develops a prognostic model using advanced bioinformatics. We analyzed single‐cell sequencing data from the Gene Expression Omnibus (GEO) database to identify subpopulations, distinguish malignant from non‐malignant cells, assess cell cycle phases, and map PCD gene distribution. Additionally, we applied consistency clustering to bulk sequencing data from GEO and TARGET (Therapeutically Applicable Research to Generate Effective Treatments) databases, facilitating survival analysis across clusters with the Kaplan–Meier method. We calculated PCD scores for each cluster using the Single‐sample Gene Set Enrichment Analysis (ssGSEA), which enabled a detailed examination of PCD‐related gene expression and pathway scores. Our study also explored drug sensitivity differences and conducted comprehensive immune cell infiltration analyses using various algorithms. We identified differentially expressed genes, leading to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses that provided insights into relevant biological processes and pathways. The prognostic model, based on five pivotal genes (BAMBI, TMCC2, NOX4, DKK1, and CBS), was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and validated in the TARGET‐OS and GSE16091 datasets, showing significant predictive accuracy. This research enhances our understanding of PCD in OS and supports the development of effective treatments.

中文翻译：

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破译骨肉瘤中的程序性细胞死亡机制以建立预后模型

骨肉瘤（OS）以其高复发率和转移率而闻名，对肿瘤学提出了重大挑战。我们的研究调查了程序性细胞死亡 (PCD) 基因在 OS 中的作用，并利用先进的生物信息学开发了一个预后模型。我们分析了来自基因表达综合 (GEO) 数据库的单细胞测序数据，以识别亚群、区分恶性细胞和非恶性细胞、评估细胞周期阶段并绘制 PCD 基因分布图。此外，我们将一致性聚类应用于来自 GEO 和 TARGET（产生有效治疗的治疗应用研究）数据库的批量测序数据，通过 Kaplan-Meier 方法促进跨聚类的生存分析。我们使用单样本基因集富集分析 (ssGSEA) 计算了每个簇的 PCD 分数，这使得能够详细检查 PCD 相关基因表达和通路分数。我们的研究还探讨了药物敏感性差异，并使用各种算法进行了全面的免疫细胞浸润分析。我们鉴定了差异表达的基因，从而进行了基因本体论 (GO) 和京都基因和基因组百科全书 (KEGG) 富集分析，为相关生物过程和途径提供了见解。该预后模型基于五个关键基因（BAMBI、TMCC2、NOX4、DKK1 和 CBS），使用最小绝对收缩和选择算子 (LASSO) 算法开发，并在 TARGET-OS 和 GSE16091 数据集中进行了验证，显示出显着的预测性准确性。这项研究增强了我们对 OS 中 PCD 的理解，并支持有效治疗方法的开发。