骨肉瘤（OS）是一种流行的原发性骨肿瘤，主要影响儿童和青少年人群，对人类健康提出了相当大的挑战。本研究的目的是开发一个以失巢凋亡相关基因 (ARG) 为中心的预后模型，旨在准确预测 OS 诊断个体的生存结果，并提供调节免疫微环境的见解。该研究的训练队列由 86 名受试者组成OS 患者来自癌症基因组图谱数据库，而验证队列由从基因表达综合数据库中提取的 53 名 OS 患者组成。差异分析利用 GS​​E33382 数据集，包含 3 个正常样本和 84 个 OS 样本。随后，该研究进行了基因本体论和京都基因百科全书和基因组富集分析。通过单变量 COX 回归分析识别与 OS 预后相关的差异表达 ARG，然后进行 LASSO 回归分析，以减轻过度拟合风险并构建稳健的预后模型。通过风险曲线、生存曲线、受试者工作特征曲线、独立预后分析、主成分分析和 t 分布随机邻域嵌入 (t-SNE) 分析来评估模型准确性。此外，还设计了列线图模型，在预测 OS 患者预后方面表现出良好的潜力。进一步的研究结合了基因集富集分析来描绘高风险和低风险群体的活跃途径。此外，通过肿瘤微环境分析、单样本基因集富集分析（ssGSEA）和免疫浸润细胞相关性分析，评估风险预后模型对OS免疫微环境的影响。进行药物敏感性分析以确定对 OS 治疗可能有效的药物。最终，通过实时定量聚合酶链式反应（RT-qPCR）对模型构建中涉及的ARG进行验证。开发了ARG风险预测模型，包括7个高风险ARG（CBS、MYC、 MMP3、CD36、SCD、COL13A1 和 HSP90B1）和四种低风险 ARG（VASH1、TNFRSF1A、PIP5K1C 和 CTNNBIP1）。该预后模型展示了预测患者总体生存率的强大能力。免疫相关性分析表明，在我们的预后模型中，与低风险组相比，高风险组表现出较低的免疫评分。具体而言，CD8 T 细胞、中性粒细胞和肿瘤浸润淋巴细胞在高危组中显着下调，同时检查点和 T 细胞共抑制机制也显着下调。此外，三个免疫检查点相关基因（CD200R1、HAVCR2 和 LAIR1）在高风险组和低风险组之间显示出显着差异。列线图模型的使用在预测 OS 患者的预后方面表现出显着的功效。此外，肿瘤转移作为一个独立的预后因素出现，表明 ARG 和 OS 转移之间存在潜在关联。值得注意的是，我们的研究确定了 8 种药物（Bortezomib、Midostaurin、CHIR.99021、JNK.Inhibitor.VIII、Lenalidomide、Sunitinib、GDC0941 和 GW.441756）对 OS 表现出敏感性。 RT-qPCR 结果表明，OS 中 CBS、MYC、MMP3 和 PIP5K1C 的表达水平降低。相反，在 OS 中观察到 CD36、SCD、COL13A1、HSP90B1、VASH1 和 CTNNBIP1 的表达水平升高。这项研究的结果为预测 OS 患者的生存结果提供了机会。此外，这些发现有望推动专注于与这种特定疾病相关的预后评估和治疗干预的研究工作。版权所有 © 2024 Mingyi Yang 等人。

Osteosarcoma (OS) represents a prevalent primary bone neoplasm predominantly affecting the pediatric and adolescent populations, presenting a considerable challenge to human health. The objective of this investigation is to develop a prognostic model centered on anoikis-related genes (ARGs), with the aim of accurately forecasting the survival outcomes of individuals diagnosed with OS and offering insights into modulating the immune microenvironment.The study's training cohort comprised 86 OS patients sourced from The Cancer Genome Atlas database, while the validation cohort consisted of 53 OS patients extracted from the Gene Expression Omnibus database. Differential analysis utilized the GSE33382 dataset, encompassing three normal samples and 84 OS samples. Subsequently, the study executed gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses. Identification of differentially expressed ARGs associated with OS prognosis was carried out through univariate COX regression analysis, followed by LASSO regression analysis to mitigate overfitting risks and construct a robust prognostic model. Model accuracy was assessed via risk curves, survival curves, receiver operating characteristic curves, independent prognostic analysis, principal component analysis, and t-distributed stochastic neighbor embedding (t-SNE) analysis. Additionally, a nomogram model was devised, exhibiting promising potential in predicting OS patient prognosis. Further investigations incorporated gene set enrichment analysis to delineate active pathways in high- and low-risk groups. Furthermore, the impact of the risk prognostic model on the immune microenvironment of OS was evaluated through tumor microenvironment analysis, single-sample gene set enrichment analysis (ssGSEA), and immune infiltration cell correlation analysis. Drug sensitivity analysis was conducted to identify potentially effective drugs for OS treatment. Ultimately, the verification of the implicated ARGs in the model construction was conducted through the utilization of real-time quantitative polymerase chain reaction (RT-qPCR).The ARGs risk prognostic model was developed, comprising seven high-risk ARGs (CBS, MYC, MMP3, CD36, SCD, COL13A1, and HSP90B1) and four low-risk ARGs (VASH1, TNFRSF1A, PIP5K1C, and CTNNBIP1). This prognostic model demonstrates a robust capability in predicting overall survival among patients. Analysis of immune correlations revealed that the high-risk group exhibited lower immune scores compared to the low-risk group within our prognostic model. Specifically, CD8+ T cells, neutrophils, and tumor-infiltrating lymphocytes were notably downregulated in the high-risk group, alongside significant downregulation of checkpoint and T cell coinhibition mechanisms. Additionally, three immune checkpoint-related genes (CD200R1, HAVCR2, and LAIR1) displayed significant differences between the high- and low-risk groups. The utilization of a nomogram model demonstrated significant efficacy in prognosticating the outcomes of OS patients. Furthermore, tumor metastasis emerged as an independent prognostic factor, suggesting a potential association between ARGs and OS metastasis. Notably, our study identified eight drugs-Bortezomib, Midostaurin, CHIR.99021, JNK.Inhibitor.VIII, Lenalidomide, Sunitinib, GDC0941, and GW.441756-as exhibiting sensitivity toward OS. The RT-qPCR findings indicate diminished expression levels of CBS, MYC, MMP3, and PIP5K1C within the context of OS. Conversely, elevated expression levels were observed for CD36, SCD, COL13A1, HSP90B1, VASH1, and CTNNBIP1 in OS.The outcomes of this investigation present an opportunity to predict the survival outcomes among individuals diagnosed with OS. Furthermore, these findings hold promise for progressing research endeavors focused on prognostic evaluation and therapeutic interventions pertaining to this particular ailment.Copyright © 2024 Mingyi Yang et al.