Department of Academic Research, The Secondary Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, China.
Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, China.
Sci Rep. 2023 Oct 25;13(1):18271. doi: 10.1038/s41598-023-45566-6.
The immune microenvironment of osteosarcoma (OS) has been reported to play an important role in disease progression and prognosis. However, owing to tumor heterogeneity, it is not ideal to predict OS prognosis by examining only infiltrating immune cells. This work aimed to build a prognostic gene signature based on similarities in the immune microenvironments of OS patients. Public datasets were used to examine the correlated genes, and the most consistent dominant infiltrating immune cell type was identified. The LASSO Cox regression model was used to establish a multiple-gene risk prediction signature. A nine-gene prognostic signature was generated from the correlated genes for M0 macrophages and then proven to be effective and reliable in validation cohorts. Signature comparison indicated the priority of the signature. Multivariate Cox regression models indicated that the signature risk score is an independent prognostic factor for OS patients regardless of the Huvos grade in all datasets. In addition, the results of the association between the signature risk score and chemotherapy sensitivity also showed that there was no significant difference in the sensitivity of any drugs between the low- and high-risk groups. A GSEA of GO and KEGG pathways found that antigen processing- and presentation-related biological functions and olfactory transduction receptor signaling pathways have important roles in signature functioning. Our findings showed that M0 macrophages were the dominant infiltrating immune cell type in OS and that the new gene signature is a promising prognostic model for OS patients.
骨肉瘤（OS）的免疫微环境被报道在疾病进展和预后中起重要作用。然而，由于肿瘤异质性，仅通过检查浸润免疫细胞来预测 OS 预后并不理想。本研究旨在基于 OS 患者免疫微环境的相似性构建一个预后基因特征。使用公共数据集来检查相关基因，并确定最一致的优势浸润免疫细胞类型。LASSO Cox 回归模型用于建立多基因风险预测特征。从与 M0 巨噬细胞相关的基因中生成了一个 9 基因预后特征，并在验证队列中证明了其有效性和可靠性。特征比较表明了特征的优先级。多变量 Cox 回归模型表明，无论 Huvos 分级如何，该特征风险评分都是 OS 患者的独立预后因素。此外，特征风险评分与化疗敏感性之间的关联结果也表明，低风险组和高风险组之间任何药物的敏感性均无显著差异。GO 和 KEGG 途径的 GSEA 发现，抗原加工和呈递相关的生物学功能以及嗅觉转导受体信号通路在特征功能中具有重要作用。我们的研究结果表明，M0 巨噬细胞是 OS 中主要浸润的免疫细胞类型，新的基因特征是 OS 患者有前途的预后模型。
