Honghui Hospital, Xi'an Jiaotong University, Xi'an Honghui Hospital North District, Xi'an, Shanxi, China.
Front Immunol. 2024 Jul 23;15:1424950. doi: 10.3389/fimmu.2024.1424950. eCollection 2024.
Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.
骨肉瘤(OS)是一种侵袭性和高致死性的骨肿瘤,突出表明迫切需要进一步探索其潜在机制。在这项研究中,我们利用来自公共数据库的 OS 和健康对照样本的批量转录组测序数据以及单细胞测序数据进行了分析。首先,我们评估了四个肿瘤微环境(TME)相关基因集在肿瘤和对照组之间的差异表达。随后,对肿瘤组织进行无监督聚类分析,鉴定出两个明显不同的簇。我们使用基因集变异分析(GSVA)计算了四个 TME 相关基因集在簇 1(C1)和簇 2(C2)的差异得分,然后进行单变量 Cox 分析。对于这两个簇,我们进行了生存分析,检查了临床病理分布的差异,分析了免疫细胞浸润和免疫逃逸预测,评估了免疫浸润丰度的差异,并评估了药物敏感性。对两个簇之间的差异表达基因(DEGs)进行了基因本体论(GO)和基因集富集分析(GSEA)。我们在 TARGET-OS 数据集上进行了加权基因共表达网络分析(WGCNA),以识别关键基因,然后进行 GO 富集分析。使用 LASSO 和多元回归分析,我们构建了一个包含 11 个基因(ALOX5AP、CD37、BIN2、C3AR1、HCLS1、ACSL5、CD209、FCGR2A、CORO1A、CD74、CD163)的预后模型,该模型在训练和验证队列中均具有良好的诊断效果和预后潜力。使用该模型,我们进行了进一步的免疫、药物敏感性和富集分析。我们进行了单细胞测序分析的细胞亚群的降维和注释,分析了每个亚群的相关基因的表达谱。我们通过各种湿实验室实验进一步证实了 ACSL5 在 OS 中的作用。我们的研究为 OS 患者的预后、治疗和药物开发提供了新的见解和理论基础。