Department of Orthopedics Trauma and Hand Surgery, Guangxi Medical University First Affiliated Hospital, Guangxi Medical University, Nanning, 530021, China.
Guangxi Medical University, Nanning, 530021, China.
Sci Rep. 2022 Jan 31;12(1):1617. doi: 10.1038/s41598-022-05103-3.
Osteosarcoma accounts for a frequently occurring cancer of the primary skeletal system. In osteosarcoma cells, a hypoxic microenvironment is commonly observed that drives tumor growth, progression, and heterogeneity. Hypoxia and tumor-infiltrating immune cells might be closely related to the prognosis of osteosarcoma. In this study, we aimed to determine the biomarkers and therapeutic targets related to hypoxia and immunity through bioinformatics methods to improve the clinical prognosis of patients. We downloaded the gene expression data of osteosarcoma samples and normal samples in the UCSC Xena database and GTEx database, respectively, and downloaded the validation dataset (GSE21257) in the GEO database. Subsequently, we performed GO enrichment analysis and KEGG pathway enrichment analysis on the data of the extracted osteosarcoma hypoxia-related genes. Through univariate COX regression analysis, lasso regression analysis, multivariate COX regression analysis, etc., we established a predictive model for the prognosis of osteosarcoma. Five genes, including ST3GAL4, TRIM8, STC2, TRPS1, and FAM207A, were found by screening. In particular, we analyzed the immune cell composition of each gene based on the five genes through the CIBERSORT algorithm and verified each gene at the cell and tissue level. Our findings are valuable for the clinical diagnosis and treatment of this disease.
骨肉瘤是原发性骨骼系统中常见的癌症。在骨肉瘤细胞中，常观察到缺氧微环境，该环境促进肿瘤生长、进展和异质性。缺氧和肿瘤浸润免疫细胞可能与骨肉瘤的预后密切相关。在这项研究中，我们旨在通过生物信息学方法确定与缺氧和免疫相关的生物标志物和治疗靶点，以改善患者的临床预后。我们分别从 UCSC Xena 数据库和 GTEx 数据库下载骨肉瘤样本和正常样本的基因表达数据，并从 GEO 数据库下载验证数据集（GSE21257）。随后，我们对提取的骨肉瘤缺氧相关基因数据进行 GO 富集分析和 KEGG 通路富集分析。通过单变量 COX 回归分析、lasso 回归分析、多变量 COX 回归分析等，我们建立了骨肉瘤预后的预测模型。通过筛选，发现了 ST3GAL4、TRIM8、STC2、TRPS1 和 FAM207A 这 5 个基因。特别是，我们通过 CIBERSORT 算法基于这 5 个基因分析了每个基因的免疫细胞组成，并在细胞和组织水平验证了每个基因。我们的研究结果对该疾病的临床诊断和治疗具有重要价值。
