DataSheet_2_A cuproptosis-related genes signature associated with prognosis and immune cell infiltration in osteosarcoma.csv

Osteosarcoma (OS) is one of the most prevalent primary bone tumors at all ages of human development. The objective of our study was to develop a model of Cuproptosis-Related Genes (CRGs) for predicting prognosis in OS patients. All datasets of OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. We obtained the gene set (81 CRGs) related to cuproptosis by accessing the database and previous literature. All the CRGs were analyzed by univariate COX regression, least absolute shrinkage and selection operator (LASSO) COX regression analysis to screen for CRGs associated with prognosis in OS patients. Then these CRGs were used to construct a prognostic signature, which was further verified by independent cohort (GSE21257) and clinical correlation analysis. Afterward, to identify underlying mechanisms, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for the high-risk group by using the GSEA method. The association between the prognostic signature and 28 types of immune infiltrating cells in the tumor microenvironment was assessed. Ultimately, Lipoic Acid Synthetase (LIAS) (HR=0.632, P=0.004), Lipoyltransferase 1 (LIPT1) (HR=0.524, P=0.011), BCL2 Like 1 (BCL2L1/BCL-XL) (HR=0.593, P=0.022), and Pyruvate Dehydrogenase Kinase 1 (PDK1) (HR=0.662, P=0.025) were identified. Subsequently, they were used to calculate the risk score and build a prognostic model. In the training cohort, risk score (HR=1.878, P=0.003) could be considered as an independent prognostic factor, and OS patients with high-risk scores showed lower survival rates. Biological pathways related to substance metabolism and transport were enriched. There were significant differences in immune infiltrating cells in the tumor microenvironment. All in all, The CRGs signature is related to the tumor immune microenvironment and could be used as a credible predictor of the prognostic status in OS patients.

骨肉瘤（Osteosarcoma, OS）是人类各发育阶段最常见的原发性骨肿瘤之一。本研究旨在构建铜死亡相关基因（Cuproptosis-Related Genes, CRGs）模型，以预测骨肉瘤患者的预后。所有骨肉瘤患者数据集均来自治疗应用研究以生成有效治疗方案（Therapeutically Applicable Research to Generate Effective Treatments, TARGET）数据库及基因表达综合（Gene Expression Omnibus, GEO）数据库。我们通过检索公共数据库及既往文献，获取了81个铜死亡相关基因集。对所有铜死亡相关基因开展单因素COX回归、最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）COX回归分析，筛选出与骨肉瘤患者预后相关的铜死亡相关基因。随后利用上述基因构建预后特征模型，并通过独立队列（GSE21257）及临床相关性分析对该模型进行验证。为进一步探究潜在发病机制，我们采用基因集富集分析（Gene Set Enrichment Analysis, GSEA）对高危组进行基因本体（Gene Ontology, GO）分析及京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）分析。评估了该预后特征与肿瘤微环境中28种免疫浸润细胞的关联。最终筛选得到硫辛酰胺合成酶（Lipoic Acid Synthetase, LIAS）（风险比HR=0.632，P=0.004）、硫辛酰转移酶1（Lipoyltransferase 1, LIPT1）（HR=0.524，P=0.011）、BCL2样蛋白1（BCL2 Like 1, BCL2L1/BCL-XL）（HR=0.593，P=0.022）及丙酮酸脱氢酶激酶1（Pyruvate Dehydrogenase Kinase 1, PDK1）（HR=0.662，P=0.025）。随后以这些基因构建风险评分公式，搭建预后模型。在训练队列中，风险评分（HR=1.878，P=0.003）可作为独立预后因素，高风险评分的骨肉瘤患者生存率更低。本研究富集到物质代谢与转运相关的生物学通路，肿瘤微环境中的免疫浸润细胞存在显著差异。总而言之，铜死亡相关基因特征与肿瘤免疫微环境密切相关，可作为骨肉瘤患者预后状态的可靠预测指标。