目标：骨肉瘤是儿童和青少年中最常见的原发恶性肿瘤，过去几十年来，骨肉瘤患者的5年生存率没有实质性改善。迫切需要开发有效的骨肉瘤诊断生物标志物。本研究旨在探讨与免疫细胞浸润相关的新型骨肉瘤发展和诊断生物标志物。 方法：从基因表达数据库(Gene Expression Omnibus，GEO)中提取包含骨肉瘤样本的三个数据集(GSE19276，GSE36001，GSE126209)，合并得到基因表达数据。然后，利用limma鉴定出差异表达基因(DEGs)，并对DEGs进行潜在生物学功能和下游通路富集分析。采用LASSO回归模型和SVM-RFE (支持向量机-递归特征消除)分析机器学习算法，识别出用于诊断骨肉瘤患者的潜在核心基因。利用接受者操作特征曲线(ROC曲线)评估这些候选基因在训练集和测试集中的鉴别能力。此外，使用CIBERSORT来描述骨肉瘤中的免疫细胞浸润特征，并研究这些潜在基因与免疫细胞丰度的相关性。采用qRT-PCR和Western印迹验证诊断候选基因的表达。 结果：将GEO数据集分为训练组（合并的GSE19276，GSE36001）和测试组（GSE126209）。在训练集中筛选出了71个DEGs，其中包括10个上调基因和61个下调基因。这些DEGs主要富集在与免疫相关的生物学功能和信号通路中。经过SVM-RFE和LASSO回归模型的机器学习后，选择了四个生物标志物用于诊断骨肉瘤的诊断表格，包括ASNS、CD70、SRGN和TRIB3。这些诊断生物标志物的诊断价值较高（AUC值介于0.900至0.955之间）。此外，这些基因与多种免疫细胞浸润，如单核细胞、巨噬细胞M0和中性粒细胞有显著相关性。 结论：骨肉瘤患者的四个与免疫相关的潜在核心基因(ASNS、CD70、SRGN、TRIB3)具有高诊断价值。这些诊断基因与免疫细胞丰度显著相关，提示它们在骨肉瘤肿瘤免疫微环境中具有重要作用。本研究提供了高准确性的新型诊断候选基因，用于诊断骨肉瘤患者。 版权所有 © 2023 Ji, Lin, Li, Tian, Wu, Wan, Liu and Xu。

Objectives: Osteosarcoma is the most common primary malignant tumor in children and adolescents, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past decades. Effective biomarkers in diagnosing osteosarcoma are warranted to be developed. This study aims to explore novel biomarkers correlated with immune cell infiltration in the development and diagnosis of osteosarcoma. Methods: Three datasets (GSE19276, GSE36001, GSE126209) comprising osteosarcoma samples were extracted from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression. Then, differentially expressed genes (DEGs) were identified by limma and potential biological functions and downstream pathways enrichment analysis of DEGs was performed. The machine learning algorithms LASSO regression model and SVM-RFE (support vector machine-recursive feature elimination) analysis were employed to identify candidate hub genes for diagnosing patients with osteosarcoma. Receiver operating characteristic (ROC) curves were developed to evaluate the discriminatory abilities of these candidates in both training and test sets. Furthermore, the characteristics of immune cell infiltration in osteosarcoma, and the correlations between these potential genes and immune cell abundance were illustrated using CIBERSORT. qRT-PCR and western blots were conducted to validate the expression of diagnostic candidates. Results: GEO datasets were divided into the training (merged GSE19276, GSE36001) and test (GSE126209) groups. A total of 71 DEGs were screened out in the training set, including 10 upregulated genes and 61 downregulated genes. These DEGs were primarily enriched in immune-related biological functions and signaling pathways. After machine learning by SVM-RFE and LASSO regression model, four biomarkers were chosen for the diagnostic nomogram for osteosarcoma, including ASNS, CD70, SRGN, and TRIB3. These diagnostic biomarkers all possessed high diagnostic values (AUC ranging from 0.900 to 0.955). Furthermore, these genes were significantly correlated with the infiltration of several immune cells, such as monocytes, macrophages M0, and neutrophils. Conclusion: Four immune-related candidate hub genes (ASNS, CD70, SRGN, TRIB3) with high diagnostic value were confirmed for osteosarcoma patients. These diagnostic genes were significantly connected with the immune cell abundance, suggesting their critical roles in the osteosarcoma tumor immune microenvironment. Our study provides highlights on novel diagnostic candidate genes with high accuracy for diagnosing osteosarcoma patients.Copyright © 2023 Ji, Lin, Li, Tian, Wu, Wan, Liu and Xu.