Division of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
Trauma Department of Orthopaedics, The Affiliated Yuebei People's Hospital of Shantou University Medical College, Shaoguan, Guangdong Province, China.
BMC Cancer. 2023 Feb 22;23(1):181. doi: 10.1186/s12885-023-10660-5.
This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients.
Firstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model's accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes' mRNA and protein expressions were verified.
A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB).
The new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.
本研究旨在深入了解基于骨形态发生蛋白（BMPs）相关基因的新型骨肉瘤（OS）特征，并确认与 OS 患者总体生存相关的预后模式。
首先，通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）进行途径分析，以寻找与 OS 特异性差异表达 BMPs 相关基因（DEBRGs）相关的可能预后机制。其次，通过单变量和多变量 Cox 分析筛选预后 DEBRGs，并使用最小绝对值收缩和选择算子（LASSO）回归分析建立 OS 患者的多基因风险预测模型。接收器操作特征（ROC）曲线衡量模型的准确性。然后，使用 GEO 数据库（GSE21257）进行独立验证。使用多变量 Cox 回归启动列线图。计算 OS 样本的免疫浸润。最后，验证三个发现的标志性基因的 mRNA 和蛋白表达。
在 OS 和对照样本中发现了 46 个 DEBRGs，并在 LASSO 回归分析下筛选出三个预后 DEBRGs（DLX2、TERT 和 EVX1）。多变量和单变量 Cox 回归分析用于构建 OS 风险模型。TARGET 训练和验证集均表明，基于 ROC 曲线，基于预后生物标志物的风险评分模型表现良好。在高风险和低风险组中，免疫细胞包括记忆 B、激活肥大细胞、静止肥大细胞、浆细胞和激活记忆 CD4+T 细胞，以及免疫、基质和 ESTIMATE 评分存在显著差异。根据 OS 患者的临床特征和风险评分，建立了预测生存的列线图，具有良好的性能。最后，使用定量实时聚合酶链反应（qRT-PCR）和蛋白质印迹（WB）研究了 OS 细胞系中三个关键 BMP 相关基因的表达。
新型与 BMP 相关的 OS 预后特征可以作为识别生物标志物的新工具，用于早期发现疾病，并为未来更好地治疗 OS 提供潜在候选方案。
