Chen Zhuo, Ni Renhua, Hu Yuanyu, Yang Yiyuan, Chen Jiawen, Tian Yun
Department of Orthopedics, Peking University Third Hospital, Beijing, China.
Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Transl Cancer Res. 2025 Jan 31;14(1):254-271. doi: 10.21037/tcr-24-1398. Epub 2025 Jan 23.
Osteosarcoma (OS) is an aggressive and fast-growing malignant tumor associated with high mortality. Early diagnosis and prompt treatment can markedly enhance prognosis and increase survival rates. Constructing prognostic models can effectively predict OS progression, assist in patient diagnosis, and provide personalized treatment plans. In this study, we identified OS-related prognostic genes using the weighted gene co-expression network analysis (WGCNA) method to construct and validate a robust prognostic model, providing guidance for patient risk assessment and clinical treatment.
Clinical data for OS samples were collected from the Gene Expression Omnibus (GEO) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. Statistical analyses, including enrichment analysis, cluster analysis, and model construction, were performed using the R programme.
The WGCNA method was used to identify genes which were important to OS development and progression, screening for those relevant to prognosis to build a reliable and widely applicable model. To enhance the model's applicability to diverse OS patient populations, we initially conducted a clustering analysis based on the identified prognostic-related key genes. We then identified differentially expressed genes (DEGs) between clusters and used these genes to subtype OS patients, assessing their ability to distinguish among different patient populations. Subsequently, we selected prognostic-related DEGs to establish the prognostic model, resulting in a risk scoring method utilizing the expression of creatine kinase, mitochondrial 2 () and cell growth regulator with EF-hand domain 1 (). We validated the predictive capability of the constructed prognostic model, confirming its robust predictive performance. Finally, based on our prognostic model, we analyzed the immune infiltration and drug sensitivity of OS patients, aiding in evaluating responses to immunotherapy and optimizing treatment plans.
A predictive model based on OS-related prognostic genes was constructed to accurately evaluate risk and guide treatment in OS patients, and and were identified as potential therapeutic targets.
骨肉瘤（OS）是一种侵袭性强、生长迅速的恶性肿瘤，死亡率高。早期诊断和及时治疗可显著改善预后并提高生存率。构建预后模型可有效预测骨肉瘤进展，辅助患者诊断，并提供个性化治疗方案。在本研究中，我们使用加权基因共表达网络分析（WGCNA）方法鉴定骨肉瘤相关的预后基因，以构建并验证一个可靠的预后模型，为患者风险评估和临床治疗提供指导。
从基因表达综合数据库（GEO）和生成有效治疗方法的治疗应用研究（TARGET）数据库收集骨肉瘤样本的临床数据。使用R程序进行统计分析，包括富集分析、聚类分析和模型构建。
采用WGCNA方法鉴定对骨肉瘤发生和进展重要的基因，筛选与预后相关的基因以构建一个可靠且广泛适用的模型。为提高该模型对不同骨肉瘤患者群体的适用性，我们首先基于鉴定出的预后相关关键基因进行聚类分析。然后我们鉴定簇间差异表达基因（DEG），并使用这些基因对骨肉瘤患者进行亚型分类，评估其区分不同患者群体的能力。随后，我们选择与预后相关的DEG建立预后模型，得到一种利用肌酸激酶、线粒体2（）和含EF手结构域1的细胞生长调节因子（）表达的风险评分方法。我们验证了构建的预后模型的预测能力，证实其具有强大的预测性能。最后，基于我们的预后模型，我们分析了骨肉瘤患者的免疫浸润和药物敏感性，有助于评估免疫治疗反应并优化治疗方案。
构建了基于骨肉瘤相关预后基因的预测模型，以准确评估骨肉瘤患者的风险并指导治疗，且和被鉴定为潜在治疗靶点。
