Jin Zhijian, Wu Jintao, Lin Jianwei, Wang Jun, Shen Yuhui
Department of Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Genet. 2022 Jun 6;13:862803. doi: 10.3389/fgene.2022.862803. eCollection 2022.
Osteosarcoma is a malignant tumor with a poor prognosis. Nowadays, there is a lack of good methods to assess the prognosis of osteosarcoma patients. Transcription co-factors (TcoFs) play crucial roles in transcriptional regulation through the interaction with transcription factors (TFs). Many studies have revealed that TcoFs are related to many diseases, especially cancer. However, few studies have been reported about prognostic prediction models of osteosarcoma by using TcoF-related genes. In order to construct a prognostic risk model with TcoF-related genes, the mRNA expression data and matched clinical information of osteosarcoma were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and the Gene Expression Omnibus (GEO) database. TARGET was used as a training set and GSE21257 from GEO was used as a validation set. Univariate Cox regression was performed to select 13 TcoF-related candidate genes, of which five genes (, , , , and ) were finally used to construct the prognostic risk model by LASSO Cox regression analysis. The Kaplan-Meier (K-M) survival curves showed an obvious difference between high- and low-risk groups. The receiver operating characteristic (ROC) curves based on TARGET demonstrated that this risk model was credible (1-year AUC: 0.607; 3-years AUC: 0.713; 5-years AUC: 0.736). Meanwhile, the risk model was associated with immune cells and immune-related functions. By combining the risk score and clinical factors, the nomogram of osteosarcoma was assessed with a C-index of 0.738 to further support the reliability of this 5-gene prognostic risk model. Finally, the expression of TcoF-related genes was validated in different cell lines by quantitative real-time PCR (qRT-PCR) and also in different tissue samples by immunohistochemistry (IHC). In conclusion, the model can predict the prognosis of osteosarcoma patients and may provide novel targets for the treatment of osteosarcoma patients.
骨肉瘤是一种预后较差的恶性肿瘤。目前，缺乏评估骨肉瘤患者预后的良好方法。转录共因子（TcoFs）通过与转录因子（TFs）相互作用在转录调控中发挥关键作用。许多研究表明，TcoFs与许多疾病有关，尤其是癌症。然而，关于使用TcoF相关基因构建骨肉瘤预后预测模型的研究报道较少。为了构建一个基于TcoF相关基因的预后风险模型，从治疗应用研究以生成有效治疗（TARGET）数据库和基因表达综合数据库（GEO）下载了骨肉瘤的mRNA表达数据和匹配的临床信息。TARGET用作训练集，来自GEO的GSE21257用作验证集。进行单变量Cox回归以选择13个TcoF相关候选基因，其中5个基因（，，，和）最终通过LASSO Cox回归分析用于构建预后风险模型。Kaplan-Meier（K-M）生存曲线显示高风险组和低风险组之间存在明显差异。基于TARGET的受试者工作特征（ROC）曲线表明该风险模型是可信的（1年AUC：0.607；3年AUC：0.713；5年AUC：0.736）。同时，该风险模型与免疫细胞和免疫相关功能有关。通过结合风险评分和临床因素，评估骨肉瘤的列线图C指数为0.738，以进一步支持这个5基因预后风险模型的可靠性。最后，通过定量实时PCR（qRT-PCR）在不同细胞系中以及通过免疫组织化学（IHC）在不同组织样本中验证了TcoF相关基因的表达。总之，该模型可以预测骨肉瘤患者的预后，并可能为骨肉瘤患者的治疗提供新的靶点。
