Identification of the Transcription Co-Factor–Related Gene Signature and Risk Score Model for Osteosarcoma - Jin, Zhijian; Wu, Jintao; Lin, Jianwei; Wang, Jun; Shen, Yuhui (Frontiers in Genetics, 2022)
Osteosarcoma is a malignant tumor with 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 Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and Gene Expression Omnibus (GEO) database. TARGET was used as training set and GSE21257 from GEO was used as validation set. Univariate Cox regression was performed to select 13 TcoF-related candidate genes, 5-genes (LMO2, MAML3, MTF2, RBPMS and SIRT1) of which were finally used to construct the prognostic risk model by LASSO Cox regression analysis. The Kaplan-Meier (K-M) survival curves showed 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-year AUC: 0.713; 5-year AUC: 0.736). Meanwhile, the risk model was associated with immune cells, and immune-related functions. By combining risk score and clinical factors, nomogram of osteosarcoma was assessed with a C-index of 0.738 to further support the reliability of this 5-genes prognostic risk model. Finally, the expression of TcoF-related genes were 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.