Abstract
Osteosarcoma is a malignant tumor most commonly arising in children and adolescents and associated with poor prognosis. In recent years, some prognostic models have been constructed to assist clinicians in the treatment of osteosarcoma. However, the prognosis and treatment of patients with osteosarcoma remain unsatisfactory. Notably, super-enhancer (SE)-associated genes strongly promote the progression of osteosarcoma. In the present study, we constructed a novel effective prognostic model using SE-associated genes from osteosarcoma. Five SE-associated genes were initially screened through the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, as well as univariate and multivariate Cox regression analyses. Meanwhile, a risk score model was constructed using the expression of these five genes. The excellent performance of the five-SE-associated-gene-based prognostic model was determined via time-dependent receiver operating characteristic (ROC) curves and Kaplan–Meier curves. Inferior outcome of overall survival (OS) was predicted in the high-risk group. A nomogram based on the polygenic risk score model was further established to validate the performance of the prognostic model. It showed that our prognostic model performed outstandingly in predicting 1-, 3-, and 5-year OS of patients with osteosarcoma. Meanwhile, these five genes also belonged to the hub genes associated with survival and necrosis of osteosarcoma according to the result of weighted gene co-expression network analysis based on the dataset of GSE39058. Therefore, we believe that the five-SE-associated-gene-based prognostic model established in this study can accurately predict the prognosis of patients with osteosarcoma and effectively assist clinicians in treating osteosarcoma in the future.
Highlights
Osteosarcoma, the most common primary bone tumor, is most commonly arising in children and adolescents (Bielack et al, 2009)
least absolute shrinkage and selection operator (Lasso) models were reconstructed according to the λmin and λlse, and survival probabilities were further estimated based on two gene lists (Figure 2D)
The outcome of receiver operating characteristic (ROC) curve analysis showed that the area under the curve minimum (AUCmin) was 0.92, implying that the five-gene-based Lasso model performed well in predicting the probability of overall survival (OS) of patients with osteosarcoma (Figure 2E)
Summary
Osteosarcoma, the most common primary bone tumor, is most commonly arising in children and adolescents (Bielack et al, 2009). The survival rate of patients with osteosarcoma is markedly low due to the tendency of osteosarcoma for early metastasis and rapid progression (Kempf-Bielack et al, 2005). The overall 5-year survival rate of patients with nonmetastatic osteosarcoma is approximately 60–70%, while the survival rate of osteosarcoma patients with metastatic symptoms is only 20–30% (Kempf-Bielack et al, 2005; Luetke et al, 2014; Meazza and Scanagatta, 2016). High-grade patients comprise nearly 80–90% of those diagnosed with osteosarcoma; the treatment of these patients is challenging (Bielack et al, 2009). Numerous traditional predictive factors, such as age, necrosis, recurrence, and clinical stage, are important contributors to clinical outcome, they are less effective in predicting the survival status due to the complex molecular mechanisms of osteosarcoma progression. It is urgent to investigate novel effective molecular biomarkers for the more precise prediction of the prognosis of patients with osteosarcoma
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