Li Guoquan, Huang Baoliang, Wu Hao, Zhang Hu
Department of Orthopedics, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Department of Orthopedics, Xiajin People's Hospital, Dezhou, China.
Transl Cancer Res. 2022 Jul;11(7):2374-2387. doi: 10.21037/tcr-22-1706.
Osteosarcoma (OS) is a common malignant bone cancer in children and teenagers that originates from osteoblast cells. Although many biomarkers have been reported in OS, they have not improved the prognosis of this disease. This study sought to identify effective biomarkers for the early diagnosis and prognosis of OS using a comprehensive bioinformatics analysis.
OS-associated microRNAs (miRNAs) were screened in the Human microRNA Disease Database (HMDD). The differentially expressed genes (DEGs) related to OS were screened using 3 data sets (GSE16088, GSE36001, and GSE56001) from the Gene Expression Omnibus (GEO) database. By comparing the targets of these miRNAs with DEGs in response to OS, we identified OS-associated candidate genes. The gene expression and clinical data of 96 OS samples with complete clinical information was downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Comprehensive bioinformatics analyses, including univariate, multivariate Cox, and Kaplan-Meier (KM) analyses were conducted based on these data to identify the prognostic genes and construct prognostic signature for OS survival and recurrence. Logistic regression analysis was performed based on the GSE42352 data set (including 103 OS and 15 normal samples) to develop a diagnostic model for OS.
By comparing the DEGs and predicted targets of the 28 OS survival-associated miRNAs, we identified 267 OS-associated candidate genes. Additionally, 14 genes were found to be significantly associated with the survival of OS patients. Finally, 3 genes [i.e., (), (), and ()] were integrated into a prognostic index. The 3-gene signature was an independent factor for OS survival [hazard ratio (HR) =1.699; P<0.001] and recurrence (HR =2.532; P=0.004) and was found to have an excellent predictive performance [area under the receiver operating characteristic (ROC) curve (AUC) >0.7]. Additionally, 2 genes (i.e., and ) were identified to be associated with OS diagnosis (P<0.05). This 2-gene diagnostic signature for OS presented a good discriminative power (AUC =0.981) and the error between the predicted and actual value was 0.029.
We constructed a 3-gene prognostic signature and a 2-gene diagnostic signature that have the potential to assist in prognosis predicting and diagnosis of OS in clinic.
骨肉瘤（OS）是儿童和青少年中常见的一种起源于成骨细胞的恶性骨癌。尽管在骨肉瘤中已报道了许多生物标志物，但它们并未改善该疾病的预后。本研究旨在通过全面的生物信息学分析来鉴定用于骨肉瘤早期诊断和预后的有效生物标志物。
在人类 microRNA 疾病数据库（HMDD）中筛选与骨肉瘤相关的 microRNA（miRNA）。使用来自基因表达综合数据库（GEO）的 3 个数据集（GSE16088、GSE36001 和 GSE56001）筛选与骨肉瘤相关的差异表达基因（DEG）。通过将这些 miRNA 的靶标与骨肉瘤相关的 DEG 进行比较，我们鉴定出与骨肉瘤相关的候选基因。从治疗应用研究以产生有效治疗方法（TARGET）数据库下载了 96 个具有完整临床信息的骨肉瘤样本的基因表达和临床数据。基于这些数据进行了包括单变量、多变量 Cox 和 Kaplan-Meier（KM）分析在内的全面生物信息学分析，以鉴定预后基因并构建骨肉瘤生存和复发的预后特征。基于 GSE42352 数据集（包括 103 个骨肉瘤样本和 15 个正常样本）进行逻辑回归分析，以建立骨肉瘤的诊断模型。
通过比较 28 个与骨肉瘤生存相关的 miRNA 的 DEG 和预测靶标，我们鉴定出 267 个与骨肉瘤相关的候选基因。此外，发现 14 个基因与骨肉瘤患者的生存显著相关。最后，将 3 个基因[即（）、（）和（）]整合到一个预后指数中。这 3 基因特征是骨肉瘤生存[风险比（HR）=1.699；P<0.001]和复发（HR =2.532；P=0.004）的独立因素，并且发现具有出色的预测性能[受试者操作特征（ROC）曲线下面积（AUC）>0.7]。此外，鉴定出 2 个基因（即和）与骨肉瘤诊断相关（P<0.05）。这种骨肉瘤的 2 基因诊断特征具有良好的判别力（AUC =0.981），预测值与实际值之间的误差为 0.029。
我们构建了一个 3 基因预后特征和一个 2 基因诊断特征，它们有可能在临床上协助骨肉瘤的预后预测和诊断。
