He Yi, Zhou Haiting, Xu Haoran, You Hongbo, Cheng Hao
Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2022 Apr 14;12:769202. doi: 10.3389/fonc.2022.769202. eCollection 2022.
Osteosarcoma is one of the most common bone tumors in teenagers. We hope to provide a reliable method to predict the prognosis of osteosarcoma and find potential targets for early diagnosis and precise treatment. To address this issue, we performed a detailed bioinformatics analysis based on the Cancer Genome Atlas (TCGA). A total of 85 osteosarcoma patients with gene expression data and clinicopathological features were included in this study, which was considered the entire set. They were randomly divided into a train set and a test set. We identified six lncRNAs (ELFN1-AS1, LINC00837, OLMALINC, AL669970.3, AC005332.4 and AC023157.3), and constructed a signature that exhibited good predictive ability of patient survival and metastasis. What's more, we found that risk score calculated by the signature was positively correlated to tumor purity, CD4 naive T cells, and negatively correlated to CD8 T cells. Furthermore, we investigated each lncRNA in the signature and found that these six lncRNAs were associated with tumorigenesis and immune cells in the tumor microenvironment. In conclusion, we constructed and validated a signature, which had good performance in the prediction of survival, metastasis and immune microenvironment. Our study indicated possible mechanisms of these lncRNAs in the development of osteosarcoma, which may provide new insights into the precise treatment of osteosarcoma.
骨肉瘤是青少年中最常见的骨肿瘤之一。我们希望提供一种可靠的方法来预测骨肉瘤的预后,并找到早期诊断和精准治疗的潜在靶点。为解决这一问题,我们基于癌症基因组图谱(TCGA)进行了详细的生物信息学分析。本研究纳入了85例具有基因表达数据和临床病理特征的骨肉瘤患者,将其视为全集。他们被随机分为训练集和测试集。我们鉴定出6种长链非编码RNA(ELFN1-AS1、LINC00837、OLMALINC、AL669970.3、AC005332.4和AC023157.3),并构建了一个对患者生存和转移具有良好预测能力的特征模型。此外,我们发现由该特征模型计算出的风险评分与肿瘤纯度、CD4幼稚T细胞呈正相关,与CD8 T细胞呈负相关。此外,我们对特征模型中的每种长链非编码RNA进行了研究,发现这6种长链非编码RNA与肿瘤发生以及肿瘤微环境中的免疫细胞有关。总之,我们构建并验证了一个在生存、转移和免疫微环境预测方面具有良好性能的特征模型。我们的研究揭示了这些长链非编码RNA在骨肉瘤发生发展中的可能机制,这可能为骨肉瘤的精准治疗提供新的见解。