DataSheet_1_A novel molecular signature for predicting prognosis and immunotherapy response in osteosarcoma based on tumor-infiltrating cell marker genes.docx

BackgroundTumor infiltrating lymphocytes (TILs), the main component in the tumor microenvironment, play a critical role in the antitumor immune response. Few studies have developed a prognostic model based on TILs in osteosarcoma.
MethodsScRNA-seq data was obtained from our previous research and bulk RNA transcriptome data was from TARGET database. WGCNA was used to obtain the immune-related gene modules. Subsequently, we applied LASSO regression analysis and SVM algorithm to construct a prognostic model based on TILs marker genes. What’s more, the prognostic model was verified by external datasets and experiment in vitro.
ResultsEleven cell clusters and 2044 TILs marker genes were identified. WGCNA results showed that 545 TILs marker genes were the most strongly related with immune. Subsequently, a risk model including 5 genes was developed. We found that the survival rate was higher in the low-risk group and the risk model could be used as an independent prognostic factor. Meanwhile, high-risk patients had a lower abundance of immune cell infiltration and many immune checkpoint genes were highly expressed in the low-risk group. The prognostic model was also demonstrated to be a good predictive capacity in external datasets. The result of RT-qPCR indicated that these 5 genes have differential expression which accorded with the predicting outcomes.
ConclusionsThis study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response.

背景：肿瘤浸润淋巴细胞（Tumor infiltrating lymphocytes, TILs）是肿瘤微环境的核心组成部分，在抗肿瘤免疫应答中发挥关键作用。目前针对骨肉瘤中基于TILs构建预后模型的研究较为匮乏。
方法：本研究的单细胞RNA测序（single-cell RNA-seq, scRNA-seq）数据来自既往研究，批量RNA转录组数据则下载自TARGET数据库。首先通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）筛选免疫相关基因模块；随后结合LASSO回归分析与支持向量机（support vector machine, SVM）算法，基于TILs标记基因构建预后模型。此外，本研究还通过外部数据集与体外实验对该预后模型进行了验证。
结果：本研究共鉴定出11个细胞簇与2044个TILs标记基因。WGCNA分析显示，其中545个TILs标记基因与免疫进程关联最为紧密。最终构建了包含5个基因的风险预测模型，结果显示低风险组患者生存率显著更高，且该风险模型可作为独立的预后因子。同时，高风险组患者的免疫细胞浸润丰度更低，而低风险组中多个免疫检查点基因表达水平更高。该预后模型在外部数据集中同样展现出良好的预测性能。实时定量聚合酶链反应（real-time quantitative polymerase chain reaction, RT-qPCR）结果证实，这5个基因的表达差异与模型预测结果一致。
结论：本研究基于TILs标记基因构建了全新的分子特征标签，该标签在预测骨肉瘤预后与免疫治疗应答方面具有良好的应用价值。