Department of Spine and Osteopathic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Front Immunol. 2023 Apr 6;14:1150588. doi: 10.3389/fimmu.2023.1150588. eCollection 2023.
Tumor 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.
ScRNA-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 .
Eleven 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.
This study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response.
肿瘤浸润淋巴细胞（TILs）是肿瘤微环境中的主要成分，在抗肿瘤免疫反应中发挥着关键作用。目前很少有研究基于骨肉瘤中的 TILs 构建预后模型。
我们从之前的研究中获得了单细胞 RNA 测序（scRNA-seq）数据，从 TARGET 数据库中获得了批量 RNA 转录组数据。采用 WGCNA 获得与免疫相关的基因模块。然后，我们应用 LASSO 回归分析和 SVM 算法，构建基于 TILs 标记基因的预后模型。此外，我们还通过外部数据集和实验对该预后模型进行了验证。
鉴定出 11 个细胞簇和 2044 个 TILs 标记基因。WGCNA 结果表明，545 个 TILs 标记基因与免疫的相关性最强。随后，我们构建了一个包含 5 个基因的风险模型。结果显示，低危组的生存率较高，该风险模型可作为独立的预后因素。此外，高危患者的免疫细胞浸润丰度较低，低危组中许多免疫检查点基因表达较高。该预后模型在外部数据集也表现出良好的预测能力。实时荧光定量 PCR（RT-qPCR）的结果表明，这 5 个基因的差异表达与预测结果一致。
本研究基于 TILs 标记基因构建了一种新的分子特征，可有效预测骨肉瘤的预后和免疫治疗反应。
