Niu Chicheng, Wang Weiwei, Xu Qingyuan, Tian Zhao, Li Hao, Ding Qiang, Guo Liang, Zeng Ping
Guangxi University of Chinese Medicine, Nanning, 530200, China.
The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, 530023, China.
Discov Oncol. 2024 Oct 22;15(1):579. doi: 10.1007/s12672-024-01461-8.
T cells play a crucial role as regulators of anti-tumor activity within the tumor microenvironment (TME) and are closely associated with the progression of osteosarcoma (OS). Nevertheless, the specific role of T cell-related genes (TCRGs) in the pathogenesis of OS remains unclear.
First, we processed single-cell RNA sequencing (scRNA-seq) data of OS from the public databases and performed cell annotation. We identified highly variable genes in each cell type using the "FindAllMarkers" function, explored the distribution of different clusters, and investigated inter-cellular communication patterns via the "CellChat" framework. Then, we used multivariate Cox analysis to construct a TCRG and developed a nomogram to predict survival probabilities for OS patients. Finally, we validated the aforementioned results using various cell lines and investigated the immune cell infiltration, expression of immune checkpoints, chemotherapy sensitivity, and the efficacy of targeted therapies across different risk groups.
From the scRNA-seq data, we identified 3,000 highly variable genes, presented the top 10 genes, and validated the expression of core genes across different cell lines.Moreover, our analysis delved into interactions between T cells and other cell types. Our analyses constructed a predictive T cell-related signature (TCRS) that incorporated these prognostic TCRGs, showing a clear prognostic separation between the high-risk and low-risk OS patient groups in multiple cohorts. Survival analysis indicated better outcomes for patients classified in the high-risk group. The low-risk group exhibited elevated levels of CD4 memory resting T cells, contrasting with the higher levels of macrophage M0 observed in the high-risk group via the CIBERSORT algorithm. Furthermore, we observed that the low-risk group exhibitedAQ1 significant up-regulation of immune checkpoint genes (ICGs) and lower Tumour Immune Dysfunction and Exclusion (TIDE) scores, suggesting that they may be suitable for immunotherapy. Conversely, the high-risk group appeared more responsive to chemotherapy and targeted therapies, according to our drug sensitivity analysis.
In conclusion, our study identified TCRGs, constructed and validated a TCRS for OS, and assessed immune response and drug sensitivity in different risk groups of OS patients. These findings provide novel insights into personalized treatment strategies for OS, potentially guiding more effective therapeutic interventions.
T细胞作为肿瘤微环境（TME）中抗肿瘤活性的调节因子发挥着关键作用，且与骨肉瘤（OS）的进展密切相关。然而，T细胞相关基因（TCRGs）在OS发病机制中的具体作用仍不清楚。
首先，我们处理了来自公共数据库的OS单细胞RNA测序（scRNA-seq）数据并进行细胞注释。我们使用“FindAllMarkers”函数在每种细胞类型中鉴定高度可变基因，探索不同簇的分布，并通过“CellChat”框架研究细胞间通讯模式。然后，我们使用多变量Cox分析构建一个TCRG并开发了一个列线图来预测OS患者的生存概率。最后，我们使用各种细胞系验证上述结果，并研究不同风险组的免疫细胞浸润、免疫检查点表达、化疗敏感性和靶向治疗效果。
从scRNA-seq数据中，我们鉴定出3000个高度可变基因，列出了前10个基因，并在不同细胞系中验证了核心基因的表达。此外，我们的分析深入研究了T细胞与其他细胞类型之间的相互作用。我们的分析构建了一个包含这些预后TCRGs的预测性T细胞相关特征（TCRS），在多个队列中显示出高危和低危OS患者组之间有明显的预后区分。生存分析表明高危组患者的预后更好。通过CIBERSORT算法观察到，低危组中CD4记忆静止T细胞水平升高，而高危组中巨噬细胞M0水平较高。此外，我们观察到低危组免疫检查点基因（ICGs）显著上调，肿瘤免疫功能障碍和排除（TIDE）评分较低，表明它们可能适合免疫治疗。相反，根据我们的药物敏感性分析，高危组似乎对化疗和靶向治疗更敏感。
总之，我们的研究鉴定了TCRGs，构建并验证了OS的TCRS，并评估了OS患者不同风险组的免疫反应和药物敏感性。这些发现为OS的个性化治疗策略提供了新的见解，可能指导更有效的治疗干预。
