Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.
J Egypt Natl Canc Inst. 2024 Oct 7;36(1):30. doi: 10.1186/s43046-024-00236-0.
Although advances in immune checkpoint inhibitor (ICI) research have provided a new treatment approach for lung adenocarcinoma (LUAD) patients, their survival is still unsatisfactory, and there are issues in the era of response prediction to immunotherapy.
Using bioinformatics methods, a prognostic signature was constructed, and its predictive ability was validated both in the internal and external datasets (GSE68465). We also explored the tumor-infiltrating immune cells, mutation profiles, and immunophenoscore (IPS) in the low-and high-risk groups.
As far as we are aware, this is the first study which introduces a novel prognostic signature model using BIRC5, CBLC, S100P, SHC3, ANOS1, VIPR1, LGR4, PGC, and IGKV4.1. According to multivariate analysis, the 9-immune-related genes (IRGs) signature provided an independent prognostic factor for the overall survival (OS). The low-risk group had better OS, and the tumor mutation burden (TMB) was significantly lower in this group. Moreover, the risk scores were negatively associated with the tumor-infiltrating immune cells, like CD8 T cells, macrophages, dendritic cells, and NK cells. In addition, the IPS were significantly higher in the low-risk group as they had higher gene expression of immune checkpoints, suggesting that ICIs could be a promising treatment option for low-risk LUAD patients.
The combination of these 9-IRGs not only could efficiently predict overall survival of LUAD patients but also show a powerful association with the expression of immune checkpoints and response to ICIs based on IPS; hoping this model paves the way for better stratification and management of patients in clinical practice.
尽管免疫检查点抑制剂（ICI）研究的进展为肺腺癌（LUAD）患者提供了新的治疗方法，但他们的生存仍然不尽如人意，而且在免疫治疗反应预测时代还存在一些问题。
使用生物信息学方法构建了一个预后特征，并在内部和外部数据集（GSE68465）中验证了其预测能力。我们还探索了低风险和高风险组中的肿瘤浸润免疫细胞、突变谱和免疫表型评分（IPS）。
据我们所知，这是第一项使用 BIRC5、CBLC、S100P、SHC3、ANOS1、VIPR1、LGR4、PGC 和 IGKV4.1 构建新的预后特征模型的研究。通过多变量分析，9 个免疫相关基因（IRG）特征为总生存期（OS）提供了一个独立的预后因素。低风险组的 OS 更好，该组的肿瘤突变负担（TMB）明显较低。此外，风险评分与肿瘤浸润免疫细胞呈负相关，如 CD8 T 细胞、巨噬细胞、树突状细胞和 NK 细胞。此外，低风险组的 IPS 明显更高，因为它们的免疫检查点基因表达更高，这表明 ICIs 可能是低风险 LUAD 患者的一种有前途的治疗选择。
这 9 个 IRG 的组合不仅可以有效地预测 LUAD 患者的总生存期，而且还可以根据 IPS 与免疫检查点的表达和对 ICIs 的反应显示出强大的相关性；希望该模型为更好地对患者进行分层和管理铺平道路，从而提高临床实践水平。
